DEV Community: Roman V

5 MCP Servers Every AI Developer Should Know About in 2026

Roman V — Sun, 26 Apr 2026 16:02:00 +0000

You've heard of MCP (Model Context Protocol). You've maybe even configured one server in Claude Code or Cursor. But the ecosystem has quietly grown from "file system access" to a full stack of production-ready tools that give your AI agents superpowers.

Here are 5 MCP servers worth adding to your toolkit — from financial data to customer intelligence to billing infrastructure.

1. Brave Search MCP — Web Search for Agents

What it does: Gives your AI agent access to Brave's search engine — web search and local search — without screen scraping or API key juggling.

Why it matters: Every agent eventually needs to look something up. Brave Search MCP is the cleanest way to give your agent web access without building a browser automation pipeline.

Quick start:

{
  "mcpServers": {
    "brave-search": {
      "command": "npx",
      "args": ["-y", "@anthropic-ai/mcp-server-brave-search"],
      "env": {
        "BRAVE_API_KEY": "your-key-here"
      }
    }
  }
}

Pricing: Free tier (1 query/sec, 2000/mo). Paid plans from $5/mo.

Get it: github.com/anthropics/mcp-servers

2. FinData MCP — Financial Data on Demand

What it does: 5 tools covering stock quotes, company fundamentals, economic indicators, SEC filings, and crypto prices. Your agent asks for AAPL's P/E ratio or the latest CPI number and gets structured data back instantly.

Why it matters: If you're building anything that touches markets — trading bots, research agents, financial dashboards — this server eliminates the need to wrangle Yahoo Finance scrapers or manage multiple API keys. The data fetching and aggregation happens server-side; your agent just calls tools.

Quick start:

pip install findata-mcp

{
  "mcpServers": {
    "findata": {
      "command": "findata-mcp",
      "args": ["--base-url", "https://findata-mcp-production-1cd3.up.railway.app"]
    }
  }
}

Then ask your agent:

\"Get me the current stock quote for NVDA and the latest GDP indicator\"

Pricing: $0.01 per tool call via x402 micropayments (pay-per-use, no subscription).

Get it: pypi.org/project/findata-mcp · GitHub

3. Feedback Synthesis MCP — Customer Intelligence Pipeline

What it does: 4 tools that collect customer feedback from GitHub Issues, Hacker News, and App Store reviews, then synthesize it into ranked pain clusters using a 3-stage LLM pipeline. Tools: synthesize_feedback, get_pain_points, search_feedback, get_sentiment_trends.

Why it matters: Every B2B SaaS team eventually builds a spreadsheet of customer complaints. This server automates the entire pipeline — collection, deduplication, clustering, and ranking — so your agent can answer \"what are our users' top 3 pain points this month?\" with real data instead of vibes.

Quick start:

{
  "mcpServers": {
    "feedback-synthesis": {
      "type": "streamableHttp",
      "url": "https://feedback-synthesis-mcp-production.up.railway.app/mcp/"
    }
  }
}

Then ask your agent:

\"Synthesize feedback for 'notion' from GitHub and Hacker News\"

Pricing: Per-call via x402: synthesize $0.05, pain points $0.02, search $0.01, trends $0.03.

Get it: feedback-synthesis-mcp on GitHub

4. MCP Billing Gateway — Monetize Any MCP Server

What it does: A reverse proxy that sits in front of your MCP server and handles billing — Stripe subscriptions, per-call credits, and x402 crypto micropayments. Your server stays unchanged. You register it, set pricing, and the gateway handles authentication, payment verification, usage metering, and revenue splits.

Why it matters: Building an MCP tool takes a weekend. Monetizing it takes weeks of billing plumbing. This gateway eliminates that entirely. Register your server, set a price per tool call, and start earning. It supports both traditional Stripe payments (for human users) and x402 micropayments (for agent-to-agent commerce).

Quick start:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/register \
  -H \"Content-Type: application/json\" \
  -d '{\"email\": \"you@example.com\", \"name\": \"Your Name\"}'

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers \
  -H \"Authorization: Bearer YOUR_OPERATOR_KEY\" \
  -H \"Content-Type: application/json\" \
  -d '{\"name\": \"My MCP Server\", \"upstream_url\": \"http://localhost:3000/mcp\", \"proxy_slug\": \"my-server\"}'

Set per-tool pricing:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/SERVER_ID/pricing \
  -H \"Authorization: Bearer YOUR_OPERATOR_KEY\" \
  -H \"Content-Type: application/json\" \
  -d '{\"tool_name\": \"my_tool\", \"credits_per_call\": 1, \"x402_price_usd\": \"0.01\"}'

Now any agent calling your server through the gateway pays per call. You get revenue reports, usage analytics, and Stripe payouts automatically.

Pricing: Free for operators processing <$100/mo. 2-3% above that.

Get it: github.com/sapph1re/mcp-billing-gateway-sdk

5. Playwright MCP — Browser Automation for Agents

What it does: Gives your AI agent full browser control — navigate pages, click elements, fill forms, take screenshots, and extract structured data from any website. Built on Microsoft's Playwright framework.

Why it matters: Some tasks require a real browser. Filling out web forms, scraping dynamic SPAs, testing web apps, taking visual snapshots. Playwright MCP bridges the gap between your agent's reasoning and the visual web.

Quick start:

{
  "mcpServers": {
    "playwright": {
      "command": "npx",
      "args": ["-y", "@anthropic-ai/mcp-server-playwright"]
    }
  }
}

Pricing: Free and open-source.

Get it: github.com/anthropics/mcp-servers

Putting It All Together

The MCP ecosystem is maturing fast. A year ago, MCP servers were mostly file system access and database queries. Today you can give your agent a full stack:

Need	Server	Cost
Web search	Brave Search	Free tier available
Financial data	FinData MCP	$0.01/call
Customer intelligence	Feedback Synthesis	$0.01-0.05/call
Billing infrastructure	MCP Billing Gateway	Free <$100/mo
Browser automation	Playwright	Free

The best part: these all use the same MCP protocol, so adding any of them to your agent is a config change — not a code change. Add the server definition to your MCP config, restart your client, and your agent has a new capability.

If you're building agents that need to interact with the real world — not just chat — MCP servers are the cleanest abstraction layer available. Start with one, add more as you need them.

What MCP servers are you using? Drop a comment — I'm always looking for new ones to try.

I Built a Tool That Turns App Store Reviews into Customer Personas in 2 Minutes

Roman V — Wed, 22 Apr 2026 06:10:34 +0000

The Problem: Customer Research Takes Too Long

If you're an indie founder or solo SaaS builder, you probably skip customer research.

Not because it's unimportant — you KNOW it's important. But because the process looks like this:

Read 200 app reviews across your competitor's listings
Manually categorize themes and patterns
Build persona profiles from what you found
Repeat for each competitor and target segment

That's a week of work. On a good week.

So most indie founders skip it and build on gut feelings instead.

What I Built

PersonaResearch automates this entire pipeline.

You paste in an App Store app ID (your competitor's app, or one in your target niche), and in about 2 minutes you get back 5 detailed customer personas synthesized from the real user reviews.

Each persona includes:

Name and demographic profile
Primary goals and motivations
Pain points extracted from actual reviews
Representative quotes from real users
Behavioral patterns and buying triggers

How It Works

The pipeline runs in three stages:

1. Review Collection — We pull the most recent reviews from the App Store (and optionally G2/Trustpilot). Currently 50-200 reviews per run.

2. Theme Extraction — Claude Haiku clusters reviews into thematic groups. "Battery drains too fast." "Love the export feature." "Pricing is confusing." Typically surfaces 6-12 distinct themes per app.

3. Persona Synthesis — Claude Sonnet takes the themes and writes full persona profiles, connecting the patterns into coherent user archetypes.

The whole thing runs async. Submit, come back in 2 minutes, download personas as Markdown.

Real Example

I ran it on a popular note-taking app with 200 reviews. Got back 5 personas:

The Overwhelmed Executive — wants capture speed over organization, frustrated by complex folder structures
The Student Researcher — needs reliable sync across devices, loves tagging, annoyed by premium paywalls
The Freelance Writer — values distraction-free writing mode, wants better export options
The Daily Journaler — emotional relationship with the app, upset by recent UI changes
The Knowledge Manager — power user who wants API access and plugin support

That took 90 seconds and cost $0 on the free tier.

Free Tier Available

Free: 3 research jobs per month — enough to validate a market segment or competitive niche.

Pro ($19/mo): 20 jobs — enough for ongoing competitive research.

Growth ($49/mo): Unlimited jobs + export + priority processing.

Try it at personaresearch.dev — sign up takes 10 seconds.

What's Next

Working on:

Google Play review support
Trustpilot + G2 sources (for B2B tools without App Store presence)
Export to Notion, Figma, and PDF
MCP server so AI agents can call this as a tool

Would love feedback from other founders using this — what data sources are most valuable to you?

Build a Paid MCP Server with x402 Micropayments: A Step-by-Step Tutorial

Roman V — Sun, 19 Apr 2026 11:20:36 +0000

You want to build an MCP server that AI agents pay to use — per call, in USDC, no subscriptions, no invoices. Just: agent calls your tool, payment happens, response comes back.

This tutorial walks through building a paid MCP server from scratch using x402 micropayments and mcp-billing-gateway for billing infrastructure. By the end, you'll have a working MCP server that charges $0.01 per tool call.

What Is x402?

x402 brings back HTTP's forgotten status code — 402 Payment Required. The flow is simple:

Agent sends request
    → Server returns 402 + payment details
    → Agent pays (USDC on Base network)
    → Agent retries with payment proof in header
    → Server verifies payment, returns data

No API keys for payment. No monthly invoices. The agent pays per call using USDC stablecoins on the Base L2 network. Transactions settle in seconds and cost fractions of a cent in gas.

This is ideal for agent-to-agent services where the "customer" is an AI agent with a crypto wallet, not a human with a credit card.

Architecture Overview

We'll build three components:

┌─────────────────┐
│   AI Agent      │
│  (Claude, etc.) │
└────────┬────────┘
         │ JSON-RPC + X-402-Payment header
         ▼
┌─────────────────────────────┐
│   mcp-billing-gateway       │
│                             │
│  • Validates x402 payment   │
│  • Tracks usage per caller  │
│  • Splits revenue           │
│  • Forwards to upstream     │
└────────────┬────────────────┘
             │ JSON-RPC (authenticated)
             ▼
┌─────────────────────────────┐
│   Your MCP Server           │
│                             │
│  • Implements tools         │
│  • Returns data             │
│  • Knows nothing about      │
│    billing                  │
└─────────────────────────────┘

The key insight: your MCP server never handles payments. The billing gateway sits in front as a reverse proxy. It verifies payment, deducts credits or validates x402 claims, then forwards the request. Your server just serves data.

Step 1: Build the MCP Server

Let's build a simple financial data MCP server with two tools. We'll use TypeScript and the official MCP SDK.

Project Setup

mkdir stock-mcp && cd stock-mcp
npm init -y
npm install @modelcontextprotocol/sdk express zod
npm install -D typescript @types/node @types/express
npx tsc --init

Update tsconfig.json:

{
  "compilerOptions": {
    "target": "ES2022",
    "module": "Node16",
    "moduleResolution": "Node16",
    "outDir": "./dist",
    "rootDir": "./src",
    "strict": true,
    "esModuleInterop": true
  }
}

Implement the Server

Create src/server.ts:

import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { z } from "zod";

export function createServer() {
  const server = new McpServer({
    name: "stock-mcp",
    version: "1.0.0",
  });

  server.tool(
    "get_stock_quote",
    "Get current stock price and daily change",
    { symbol: z.string().describe("Stock ticker symbol (e.g. AAPL)") },
    async ({ symbol }) => {
      const ticker = symbol.toUpperCase();

      // Replace with your real data source
      const res = await fetch(
        `https://query1.finance.yahoo.com/v8/finance/chart/${ticker}?range=1d`
      );
      const data = await res.json();
      const meta = data.chart.result[0].meta;

      return {
        content: [
          {
            type: "text",
            text: JSON.stringify({
              symbol: ticker,
              price: meta.regularMarketPrice,
              previousClose: meta.previousClose,
              change: (
                meta.regularMarketPrice - meta.previousClose
              ).toFixed(2),
              changePercent: (
                ((meta.regularMarketPrice - meta.previousClose) /
                  meta.previousClose) *
                100
              ).toFixed(2),
              currency: meta.currency,
              exchange: meta.exchangeName,
            }),
          },
        ],
      };
    }
  );

  server.tool(
    "get_market_summary",
    "Get major market indices overview",
    {},
    async () => {
      const indices = ["^GSPC", "^DJI", "^IXIC"];
      const results = await Promise.all(
        indices.map(async (idx) => {
          const res = await fetch(
            `https://query1.finance.yahoo.com/v8/finance/chart/${idx}?range=1d`
          );
          const data = await res.json();
          const meta = data.chart.result[0].meta;
          return {
            index: meta.shortName || idx,
            price: meta.regularMarketPrice,
            change: (
              meta.regularMarketPrice - meta.previousClose
            ).toFixed(2),
          };
        })
      );

      return {
        content: [
          { type: "text", text: JSON.stringify(results, null, 2) },
        ],
      };
    }
  );

  return server;
}

Add HTTP Transport

Create src/index.ts:

import express from "express";
import { createServer } from "./server.js";
import { StreamableHTTPServerTransport } from "@modelcontextprotocol/sdk/server/streamableHttp.js";

const app = express();
app.use(express.json());

const sessions = new Map<string, StreamableHTTPServerTransport>();

app.post("/mcp", async (req, res) => {
  const sessionId = req.headers["mcp-session-id"] as string | undefined;

  if (sessionId && sessions.has(sessionId)) {
    const transport = sessions.get(sessionId)!;
    await transport.handleRequest(req, res);
    return;
  }

  // New session
  const transport = new StreamableHTTPServerTransport({ sessionIdGenerator: () => crypto.randomUUID() });
  const server = createServer();
  await server.connect(transport);

  sessions.set(transport.sessionId!, transport);

  transport.onclose = () => {
    sessions.delete(transport.sessionId!);
  };

  await transport.handleRequest(req, res);
});

app.get("/health", (_, res) => res.json({ status: "ok" }));

const PORT = process.env.PORT || 3000;
app.listen(PORT, () => {
  console.log(`Stock MCP server running on port ${PORT}`);
});

Build and test:

npx tsc
node dist/index.js

Your MCP server is running. Agents can call get_stock_quote and get_market_summary — but right now, for free. Let's fix that.

Step 2: Register with the Billing Gateway

The mcp-billing-gateway handles payment verification, usage tracking, and revenue collection. You register your server, set pricing, and it proxies all requests.

Register as an Operator

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/register \
  -H "Content-Type: application/json" \
  -d '{
    "email": "you@example.com",
    "name": "Your Name"
  }'

You'll get back:

{
  "operator_id": "op_01jvz...",
  "api_key": "cgwop_abcd1234...",
  "stripe_onboard_url": "https://connect.stripe.com/setup/..."
}

Save your api_key — it's shown once. Complete the Stripe onboarding if you want fiat payouts (optional for x402-only).

Register Your Server

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Stock MCP",
    "description": "Real-time stock quotes and market data",
    "upstream_url": "https://your-deployed-server.com/mcp",
    "proxy_slug": "stock-data",
    "auth_mode": "none",
    "transport": "http",
    "is_public": true
  }'

Your server is now accessible via the gateway at /proxy/stock-data.

Step 3: Set x402 Pricing

Create a per-call pricing plan:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/SERVER_ID/plans \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Pay Per Call",
    "billing_model": "per_call",
    "price_per_call_usd_micro": 10000,
    "free_calls_per_month": 50,
    "is_default": true
  }'

This sets pricing at $0.01 per call (10,000 micro-USD) with 50 free calls per month. The free tier lets agents try your tools before committing.

The billing gateway accepts both Stripe credits and x402 payments. For x402, agents include a X-402-Payment header with their USDC payment proof — no API key needed.

Step 4: How Agents Pay

Here's what the payment flow looks like from the agent's perspective.

With API Key (Stripe Credits)

# Agent calls through the gateway
curl -X POST https://mcp-billing-gateway-production.up.railway.app/proxy/stock-data \
  -H "Authorization: Bearer cgwcl_agent_key..." \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "get_stock_quote",
      "arguments": { "symbol": "AAPL" }
    }
  }'

With x402 (USDC on Base)

For x402, the agent doesn't need an API key. The flow is:

Agent sends the request without payment
Gateway returns 402 with pricing info:

{
  "error": {
    "code": 402,
    "message": "Payment Required",
    "data": {
      "price_usd_micro": 10000,
      "x402_address": "0xYourWalletAddress",
      "chain": "base"
    }
  }
}

Agent sends USDC payment on Base and retries with proof:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/proxy/stock-data \
  -H "X-402-Payment: eyJ0eXBlIjoiZXhhY3QiLC..." \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
      "name": "get_stock_quote",
      "arguments": { "symbol": "AAPL" }
    }
  }'

The gateway validates the payment on-chain, records usage, and forwards the request to your server. Replay protection prevents the same payment from being used twice.

Step 5: Monitor Revenue

Check how your server is performing:

# Usage breakdown
curl https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/SERVER_ID/usage \
  -H "Authorization: Bearer cgwop_abcd1234..."

Response includes calls by day, by tool, and by billing rail (fiat vs. x402):

{
  "total_calls": 1247,
  "total_revenue_usd_cents": 1197,
  "by_billing_rail": {
    "x402": 891,
    "fiat_credit": 306,
    "free": 50
  },
  "by_tool": {
    "get_stock_quote": 823,
    "get_market_summary": 424
  }
}

Step 6: Configure Agent Access (Claude Code)

For agents using Claude Code, add this to their MCP config:

{
  "mcpServers": {
    "stock-data": {
      "type": "streamableHttp",
      "url": "https://mcp-billing-gateway-production.up.railway.app/proxy/stock-data",
      "headers": {
        "Authorization": "Bearer cgwcl_agent_api_key..."
      }
    }
  }
}

The agent can now call get_stock_quote and get_market_summary as regular MCP tools. Billing happens transparently — the agent doesn't even know it's being charged.

What You Get Without Building

By using the billing gateway instead of rolling your own payment stack, you skip building:

Payment verification — x402 proof validation + Stripe charge handling
Usage metering — per-call tracking with tool-level granularity
Credit system — prepaid balances with atomic debits and auto-refunds on errors
Subscription management — Stripe-managed recurring billing with period tracking
Revenue splitting — configurable operator/platform splits
API key management — SHA256-hashed keys with prefixes, rotation, and revocation
Replay protection — x402 payment hash dedup within 5-minute windows
Abuse prevention — IP logging, account suspension, rate tracking

That's 2–4 weeks of billing engineering you don't have to do. Your server stays pure — it just serves data.

When to Use x402 vs. Stripe

Scenario	Payment Method
Agent-to-agent services	x402 (agents have wallets)
Human developers	Stripe subscription
Mixed audience	Both — gateway supports dual-rail
Free/open-source tools	No billing needed

x402 shines for machine-to-machine payments where there's no human to enter a credit card. If your MCP server is consumed primarily by AI agents, x402 with per-call pricing is the natural fit.

Next Steps

Deploy your server to Railway, Fly.io, or any cloud provider
Register with the billing gateway at mcp-billing-gateway-production.up.railway.app
Set your pricing — start with a generous free tier to attract early adopters
Submit to MCP registries like Glama and the official MCP registry to get discovered

The billing gateway is open for operator registration. The SDK documentation has full API reference and examples.

This tutorial uses mcp-billing-gateway for billing infrastructure and the MCP TypeScript SDK for the server implementation.

Turn Scattered Customer Feedback Into Ranked Pain Clusters with MCP

Roman V — Fri, 17 Apr 2026 03:41:54 +0000

Your customers are telling you what's wrong with your product. The problem is they're telling you in 14 different places — GitHub Issues, Hacker News threads, App Store reviews — and nobody on your 5-person team has time to read 300 feedback items, let alone synthesize them into priorities.

You could hire a PM to triage feedback. You could pay $20-80/seat/month for Productboard. Or you could point an MCP tool at your feedback sources and get ranked pain clusters in seconds.

feedback-synthesis-mcp is an MCP server that collects customer feedback from multiple sources, runs a 3-stage LLM pipeline, and returns ranked pain clusters with evidence links and suggested actions.

What It Does

Four tools, one server:

synthesize_feedback — the full pipeline. Collects from multiple sources, extracts themes, clusters them, ranks by severity x frequency. Returns pain clusters with evidence URLs.
get_pain_points — quick single-source extraction. Faster and cheaper when you just need the top 5 issues from one repo.
search_feedback — search across cached feedback items. Drill into a specific topic after synthesis.
get_sentiment_trends — time-series sentiment analysis. Track how releases affect user perception.

The Pipeline

When you call synthesize_feedback, here's what happens server-side:

Feedback items (up to 200 per source)
  → Stage 1: Batch extraction (Haiku) — themes, sentiment, severity
  → Stage 2: Theme clustering (Haiku) — deduplicate, group similar
  → Stage 3: Pain synthesis (Sonnet) — rank, describe, link evidence
  → Ranked pain clusters with action items

Three sources in v1: GitHub Issues (REST API), Hacker News (Firebase API), and App Store reviews (RSS). Each collector handles auth, rate limiting, and data normalization independently.

Example: Synthesizing GitHub + HN Feedback

from feedback_synthesis_mcp import FeedbackSynthesisClient

client = FeedbackSynthesisClient(
    private_key="0xYOUR_WALLET_KEY",
    network="base-mainnet"
)

result = client.synthesize_feedback(
    sources=[
        {"type": "github_issues", "target": "owner/repo", "labels": ["bug"]},
        {"type": "hackernews", "target": "Show HN: MyProduct"}
    ],
    max_items_per_source=200,
    since="2026-01-01T00:00:00Z"
)

for cluster in result["pain_clusters"]:
    print(f"#{cluster['rank']} [{cluster['severity']}] {cluster['title']}")
    print(f"   Frequency: {cluster['frequency']} mentions")
    print(f"   Impact: {cluster['impact_score']:.2f}")
    for action in cluster["suggested_actions"]:
        print(f"   → {action}")

Sample output:

#1 [critical] Authentication flow breaks on mobile Safari
   Frequency: 23 mentions
   Impact: 0.92
   → Fix Safari WebAuthn polyfill (see issue #142)
   → Add fallback auth flow for mobile browsers

#2 [high] Rate limiting too aggressive for batch operations
   Frequency: 12 mentions
   Impact: 0.78
   → Increase default rate limit for authenticated users
   → Add batch endpoint for bulk operations

Each pain cluster includes direct URLs to the original feedback items — you can click through to the GitHub issue or HN comment that surfaced the problem.

Quick Pain Points (Cheaper, Faster)

Don't need the full pipeline? get_pain_points runs a single LLM pass on one source:

points = client.get_pain_points(
    source={"type": "github_issues", "target": "owner/repo"},
    max_items=100,
    top_n=5
)

for p in points["pain_points"]:
    print(f"{p['title']} — {p['frequency']} mentions")

Costs $0.02 per call instead of $0.05 for full synthesis.

Sentiment Over Time

Track how sentiment shifts across releases:

trends = client.get_sentiment_trends(
    sources=[
        {"type": "appstore", "target": "com.example.myapp"},
        {"type": "github_issues", "target": "owner/repo"}
    ],
    since="2025-10-01T00:00:00Z",
    granularity="weekly"
)

for week in trends["notable_shifts"]:
    print(f"{week['week']}: {week['direction']} shift ({week['magnitude']:+.2f})")
    print(f"  Likely cause: {week['likely_cause']}")

Using with Claude Code

Add the server to your MCP config:

{
  "mcpServers": {
    "feedback": {
      "command": "uvx",
      "args": ["feedback-synthesis-mcp"],
      "env": {
        "FEEDBACK_WALLET_PRIVATE_KEY": "0xYOUR_KEY",
        "FEEDBACK_NETWORK": "base-mainnet"
      }
    }
  }
}

Now you can ask Claude directly:

"What are the top pain points in our GitHub issues from the last quarter?"

Claude calls get_pain_points, gets structured results, and summarizes the findings with links to the relevant issues.

Using via Streamable HTTP (Direct MCP)

Connect directly to the hosted MCP endpoint without installing anything:

https://feedback-synthesis-mcp-production.up.railway.app/mcp

Supports MCP Streamable HTTP transport. Discovery is free — you only pay when calling tools.

Payment

Same model as findata-mcp: x402 micropayments on Base mainnet, paid automatically by the client.

Tool	Price
synthesize_feedback	$0.05
get_sentiment_trends	$0.03
get_pain_points	$0.02
search_feedback	$0.01

Invalid requests are rejected before payment. No API keys, no subscriptions.

Architecture

Same proven split as findata-mcp:

Thin client (public, PyPI): MCP tool definitions + x402 payment signing. No business logic.
Backend server (private, Railway): Collectors, 3-stage LLM pipeline, payment verification, caching.

The valuable part — the multi-source collection, theme extraction, and pain synthesis — stays server-side. The client is a typed proxy.

Who This Is For

Developers and technical product leads at small B2B SaaS companies (2-30 people) who:

Use Claude Code, Cursor, or AI coding tools with MCP
Need customer intelligence but can't justify Productboard or Enterpret pricing
Want programmatic access to synthesized feedback, not another dashboard
Want their AI agents to have context about what customers are saying

Getting Started

pip install feedback-synthesis-mcp

You need USDC on Base mainnet. Four tools, three data sources, structured pain clusters.

PyPI: feedback-synthesis-mcp
GitHub: sapph1re/feedback-synthesis-mcp
Glama: glama.ai/mcp/servers/sapph1re/feedback-synthesis-mcp
Live endpoint: feedback-synthesis-mcp-production.up.railway.app

Stop reading feedback manually. Let the pipeline read 300 items and tell you the five things that actually matter.

Real-Time Financial Data for AI Agents via MCP

Roman V — Fri, 17 Apr 2026 03:41:07 +0000

You're building an AI agent that needs stock quotes, crypto prices, or economic indicators. You could wire up Yahoo Finance, CoinGecko, and FRED APIs yourself — handle rate limits, parse inconsistent response formats, manage three sets of API keys, and hope nothing breaks when they change their schemas.

Or you could make one MCP tool call and get structured data back in seconds.

findata-mcp is a financial data MCP server that gives AI agents access to five data tools — stock quotes, company fundamentals, economic indicators, SEC filings, and crypto prices — through a single hosted endpoint with x402 micropayments.

Why MCP for Financial Data?

The Model Context Protocol is becoming the standard way AI agents discover and call external tools. Instead of hardcoding API integrations, your agent connects to an MCP server and gets a typed tool catalog it can reason about.

For financial data specifically, MCP solves three problems:

Data source abstraction — your agent doesn't need to know that stock quotes come from one API, economic data from another, and crypto from a third. It sees five tools with clear schemas.
Structured output — every response follows a consistent format. No more parsing HTML tables or guessing JSON shapes.
Payment built-in — x402 micropayments mean your agent can pay per call ($0.01) without API key management or monthly subscriptions.

The Five Tools

stock_quote

Real-time delayed equity quotes. Pass a ticker, get back price, change, volume, market cap.

from findata_mcp import FinDataClient

client = FinDataClient(
    private_key="0xYOUR_WALLET_KEY",
    network="base-mainnet"
)

quote = client.stock_quote("AAPL")
print(f"Apple: ${quote['price']} ({quote['change_percent']}%)")

company_fundamentals

Market cap, P/E ratio, EPS, dividend yield, sector classification — the numbers you need for valuation.

fundamentals = client.company_fundamentals("NVDA")
print(f"NVIDIA P/E: {fundamentals['pe_ratio']}")
print(f"Market cap: ${fundamentals['market_cap']:,.0f}")

economic_indicator

FRED-backed macroeconomic data. GDP, unemployment, CPI, federal funds rate — 400+ series.

gdp = client.economic_indicator("GDP")
print(f"US GDP: ${gdp['value']}B ({gdp['date']})")

crypto_price

Cryptocurrency pricing with 24h change, volume, and market cap.

btc = client.crypto_price("bitcoin")
print(f"BTC: ${btc['price']:,.2f} (24h: {btc['change_24h']}%)")

sec_filing

SEC EDGAR filing retrieval. 10-K, 10-Q, 8-K — search by company or CIK.

filings = client.sec_filing("TSLA", filing_type="10-K")
for f in filings['filings']:
    print(f"{f['type']} filed {f['date']}: {f['url']}")

How Payment Works

FinData uses x402 micropayments on Base mainnet. Each tool call costs $0.01 in USDC — paid automatically by the client library.

The flow:

Your agent calls a tool
The client signs a USDC payment claim with your wallet key
The server verifies the payment on-chain and returns data
Invalid requests (bad ticker, malformed params) are rejected before payment

No API keys. No monthly bills. No rate limit tiers. Just pay per call.

from findata_mcp import FinDataClient

client = FinDataClient(
    private_key="0xYOUR_WALLET_KEY",
    network="base-mainnet"
)

# Each call costs $0.01 USDC on Base
quote = client.stock_quote("MSFT")

You need USDC on Base chain in your wallet. If you're using a test wallet, fund it on Base bridge.

Using with Claude Code

If you're using Claude Code or any MCP-compatible client, add the server to your config:

{
  "mcpServers": {
    "findata": {
      "command": "uvx",
      "args": ["findata-mcp"],
      "env": {
        "FINDATA_WALLET_PRIVATE_KEY": "0xYOUR_KEY",
        "FINDATA_NETWORK": "base-mainnet"
      }
    }
  }
}

Now Claude can call financial data tools directly during conversations:

"What's Apple's current P/E ratio compared to the sector average?"

Claude calls company_fundamentals("AAPL"), gets structured data, and answers with real numbers.

Using via Streamable HTTP (Direct MCP)

You can also connect directly to the hosted MCP endpoint without installing anything:

https://findata-mcp-production-1cd3.up.railway.app/mcp

This endpoint supports the MCP Streamable HTTP transport. Discovery calls (initialize, tools/list) are free — you only pay when calling tools.

Architecture: Why a Hosted Server?

FinData is split into two components:

Thin client (public, PyPI): MCP tool definitions + x402 payment signing. Zero data-fetching logic.
Backend server (private, hosted on Railway): All the actual work — API calls, data normalization, payment verification, caching.

This matters because the valuable part (data aggregation, source management, error handling) stays server-side. The client is just a typed proxy that signs payments. You get the benefit of a managed service without vendor lock-in on the protocol level — any MCP client can talk to it.

Getting Started

pip install findata-mcp

You need a wallet with USDC on Base mainnet. Each call is $0.01. Five tools, structured responses, no API key management.

PyPI: findata-mcp
GitHub: sapph1re/findata-mcp
Glama: glama.ai/mcp/servers/findata-mcp
Live endpoint: findata-mcp-production-1cd3.up.railway.app

If you're building agents that need financial data, stop wiring up individual APIs. One MCP server, five tools, $0.01 per call.

How to Monetize Your MCP Server in 10 Minutes

Roman V — Wed, 15 Apr 2026 23:54:34 +0000

You built an MCP server. It works. Agents call your tools, get results, everybody's happy — except you're paying for compute and API calls out of pocket.

You want to charge for it. But now you're looking at building: payment verification, usage tracking, API key management, subscription tiers, credit balances, revenue splits, Stripe integration, webhook handling. That's 2–4 weeks of work that has nothing to do with the tool you actually built.

This is the billing problem every MCP server operator hits. You can build tools in a weekend, but monetizing them takes weeks. Most operators choose "not monetizing" and either burn money or shut down.

mcp-billing-gateway solves this. It's a reverse proxy that sits in front of your MCP server and handles all billing — Stripe subscriptions, per-call credits, and x402 crypto payments. Your server stays unchanged. You register it, set pricing, and start earning.

Architecture: How It Works

AI Agent (caller)
    │
    ├─ Authorization: Bearer cgwcl_...
    │
    ▼
┌──────────────────────────┐
│   mcp-billing-gateway    │
│                          │
│  1. Authenticate caller  │
│  2. Check credits/sub    │
│  3. Debit payment        │
│  4. Forward JSON-RPC     │
│  5. Record usage         │
│  6. Return response      │
└──────────┬───────────────┘
           │
           ▼
┌──────────────────────────┐
│  Your MCP Server         │
│  (unchanged, untouched)  │
└──────────────────────────┘

Your MCP server never sees billing logic. The gateway proxies JSON-RPC requests, handling authentication, payment verification, and usage metering transparently. If a caller doesn't have credits or a valid subscription, they get a 402 Payment Required before your server is even contacted.

Step 1: Register as an Operator

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/register \
  -H "Content-Type: application/json" \
  -d '{
    "email": "you@yourcompany.com",
    "name": "Your Company"
  }'

Response:

{
  "operator_id": "op_01jvz...",
  "api_key": "cgwop_abcd1234...",
  "stripe_onboard_url": "https://connect.stripe.com/setup/abc123..."
}

Save that API key — it's shown once. Visit the stripe_onboard_url to connect your Stripe account (business info + bank account for payouts). This uses Stripe Connect Express, so you get a managed account without handling PCI compliance yourself.

Step 2: Register Your MCP Server

Point the gateway at your upstream server:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Weather MCP",
    "description": "Real-time weather data for AI agents",
    "upstream_url": "https://your-mcp-server.com/mcp",
    "proxy_slug": "weather",
    "auth_mode": "header",
    "upstream_auth_token": "your-server-secret",
    "transport": "http",
    "is_public": true
  }'

Your server is now proxied at:

https://mcp-billing-gateway-production.up.railway.app/proxy/weather

The auth_mode field controls how the gateway authenticates to your upstream: header injects a Bearer token, query appends it as a query parameter, none sends requests unauthenticated. The upstream_auth_token is your server's own auth — callers never see it.

Step 3: Set Pricing

The gateway supports three billing models. You can have multiple plans per server.

Per-call (pay-as-you-go):

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/srv_01jvz.../plans \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Pay-as-you-go",
    "billing_model": "per_call",
    "price_per_call_usd_micro": 10000,
    "free_calls_per_month": 100,
    "is_default": true
  }'

That's $0.01 per call (prices are in micro-units: 10,000 micro = $0.01) with 100 free calls per month. The free tier resets monthly.

Monthly subscription:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/srv_01jvz.../plans \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Pro",
    "billing_model": "subscription",
    "subscription_price_usd_cents": 999,
    "subscription_interval": "monthly",
    "subscription_call_limit": 10000,
    "is_default": false
  }'

$9.99/month for up to 10,000 calls. Managed via Stripe, with automatic period tracking and cancellation handling.

Tiered pricing:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/srv_01jvz.../plans \
  -H "Authorization: Bearer cgwop_abcd1234..." \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Volume",
    "billing_model": "tiered",
    "tiers": [
      {"up_to": 1000, "price_usd_micro": 0},
      {"up_to": 10000, "price_usd_micro": 5000},
      {"up_to": null, "price_usd_micro": 10000}
    ],
    "is_default": false
  }'

First 1,000 calls free, next 9,000 at $0.005 each, everything above at $0.01.

Step 4: Callers Start Using Your Server

From the caller's perspective, the integration is straightforward. Using the TypeScript SDK:

import { GatewayCallerSDK } from '@mcp-billing-gateway/caller-sdk';

const client = new GatewayCallerSDK({
  baseUrl: 'https://mcp-billing-gateway-production.up.railway.app'
});

// Register (one-time)
const { api_key } = await client.register('bot@myapp.com', 'My Agent');
client.setApiKey(api_key);

// Add credits ($10.00)
await client.topupCredits(1000, 'pm_stripe_payment_method_id');

// Call your tool — billing happens automatically
const result = await client.callTool('weather', 'get_weather', {
  city: 'Berlin'
});
console.log(result);

Or with plain curl:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/proxy/weather \
  -H "Authorization: Bearer cgwcl_caller_key..." \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "req-1",
    "method": "tools/call",
    "params": {
      "name": "get_weather",
      "arguments": {"city": "Berlin"}
    }
  }'

The gateway authenticates the caller, checks their credit balance, debits the call cost, forwards the JSON-RPC request to your server, and returns the response. If the upstream returns an error, credits are automatically refunded.

x402 Crypto Payments

For agent-to-agent transactions where callers have crypto wallets, the gateway supports x402 payments — USDC on Base chain. No API key needed. The caller sends a payment claim header:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/proxy/weather \
  -H "X-402-Payment: <base64-encoded-payment-claim>" \
  -H "Content-Type: application/json" \
  -d '{
    "jsonrpc": "2.0",
    "id": "req-1",
    "method": "tools/call",
    "params": {"name": "get_weather", "arguments": {"city": "Berlin"}}
  }'

If a caller sends a request without valid payment, they get a structured 402 response with pricing info:

{
  "jsonrpc": "2.0",
  "id": "req-1",
  "error": {
    "code": 402,
    "message": "Payment Required",
    "data": {
      "price_usd_micro": 10000,
      "x402_address": "0x...",
      "chain": "base"
    }
  }
}

This means your single server can accept both fiat (Stripe) and crypto (USDC) payments simultaneously, depending on what the caller supports.

Revenue and Analytics

Track how your server is being used and how much you're earning:

# Usage analytics
curl "https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/servers/srv_01jvz.../usage?from=2026-04-01&to=2026-04-15" \
  -H "Authorization: Bearer cgwop_abcd1234..."

The response includes usage broken down by day, by tool name, by billing rail (fiat vs. x402), and top callers. You see exactly which tools are popular and which pricing model callers prefer.

# Revenue and payouts
curl https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/revenue \
  -H "Authorization: Bearer cgwop_abcd1234..."

The default revenue split is 85/15 — you keep 85% of each call's revenue, the platform takes 15%. When you're ready to collect:

curl -X POST https://mcp-billing-gateway-production.up.railway.app/api/v1/operator/payouts/request \
  -H "Authorization: Bearer cgwop_abcd1234..."

Funds transfer to your linked Stripe account within 1–2 business days. Minimum payout: $1.00.

Self-Hosting

The gateway is open for self-hosting if you want full control:

docker run -p 3000:3000 \
  -e DB_PATH=/data/billing.db \
  -e STRIPE_SECRET_KEY=sk_test_... \
  -e STRIPE_WEBHOOK_SECRET=whsec_... \
  -e PLATFORM_URL=https://your-domain.com \
  -v billing-data:/data \
  mcp-billing-gateway

SQLite for storage (no Postgres dependency), Stripe for payment processing, and the full operator + caller API available at your own domain. Set DEFAULT_TAKE_RATE_BPS to adjust the revenue split (default 1500 = 15%).

What You Don't Have to Build

Here's what the gateway handles so you don't have to:

Concern	Without Gateway	With Gateway
API key management	Build auth system	Built-in (hashed, scoped, revocable)
Payment processing	Integrate Stripe SDK	Pre-integrated via Stripe Connect
Usage metering	Build tracking system	Per-call metering with breakdown
Subscription management	Handle Stripe webhooks	Automatic period tracking + cancellation
Credit system	Build ledger + balance tracking	Prepaid credits with auto-refund on errors
x402 crypto payments	Implement payment verification	Built-in x402 validation + anti-replay
Revenue analytics	Build dashboard	Usage by day, tool, billing rail, caller
Payouts	Manual Stripe transfers	One API call, automatic splits

Getting Started

Register as an operator (30 seconds)
Complete Stripe onboarding (2 minutes)
Register your server with a proxy slug (1 minute)
Create a pricing plan (1 minute)
Share the proxy URL with your users

Your MCP server is now monetized. No code changes to your server, no new dependencies, no payment infrastructure to maintain.

Live gateway: mcp-billing-gateway-production.up.railway.app
SDK docs: sapph1re/mcp-billing-gateway-sdk
Glama listing: glama.ai/mcp/servers/sapph1re/mcp-billing-gateway-sdk

How to Stop AI Agent Cost Blowups Before They Happen

Roman V — Wed, 15 Apr 2026 23:54:14 +0000

You deploy a four-agent pipeline that should cost about $0.80 per run. By end of day it has burned through $47 on a single stuck researcher loop. Sound familiar?

If you're running AI agents in production, cost blowups are not a question of if but when. 57% of organizations already have agents in production, yet 90% of agent projects fail within 30 days — and runaway LLM costs are the number one pain point.

The core problem: agents make autonomous decisions about how many LLM calls to issue. A retry loop, an overly verbose chain-of-thought, or a stuck tool call can silently 10x your bill before you notice.

The Current State of Agent Cost Control

Most teams handle this with one of three approaches, all of which fall short:

Manual monitoring. You watch dashboards and kill processes when costs spike. This works until you're asleep, in a meeting, or running 20 agents in parallel.

Provider-level spending caps. OpenAI and Anthropic offer monthly limits, but they're account-wide. You can't set a $5 budget for a specific research pipeline while allowing your coding agent $50.

Gateway proxies (Helicone, Portkey). These require routing all traffic through an external service. They add latency, a point of failure, and vendor lock-in. And they still don't give you per-agent circuit breakers.

What's missing is a framework-native solution: something that hooks directly into CrewAI, AutoGen, or LangGraph at the process level, enforces hard limits before each LLM call, and trips a circuit breaker when things go wrong — without requiring any external infrastructure.

Introducing agent-cost-guardrails

agent-cost-guardrails is an open-source Python library that does exactly this. Pure Python, zero infrastructure, framework-native hooks.

pip install agent-cost-guardrails

Here's what it gives you:

Hard budget limits — raises BudgetExceededError when spend exceeds your cap
Per-call token limits — prevents any single LLM call from consuming too many tokens
Rate limiting — tokens-per-minute sliding window to control burst spend
Circuit breaker — trips after N consecutive violations, requires manual reset
Alert callbacks — fire at configurable thresholds (50%, 80%, 100%)
Cost breakdown — track spend by model and by agent
Bundled pricing — 30+ models from OpenAI, Anthropic, Google, Mistral, DeepSeek, and Meta

Quick Start: The Context Manager

The simplest way to use it:

from agent_cost_guardrails import BudgetGuard

with BudgetGuard(max_usd=5.00) as guard:
    guard.pre_call_check(estimated_tokens=2000)
    # ... your LLM call here ...
    cost = guard.post_call_record(
        model="gpt-4o",
        input_tokens=1500,
        output_tokens=800
    )
    print(f"Call cost: ${cost:.4f}")
    print(guard.cost_report())

pre_call_check() validates the budget, rate limit, and circuit breaker before the call happens. post_call_record() tracks the actual spend. If the budget is exceeded, BudgetExceededError stops execution immediately.

CrewAI Integration

CrewAI is the most popular multi-agent framework, and it has the worst cost visibility out of the box. The logging inside Tasks is broken, and there's no built-in token cap.

from crewai import Agent, Task, Crew
from agent_cost_guardrails.integrations import CrewAIGuardrails

def cost_alert(threshold, current, budget):
    print(f"WARNING: {threshold*100:.0f}% of ${budget:.2f} budget used")

guards = CrewAIGuardrails(
    max_usd=5.00,
    max_tokens_per_call=4096,
    on_alert=cost_alert
)
guards.install()

researcher = Agent(
    role="Market Researcher",
    goal="Find competitor pricing data",
    llm="gpt-4o"
)
task = Task(
    description="Research competitor pricing for SaaS analytics tools",
    agent=researcher
)
crew = Crew(agents=[researcher], tasks=[task])
crew.kickoff()

report = guards.cost_report()
print(f"Total: ${report['total_cost_usd']:.4f}")
print(f"By agent: {report['cost_by_agent']}")

guards.uninstall()

guards.install() registers @before_llm_call and @after_llm_call hooks globally. Every LLM call CrewAI makes — across all agents and tasks — gets checked and tracked automatically.

AutoGen / AG2 Integration

AutoGen's register_hook() system gives us a clean interception point:

from autogen import AssistantAgent, UserProxyAgent
from agent_cost_guardrails.integrations import AutoGenGuardrails

guards = AutoGenGuardrails(max_usd=10.00)

assistant = AssistantAgent("analyst", llm_config={"model": "gpt-4o"})
proxy = UserProxyAgent("user", human_input_mode="NEVER")

guards.wrap_agent(assistant)
guards.wrap_agent(proxy)

proxy.initiate_chat(assistant, message="Analyze Q1 revenue trends")

print(guards.cost_report())

The library uses AG2's safeguard_llm_inputs / safeguard_llm_outputs hooks with automatic fallback to legacy hook names for older AutoGen versions.

LangGraph / LangChain Integration

LangGraph uses LangChain's callback system, so the integration plugs in via BaseCallbackHandler:

from langgraph.graph import StateGraph
from agent_cost_guardrails.integrations import LangGraphGuardrails

guards = LangGraphGuardrails(max_usd=2.00)

graph = build_your_graph()  # your StateGraph
result = graph.invoke(
    initial_state,
    config={"callbacks": [guards.callback_handler]}
)

report = guards.cost_report()
print(f"Remaining budget: ${report['remaining_usd']:.2f}")

The callback handler intercepts on_llm_start, on_chat_model_start, and on_llm_end events. It extracts actual token usage from the response when available, and falls back to tiktoken estimation when not.

Circuit Breaker: Your Safety Net

The circuit breaker is what separates this from basic cost tracking. When an agent enters a failure loop — retrying the same failed tool call, generating invalid outputs, or hitting rate limits — the circuit breaker trips after N consecutive violations and stops all LLM calls until you explicitly reset it.

guard = BudgetGuard(
    max_usd=10.00,
    max_tokens_per_call=8192,
    circuit_breaker_max_violations=3
)

# After 3 consecutive per-call violations:
# CircuitBreakerTrippedError is raised on the next pre_call_check()
# All agents stop. No more silent cost accumulation.

This is the difference between a $5 mistake and a $500 one.

Custom Model Pricing

The library ships with pricing for 30+ models, but you can override or extend it:

from agent_cost_guardrails import set_custom_pricing

set_custom_pricing({
    "my-fine-tuned-gpt4": {
        "input_per_mtok": 6.00,
        "output_per_mtok": 18.00,
    }
})

Pricing is maintained per million tokens (input and output separately) and supports prefix matching — so gpt-4o-2024-05-13 automatically resolves to the gpt-4o price.

Before and After

Before agent-cost-guardrails:

4-agent pipeline expected cost: ~$0.80/run
Actual cost with stuck loops: $3–$12/run
Worst case (overnight): $47 in a single stuck session
Discovery: next morning, checking the billing dashboard

After:

Hard cap: $2.00/run, enforced at the framework level
Circuit breaker trips after 3 violations — no silent loops
Alert at 80% budget → you get notified before hitting the cap
Per-agent breakdown → identify which agent is the problem

Getting Started

pip install agent-cost-guardrails          # core
pip install agent-cost-guardrails[crewai]  # + CrewAI hooks
pip install agent-cost-guardrails[autogen] # + AutoGen hooks
pip install agent-cost-guardrails[langgraph] # + LangGraph callbacks
pip install agent-cost-guardrails[all]     # everything

The library is MIT-licensed. Source, docs, and examples on GitHub:

PyPI: agent-cost-guardrails
GitHub: sapph1re/agent-cost-guardrails

If you're running agents in production and haven't had a cost blowup yet, you will. The question is whether you'll catch it at $2 or at $200.