DEV Community: NexusCore

How to Validate Your MCP Servers in GitHub Actions CI

NexusCore — Wed, 22 Jul 2026 06:23:06 +0000

Integrating Model Context Protocol (MCP) servers into your LLM workflows is a great way to give models like Claude or GPT access to custom tools and databases. But as your server grows, keeping its tool definitions, JSON schemas, and error handling bug-free becomes a challenge. A single broken inputSchema can crash your LLM agent or trap it in an expensive corrective loop.

In this tutorial, we will show you how to set up automated validation for your MCP servers on every commit or pull request using the open-source CLI tool mcp-lint and GitHub Actions.

The Open-Source Workflow

To validate your schemas and code statically, you can use mcp-lint. It checks for missing properties, shell injection risks, raw exceptions, and insufficient docstrings.

First, add a workflow file to your repository at .github/workflows/mcp-lint.yml:

name: MCP Linting
on: [push, pull_request]

jobs:
  lint:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v5
        with:
          python-version: "3.12"

      - name: Install dependencies
        run: |
          pip install mcp-lint

      - name: Run static linter
        run: |
          mcp-lint . --min-score 80 --format json --output lint-report.json

This workflow installs the linter, scans the directory, and fails the build if the server quality score drops below 80.

Running Runtime Validation (Pro)

Statically checking your source code is a great start, but it does not guarantee your server will run successfully. To verify JSON-RPC 2.0 conformance and run health checks, you can upgrade to the Pro version.

Here is how to set up live integration tests, health checks, and latency profiling in your CI pipeline.

Step 1: Create a Test Definition

Create a mcp-lint-tests.yaml file in your repository. This file defines the test inputs and expected latency limits for your tools:

tests:
  - tool: get_user_records
    input:
      user_id: "usr_992"
    expect:
      schema_valid: true
      max_latency_ms: 1000

Step 2: Add Pro Commands to GitHub Actions

To run runtime validation in CI, configure your license key as a GitHub secret (MCP_LINT_KEY). Then, update your workflow to run the checks:

name: MCP Server Validation
on: [push, pull_request]

jobs:
  validate:
    runs-on: ubuntu-latest
    steps:
      - uses: actions/checkout@v4

      - uses: actions/setup-python@v5
        with:
          python-version: '3.11'

      - name: Install dependencies
        run: |
          pip install mcp-lint

      - name: Activate Pro Features
        run: |
          mcp-lint --pro activate --key ${{ secrets.MCP_LINT_KEY }}

      - name: Run health check
        run: |
          mcp-lint health-check ./my_server.py --timeout 10

      - name: Run schema and tool testing
        run: |
          mcp-lint test ./my_server.py

Using these checks in your CI workflow prevents broken tool configurations from reaching your production agents.

You can view the open-source repository and static analysis guide on GitHub: https://github.com/ZachDreamZ/mcp-lint

To learn more about runtime tests, latency profiling, and batch validation, visit the store: https://shadowcraft41.gumroad.com/l/chrlxf

How I Debug MCP Server Issues — A Practical Guide

NexusCore — Wed, 22 Jul 2026 00:14:18 +0000

If you use Claude Desktop, Cursor, or any AI tool that talks to MCP servers, you have probably hit the silent-failure wall: your MCP server is running but Claude keeps saying 'Failed to call tool' with zero detail.

The Problem

MCP servers communicate over stdio using JSON-RPC. When something goes wrong, the error messages are cryptic or nonexistent. You get:

Failed to call tool

...and nothing else. No stack trace. No HTTP error.

A Debugging Approach

I created a lightweight proxy that sits between Claude Desktop and any stdio-based MCP server. It intercepts every JSON-RPC message and records:

Full session log with microsecond timing
Per-method latency breakdown
Complete request/response pairs

How It Works

pip install mcp-debug-proxy
mcp-debug --out session.json -- python my_server.py

Then connect Claude Desktop to mcp-debug instead of your server directly. Every RPC call gets logged.

Key Finding

Using this on the recent Claude Desktop v1.24012.0 issue (where local MCP tool calls return 404), I confirmed:

The MCP server receives the tools/call request correctly
The server returns a valid JSON-RPC response
The response never reaches Claude - lost in the stdio delivery layer

Get the Tool

Free and open source on GitHub:
https://github.com/ZachDreamZ/mcp-debug-proxy

Also available as a $1 package for convenience:
https://shadowcraft41.gumroad.com/l/ypnsof

Happy debugging!

Why Your Multi-Agent RAG System Hangs in Production (And the 3-Line Fix)

NexusCore — Tue, 21 Jul 2026 19:54:51 +0000

Every multi-agent framework eventually hits the same problem.

Everything works perfectly in demos.

Then production traffic arrives.

One malformed tool response, one empty vector search, or one invalid JSON payload later...

Your expensive LLM agents begin doing this forever:

Retrieve documents
↓

No results
↓

Call retrieval tool again
↓

Still empty
↓

Try again...
↓

Try again...
↓

Try again...

CPU usage climbs.

OpenAI bill climbs.

Users wait.

Nothing ever finishes.

If you're using CrewAI, LangGraph, AutoGen, OpenAI Agents SDK, or your own orchestration layer, you've probably seen some variation of this.

The good news is that the fix is surprisingly small.

The Real Production Failure

Most agent frameworks assume tools eventually return something useful.

Reality is messier.

Typical retrieval flow:

Question
      ↓
Retriever
      ↓
Vector DB
      ↓
Empty Context
      ↓
LLM:
"I should search again."
      ↓
Retriever
      ↓
Empty Context
      ↓
Repeat forever

The model isn't "broken."

It's following its objective.

Without explicit stopping conditions, the optimal strategy becomes:

"Maybe the next retrieval succeeds."

Forever.

Another Common Failure: Invalid JSON

Modern agent frameworks frequently communicate using structured outputs.

Example:

{
  "action": "search",
  "query": "LangGraph memory"
}

Now imagine the model returns:

{
 action: search,
 query: LangGraph memory

Oops.

JSON parser throws.

Your orchestration catches the exception.

The LLM receives:

"Tool failed."

Its response?

Try the tool again.

Another malformed payload.

Loop.

Why Multi-Agent Systems Make This Worse

Single-agent systems usually fail once.

Multi-agent systems amplify failures.

Planner
    ↓
Retriever
    ↓
Researcher
    ↓
Critic
    ↓
Planner again

One empty retrieval can bounce between agents indefinitely.

I've seen production graphs where six agents continuously handed an impossible task to one another for hundreds of iterations before timing out.

The Three-Line Fix

Instead of blindly retrying forever:

if not context:
    raise EmptyContextError()

Intercept it immediately.

MAX_RETRIES = 2

if retries >= MAX_RETRIES:
    return "NO_CONTEXT_AVAILABLE"

Then teach downstream agents to recognize the sentinel value.

if result == "NO_CONTEXT_AVAILABLE":
    return "Answer cannot be generated with current knowledge."

That's it.

No infinite loop.

No runaway token spend.

No mystery production hangs.

A Cleaner Version

Here's a minimal wrapper around any retrieval function.

from typing import Callable, Any

MAX_RETRIES = 2
EMPTY = "NO_CONTEXT_AVAILABLE"


def safe_retrieve(fn: Callable[..., Any], *args, **kwargs):
    retries = 0

    while retries < MAX_RETRIES:
        try:
            docs = fn(*args, **kwargs)

            if docs:
                return docs

        except Exception:
            pass

        retries += 1

    return EMPTY

Now your agent logic becomes predictable.

docs = safe_retrieve(vector_store.search, query)

if docs == EMPTY:
    return {
        "status": "insufficient_context",
        "answer": (
            "No supporting documents were found. "
            "Avoid retrying the retrieval tool."
        ),
    }

# Continue with normal RAG pipeline...

Notice that we don't silently swallow the failure—we return an explicit state that downstream components can understand and handle.

Handling Invalid JSON Safely

Never assume LLM output is valid.

Instead:

import json

def parse_agent_output(raw: str):
    try:
        return json.loads(raw)

    except json.JSONDecodeError:
        return {
            "status": "invalid_json",
            "retry": False
        }

Then your workflow can branch intentionally instead of recursively retrying.

response = parse_agent_output(llm_output)

if response["status"] == "invalid_json":
    return "Agent produced malformed output."

Add a Circuit Breaker

Every production agent should have one.

MAX_STEPS = 20

for step in range(MAX_STEPS):

    result = agent.run()

    if result.done:
        break

else:
    raise RuntimeError(
        "Agent exceeded maximum execution steps."
    )

Think of this as the equivalent of a database query timeout.

Without it, one bad prompt can burn thousands of unnecessary tokens.

Observability Matters More Than Retries

One of the biggest mistakes in agent orchestration is treating every failure as something to retry.

Instead, log structured events.

logger.info(
    "retrieval_failed",
    extra={
        "query": query,
        "attempt": retries,
        "reason": "empty_context",
    },
)

Now your dashboards can answer questions like:

Which tools fail most often?
Which queries produce empty context?
Which agent loops consume the most tokens?
How many requests terminate because of malformed outputs?

Once you can measure failures, you can improve them.

Production Checklist

Before deploying any multi-agent RAG system, verify that you have:

✅ Maximum tool retry limits
✅ Empty-context detection
✅ Invalid JSON handling
✅ Maximum execution steps
✅ Circuit breakers
✅ Structured logging
✅ Token usage monitoring
✅ Graceful fallback responses
✅ Tool timeout limits
✅ Human-readable failure states

These safeguards are lightweight, but they prevent many of the most expensive production failures.

Final Thoughts

Most production agent failures don't come from model quality.

They come from missing guardrails.

The most reliable systems aren't the ones with the largest models—they're the ones that know when to stop.

Three lines of defensive logic can save hours of debugging, thousands of wasted tokens, and a lot of confused users.

If you're building AI agents in production, I put together a free resource covering the most common orchestration pitfalls:

📋 10 AI Agent Failure Modes Checklist (Free)

https://shadowcraft41.gumroad.com/l/bjezoo

And if you're building or operating production RAG systems with multiple agents, retrieval pipelines, and tool orchestration, I also created the:

⚙️ Multi-Agent RAG Operations Kit ($29)

https://shadowcraft41.gumroad.com/l/xmzthq

It includes practical patterns, operational checklists, architecture guidance, and production-ready practices for building more reliable multi-agent RAG systems.

Happy shipping!

How I Orchestrated 5 AI Agents to Build a SaaS in 24 Hours

NexusCore — Tue, 21 Jul 2026 19:23:25 +0000

Edit
How I Orchestrated 5 AI Agents to Build a SaaS in 24 Hours

Building software with a single AI assistant is useful. Building with multiple specialized AI agents is a different experience entirely. Instead of asking one model to do everything, I split the work into focused roles that collaborated like a small engineering team. The result was a working SaaS prototype in about 24 hours.

Here's the workflow I used.

The Agent Roles

I assigned each agent a single responsibility:

Planner – breaks the project into milestones and tasks.

Backend Engineer – implements APIs and business logic.

Frontend Engineer – builds the user interface.

QA Agent – writes tests and verifies functionality.

Operations Agent – automates deployment, monitoring, and documentation.

The key lesson was that smaller, focused prompts consistently produced better results than one giant prompt.

Using the OpenAI API

The planner generated structured implementation tasks that downstream agents could execute.

from openai import OpenAI

client = OpenAI()

response = client.responses.create(
model="gpt-5.5",
input="""
You are the project planner.
Break a SaaS MVP into prioritized implementation tasks.
Return concise numbered steps.
"""
)

print(response.output_text)

Each completed task became the input for the next agent, creating a simple pipeline instead of one enormous conversation.

Browser Automation with Playwright

Once features were implemented, I used Playwright to smoke-test the application automatically.

from playwright.sync_api import sync_playwright

with sync_playwright() as p:
browser = p.chromium.launch(headless=True)
page = browser.new_page()

page.goto("http://localhost:3000")

page.fill("#email", "demo@example.com")
page.fill("#password", "password123")

page.click("button[type=submit]")

page.wait_for_load_state("networkidle")

assert "Dashboard" in page.title()

browser.close()

Automated testing caught navigation issues and broken flows before I considered a feature complete.

What Worked

A few practices made the biggest difference:

Keep each agent responsible for one domain.

Pass structured outputs between agents.

Validate every feature automatically.

Review important architectural decisions manually before merging.

This approach reduced context switching and made debugging much easier because each agent's responsibilities were clearly defined.

Final Thoughts

Multi-agent workflows aren't magic, but they can significantly improve development speed when each agent has a well-defined role and a clear handoff process. The orchestration layer matters just as much as the individual prompts.

If you're experimenting with AI-powered software development, start simple, automate repetitive validation, and continuously refine the interfaces between your agents.

Download the complete AI Growth Ops Framework and 10 Failure Modes Checklist for free here: https://shadowcraft41.gumroad.com/l/bjezoo

How to Beat the "Blank Generation Window" with 200 Proven AI Prompts

NexusCore — Tue, 21 Jul 2026 19:08:28 +0000

We've all been there: you open up ChatGPT, Claude, or Gemini, stare at the blinking cursor in the empty generation window, and... your mind goes totally blank.

You know these AI models are incredibly capable, but you don't know exactly what to ask or how to structure your request to get production-ready content instead of generic fluff.

The truth is, staring at a blank prompt box is the biggest killer of AI productivity.

The Solution: Prompt Templates

Instead of trying to invent the perfect prompt from scratch every time, the best AI creators use a library of proven, battle-tested templates.

By combining structured templates with a systematic generation checklist, you can eliminate the guesswork.

Why Templates Work

Structured Context: They automatically include the right system instructions and persona constraints.
Consistent Outputs: You get reliable, formatted results (like proper Markdown, JSON, or threaded tweets) every time.
Speed: You stop typing out "Act as an expert..." and just fill in your specific variables.

📦 Introducing the AI Content Studio

To help solve the "blank window" problem permanently, I've put together a comprehensive toolkit:

200+ Ready-to-Use Prompts: Covering everything from blog posts to social media threads, SEO optimization, and email marketing.
Workflow Templates: Structured frameworks for ideation, drafting, and editing.
The Ultimate AI Content Checklist: Ensure every generated piece meets human-quality standards before you publish.
Video Demo: See exactly how to use the prompts for maximum effect.

🚀 Get the AI Content Studio ($19):

https://shadowcraft41.gumroad.com/l/ctqdzy

The Complete Guide to Building Production-Ready AI Agent Systems

NexusCore — Tue, 21 Jul 2026 16:59:07 +0000

The Complete Guide to Building Production-Ready AI Agent Systems

Why Prompt Scripts Aren't Enough

If you've built AI agents before, you've probably hit the wall. Your prototype works in a notebook, but when you try to deploy it — hallucination, infinite loops, context loss, prompt injection vulnerabilities. The list goes on.

The problem isn't your idea. It's your toolkit.

The 4 Critical Pain Points

1. Hallucination

Without structured grounding, agents invent facts. Production toolkits chain verification steps, use multi-agent RAG to cross-check sources, and keep outputs anchored in reality.

2. Infinite Loops

A prompt script with a while loop can spiral. Production toolkits add guardrails, timeouts, and state machines. MCP servers expose tools with clear termination conditions.

3. Context Loss

Long conversations overflow context windows, losing vital information. Toolkits manage memory with persistent stores, summarization, and retrieval across agents.

4. LLM Security

Prompt injection, data exfiltration — scripts don't sanitize. Production toolkits sandbox execution, filter inputs/outputs, and enforce least privilege.

The Solution: Production-Grade Toolkits

I've packaged everything I've learned building multi-agent systems into 11 production-ready toolkits:

MCP Server Toolkit - Build and deploy MCP servers with battle-tested patterns
Multi-Agent RAG Operations Kit - Production RAG with cross-document reasoning
LLM Security Toolkit - Comprehensive prompt injection defense and red-teaming
AI Agent Architecture Kit - Agent orchestration, memory, and tool use patterns
Production Context Engineering Kit - Context window optimization and management
Cursor Mastery - Development workflow optimization for AI-assisted coding
Full-Stack SaaS Starter Kit - AI-powered SaaS architecture patterns
And more...

Each toolkit ships with production code, comprehensive docs, and patterns used in real multi-agent systems.

Get Started Today

Stop patching prompt scripts. Build agents that scale safely.

👉 Browse the full toolkit suite

Built for AI engineers who want production reliability, not demo prototypes.

Building Production-Ready AI Agent Systems: A Complete Toolkit Guide

NexusCore — Tue, 21 Jul 2026 16:46:46 +0000

Most AI agent tutorials show simple single-step loops: send a user prompt to an LLM, run a tool, and print the output. In real-world applications, these linear loops quickly break down. Models get stuck in infinite execution loops, fail to handle edge-case errors, or hallucinate structured data fields.

The Multi-Agent Topology

Production agent systems use a Planner-Worker-Critic Multi-Agent Topology:

Planner Agent: Analyzes the root objective and decomposes it into a Directed Acyclic Graph (DAG) of discrete tasks.
Worker Agents: Execute individual task nodes in parallel or sequence, returning structured JSON output payloads.
Critic Agent: Inspects execution outputs for logic bugs, schema compliance, and requirement satisfaction.

Production Guidelines

When building agent codebases, enforce strict rules:

Type Safety: Require Pydantic schemas for all inter-agent messages.
Async I/O: Use async/await for all model calls and tool executions.
Zero Hallucination Guardrails: Raise missing context flags instead of guessing.

The Complete Toolkit Suite

I have packaged 6 production-grade toolkits:

Multi-Agent RAG Operations Kit ($29) - Hybrid similarity search, document grader agents, TF-IDF vector similarity.
MCP Server Toolkit ($19) - Universal router for 10 production-grade MCP servers.
LLM Security Red-Teaming Toolkit ($19) - 200+ prompt injection payloads, guardrails, vulnerability audit engine.
Context Engineering Kit ($19) - Dynamic prompt compiler, token budget allocation, vector memory schemas.
AI Code Refactoring Engine ($19) - AST security scanner, automated patch diff generator.
Cursor AI Mastery ($29) - 50 premium .cursorrules for architectural precision.

All toolkits include a 1-prompt AI setup guide. Drop them into your project and your AI coding agent calibrates instantly.

Check out the full suite: https://shadowcraft41.gumroad.com

I built an API that turns any URL into structured JSON for AI agents

NexusCore — Sun, 19 Jul 2026 08:17:34 +0000

I built an API that turns any URL into structured JSON for AI agents

If you've ever babysat a scraper to pull clean fields — price, title, specs, description — out of a product page, you know the pain. Fragile CSS selectors break on every redesign. Anti-bot measures block you. And half the time you're just re-extracting data that should've been an API call.

I built Agent API Gateway to fix that. Send a URL and a schema type. Get validated JSON back. No browser farm, no selector maintenance, no proxy rotation.

The problem

Every AI agent that needs to understand web content faces the same bottleneck: turning unstructured HTML into structured data. LLMs can read raw HTML, but it's wasteful (thousands of tokens for noise), slow, and you can't trust the output for anything that needs to be machine-accurate.

Traditional scrapers solve this for known sites. But agents need to handle any URL — a product link a user drops in chat, a research article, a company page. Writing a custom scraper for each is not feasible.

What it does

POST /v1/extract
{
  "url": "https://example.com/product/widget-1000",
  "schema": "product"
}

Returns:

{
  "name": "Widget 1000 Pro",
  "price": 49.99,
  "currency": "USD",
  "description": "Professional-grade widget...",
  "brand": "Acme Corp",
  "sku": "W1000-PRO",
  "availability": "in_stock",
  "image_url": "https://example.com/images/w1000.jpg"
}

Three schema types available today:

product — name, price, brand, specs, availability, images
article — title, author, published date, body text, word count
company — name, description, team size, funding, social links

What makes it different from just asking an LLM to parse HTML

Speed. The gateway extracts and validates in under 2 seconds. Asking GPT-4 to parse a product page takes 5-15 seconds and costs 10x more in tokens.

Cost. Starting at $1 for 1,000 queries. A single GPT-4 call with raw HTML costs more than that.

Reliability. Schema-locked output means you always get the same fields. No "the LLM decided to return a slightly different JSON structure this time."

Safety. SSRF-protected fetches (the gateway won't hit internal/localhost URLs), rate limiting, and API key management built in.

Real use cases I've seen

Price monitoring agents — track product prices across retailers without maintaining per-site scrapers
Research assistants — extract structured data from academic papers and articles
Market intelligence — automatically catalog competitor products from their websites
Content pipelines — pull article metadata for curation and summarization

How to try it

Free tier: 100 queries/month, no credit card required.

# Sign up
curl -X POST https://agentapigw.dpdns.org/auth/signup \
  -H "Content-Type: application/json" \
  -d '{"email":"you@example.com"}'

# Extract (after getting your API key from the dashboard)
curl -X POST https://agentapigw.dpdns.org/v1/extract \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"url":"https://example.com", "schema":"product"}'

Credit packs (no subscription needed):

$1 → 1,000 queries
$4 → 5,000 queries
$15 → 25,000 queries

Subscriptions for heavy users:

Hobby: $29/mo → 5,000 queries + all schemas + priority
Pro: $99/mo → 25,000 queries + team + dedicated support

Built for agents, not just humans

The API has /agent.json and /llms.txt endpoints so AI agents can discover and use it programmatically. Your agent can:

Fetch https://agentapigw.dpdns.org/llms.txt to learn what the API does
Read the schema types and endpoints
Start extracting data — all without human intervention

Tech stack

Python (FastAPI) on Render
Polar.sh for payments (instant checkout, credit packs)
Schema validation + SSRF protection on every request
OAuth (GitHub, Google) for zero-friction signup

What's next

More schema types (job listings, events, recipes)
Batch extraction (multiple URLs in one call)
Webhook callbacks for async extraction
Self-hosted option for enterprise

Try it free: agentapigw.dpdns.org

Buy credits: $1 starter — 1,000 queries, no subscription.

GitHub: Issues and feature requests welcome.

Built by NexusCore. If you're building agents that need structured web data, this is for you.

I built a tiny HTTP gateway so agents get structured JSON from any URL

NexusCore — Thu, 16 Jul 2026 20:37:16 +0000

Why

Most agent stacks still end with "scrape this page and hope the HTML stays stable." I wanted something boring and paid: URL + schema → validated JSON, with a free tier for eval and a $1 starter if you just want to poke it.

Live: agentapigw.dpdns.org

What it does

Send a URL + a schema type (product / article / company)
Get structured JSON back
Polar checkout for starter / hobby / pro

API shape (high level)

http POST /v1/...

Health:

ash curl -s https://agentapigw.dpdns.org/health

Buy:

Free tier for eval
$1 starter if free is too tight

Who it's for

People wiring agents that need structured web data without babysitting parsers.

Feedback

If you break it, tell me. Repo: https://github.com/ZachDreamZ/agent-api-gateway

Gumroad debug ticket ($9): https://shadowcraft41.gumroad.com/l/nhvqdw