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Roman V
Roman V

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Turn Scattered Customer Feedback Into Ranked Pain Clusters with MCP

#mcp #ai #saas #python

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:

  1. synthesize_feedback — the full pipeline. Collects from multiple sources, extracts themes, clusters them, ranks by severity x frequency. Returns pain clusters with evidence URLs.
  2. get_pain_points — quick single-source extraction. Faster and cheaper when you just need the top 5 issues from one repo.
  3. search_feedback — search across cached feedback items. Drill into a specific topic after synthesis.
  4. 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
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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}")
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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
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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")
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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']}")
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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"
      }
    }
  }
}
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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
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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
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You need USDC on Base mainnet. Four tools, three data sources, structured pain clusters.

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

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