CodeHealth MCP

Codebase health analysis that works everywhere. Dead code, circular dependencies, coupling issues, and architectural drift — exposed as MCP tools for Claude Desktop, Cursor, Windsurf, and Slack.

The Problem

Dead code, circular dependencies, excessive coupling, and architectural drift are invisible in day-to-day work. Static analysis tools produce noise in CI dashboards nobody checks. CodeHealth MCP brings these insights into the tools developers actually use — via the Model Context Protocol.

What CodeHealth MCP Does

7 analysis tools, available in any MCP-compatible client:

Tool	What It Finds
`analyze_dead_code`	Unused functions, classes, modules with file:line + fix suggestions
`detect_circular_deps`	Module import cycles via DFS with impact assessment
`analyze_coupling`	Fan-out per module, tight cluster detection, refactoring suggestions
`detect_architectural_drift`	Layer boundary violations (UI→Data, Business→UI, etc.)
`full_health_scan`	All four analyses + 0–100 health score + prioritized action items
`explain_finding`	AI-powered detailed explanation of any finding
`check_mcp_health`	Remote MCP handshake (`initialize` + `tools/list`), schema drift, secret scan — HTTP 200 is not healthy

Where It Works

Client	How to Add
Claude Desktop	Add to `claude_desktop_config.json`
Cursor / Windsurf	Add to MCP settings
Slack	Built-in Agent Builder integration with Block Kit UI
Any MCP client	Standard MCP server (stdio) or remote Streamable HTTP

Claude Desktop Config (stdio)

config.json

{
  "mcpServers": {
    "codehealth": {
      "command": "node",
      "args": ["/path/to/codehealth-mcp/mcp-server/index.js"]
    }
  }
}

Remote Streamable HTTP (Glama / hosted)

Public HTTPS + streamable-http is required to list CodeSentinel as a Glama remote connector. Replace the host from your deploy env — do not commit a fake hostname.

server.ts

export MCP_BEARER_TOKEN="replace-with-a-long-random-secret"
npm run mcp:http

Local default: http://127.0.0.1:8787/mcp (health: GET /health). Production must be HTTPS.

config.json

{
  "mcpServers": {
    "codesentinel": {
      "type": "streamable-http",
      "url": "https://${MCP_HTTP_HOST}/mcp",
      "headers": {
        "Authorization": "Bearer ${MCP_BEARER_TOKEN}"
      }
    }
  }
}

Cursor / Claude remote connectors use the same url + Authorization header. Unauthenticated /mcp returns HTTP 401. LLM_API_KEY and other provider keys stay on the server and are never echoed.

Deploy on Vercel (public HTTPS)

Stateless Streamable HTTP (JSON request/response) runs on Vercel Fluid Compute. No sticky sessions. Do not invent a hostname — use the URL Vercel assigns.

Terminal

npx vercel          # preview
npx vercel env add MCP_BEARER_TOKEN     # required for /mcp — fail-closed Bearer auth
npx vercel env add LLM_API_KEY          # optional, server-side only
npx vercel env add DAYTONA_API_KEY      # optional, isolated GitHub scans
npx vercel env add GITHUB_TOKEN         # optional, private repo fetch
npx vercel --prod
# GET /health must be 200 even if MCP_BEARER_TOKEN is not set yet.

After deploy, the MCP endpoint is:

https://$VERCEL_PROJECT_PRODUCTION_URL/mcp

(VERCEL_URL for a specific deployment). Health: https://$VERCEL_PROJECT_PRODUCTION_URL/health.

Turn off Vercel Deployment Protection on the production host, or Glama/clients cannot complete initialize.

Glama connector fields (fill after the Vercel URL exists)

Field	Value
Type	Connector (remote MCP)
Server URL	`https://$VERCEL_PROJECT_PRODUCTION_URL/mcp`
Transport	`streamable-http`
Auth	API Key / Bearer
Header	`Authorization`
Header value	`Bearer $MCP_BEARER_TOKEN` (same secret as the Vercel env)
Ownership claim	`https://$VERCEL_PROJECT_PRODUCTION_URL/.well-known/glama.json` (static `public/` file)

See docs/mcp-http.md for Vercel env vars, Fluid Compute notes, and Docker/Fly fallback.

Quick Start

bash

git clone https://github.com/Cubiczan/codesentinel.git
cd codesentinel
npm install
cp .env.sample .env
# Edit .env with your LLM API key (and MCP_BEARER_TOKEN for HTTP mode)
npm start

HTTP MCP (same tools, Bearer auth):

server.ts

export MCP_BEARER_TOKEN="replace-with-a-long-random-secret"
npm run mcp:http
npm run mcp:http:smoke

Use in Claude Desktop

Code

Run a full health scan on /path/to/my/repo

Code

Find circular dependencies in the frontend

Code

Check coupling metrics in src/services

Code

Check MCP health on https://example.com/mcp

Remote MCP protocol health (not HTTP uptime)

A remote MCP endpoint can return HTTP 200 while initialize, tools/list, or the SSE stream fails. CodeSentinel probes the protocol itself:

Synthetic Streamable HTTP / legacy SSE handshake (initialize + tools/list)
Canonical tool-schema hash and drift alarms
Discovery-latency metrics
Secret scanning of tool descriptions/schemas before they enter agent context

Terminal

npm test
npm run mcp:health -- https://example.com/mcp

Library: src/lib/mcp-health. Analyzer: lib/analyzers/mcp-health.js. Full write-up: docs/mcp-health.md.

Daytona sandbox scans (optional)

Set DAYTONA_API_KEY (and optionally GITHUB_TOKEN for private repos). MCP tools and Slack analysis will shallow-clone GitHub URLs in a Daytona VM and return live import-graph findings instead of demo data.

Code

full_health_scan repo_path=https://github.com/org/repo

Use in Slack

Add the Slack app manifest, enable Agent Builder, and @CodeHealth in any channel.

Architecture

Code

┌──────────────────────────────────────────┐
│          MCP CLIENT (any)                │
│  Claude Desktop, Cursor, Slack, etc.     │
└──────────────────┬───────────────────────┘
                   │ MCP Protocol (stdio or Streamable HTTP)
┌──────────────────▼───────────────────────┐
│         CODEHEALTH MCP SERVER            │
│                                          │
│  🔧 analyze_dead_code                    │
│  🔧 detect_circular_deps                 │
│  🔧 analyze_coupling                     │
│  🔧 detect_architectural_drift           │
│  🔧 full_health_scan                     │
│  🔧 explain_finding                      │
│  🔧 check_mcp_health                     │
│                                          │
│  ┌──────────────────────────────────┐    │
│  │       Analysis Engine            │    │
│  │  dead-code | circular-deps       │    │
│  │  coupling | drift | mcp-health   │    │
│  └──────────────────────────────────┘    │
│                                          │
│  ┌──────────────────────────────────┐    │
│  │       LLM Provider               │    │
│  │  Deepseek / OpenAI / Anthropic   │    │
│  └──────────────────────────────────┘    │
└──────────────────────────────────────────┘

Slack Integration

CodeHealth MCP ships with a full Slack Agent Builder app featuring:

Block Kit UI — Severity-coded findings, health scores, actionable suggestions
Thread-based conversations — Follow-up analysis in threads
Suggested prompts — One-click analysis triggers
MCP server — Same tools, available everywhere

Demo Sandbox (Devpost judges)

The live demo workspace is codehealthdemo.slack.com — the CodeSentinel agent (App ID A0BEHRDN5TQ) is installed and authorized there. Mention it in any channel:

Code

@CodeSentinel run a full health scan on https://github.com/icohangar-ops/codesentinel

Sandbox configuration:

Live agent response in the sandbox — a real @CodeSentinel mention in #general triggering a Daytona-sandboxed repo scan:

CodeSentinel responding in #general

App credentials & App ID	Agent capability enabled	Socket Mode enabled

Adding Custom Analyzers

Each analyzer follows a simple interface:

javascript

function analyze(repoInfo) {
  return {
    type: "your_analysis_type",
    findings: [
      {
        type: "finding_type",
        severity: "critical" | "warning" | "info",
        file: "path/to/file.ts",
        line: 42,
        name: "symbol_name",
        reason: "Why this is a problem",
        suggestion: "How to fix it",
      },
    ],
    stats: { /* summary metrics */ },
  };
}

Add a new analyzer in lib/analyzers/, register it in analysis-engine.js, and it's automatically available in Slack and via MCP.

Roadmap

Real AST analysis — ts-morph for TypeScript, tree-sitter for multi-language
GitHub App — Automatic analysis on PRs with inline comments
Historical trends — Track health score over time per repo
Custom architecture rules — Define layer boundaries via config
Team dashboards — Aggregate health in Slack Canvas

Project Structure

Code

codehealth-mcp/
├── app.js                    # Bolt app entry (Slack)
├── manifest.json             # Slack app manifest
├── lib/
│   ├── analysis-engine.js    # Analysis orchestrator + health score
│   ├── intent-parser.js      # NLP intent classification
│   ├── block-kit-builder.js  # Rich Slack UI
│   ├── llm-provider.js       # Multi-provider LLM
│   └── analyzers/            # dead-code, circular-deps, coupling, drift, mcp-health
├── src/lib/
│   ├── resilience/           # safeFetch / retry
│   └── mcp-health/           # handshake, schema hash, secret scan, CLI
├── mcp-server/
│   ├── index.js              # MCP stdio entry (unchanged tools)
│   ├── http.js               # Streamable HTTP (stateless, Bearer auth)
│   ├── create-server.js      # Shared tool registration
│   └── package.json
├── docs/mcp-http.md          # Remote / Glama / Fly / Railway notes
├── test/                     # handshake / HTTP transport / secret-scan tests
└── functions/                # Slack function definitions

Community & Registry

CodeHealth MCP is listed in the following directories:

awesome-mcp-servers – A curated list of MCP servers.
MCP Registry – Official registry for Model Context Protocol servers.
Glama – Remote connectors must be public HTTPS speaking streamable-http (see docs/mcp-http.md).

License

MIT. See LICENSE.