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ai-backend-performance-mcp

ai-backend-performance-mcp

Static analysis MCP server for Node.js backend performance issues. AI agents can inspect a project for database query anti-patterns, async bottlenecks, connection pooling mistakes, and dependency hygiene problems — without modifying your code.

Why this project?

Backend performance issues often hide in plain sight: N+1 queries in loops, clients created per request, sequential awaits that could run in parallel, or dependencies misclassified in package.json. This MCP server exposes those patterns as structured, evidence-backed findings that AI coding assistants can reason about.

What it does

  • Read-only static analysis of JavaScript/TypeScript source files

  • Six focused MCP tools for common backend performance categories

  • Structured findings with severity, confidence, code snippets, and recommendations

  • Distinguishes confirmed evidence from potential issues

What it does not do

  • Execute your application or repository code

  • Modify files, install packages, or change indexes

  • Replace profiling, load testing, or database EXPLAIN analysis

Related MCP server: DepHealth MCP

Architecture

flowchart TD
  Client[MCP Client / AI Agent]
  Server[MCP Server]
  Tools[MCP Tools]
  Engine[Analysis Engine]
  Analyzers[Individual Analyzers]
  Findings[Structured Findings]

  Client --> Server
  Server --> Tools
  Tools --> Engine
  Engine --> Analyzers
  Analyzers --> Findings
  Findings --> Tools
  Tools --> Server
  Server --> Client

See docs/architecture.md for layer details.

Analyzers

Analyzer

Detects

Database queries

N+1 patterns, unbounded finds/queries

MongoDB indexes

Filter/sort fields without matching createIndex

Async patterns

await in loops, sequential awaits, blocking sync ops

Connection pooling

Client/pool creation in handlers or loops

Dependencies

Unused deps, dev/prod misclassification, lockfile stats

MCP Tools

Tool

Description

analyze_project

Full scan with grouped findings and summary

analyze_database_queries

MongoDB/PostgreSQL query patterns

analyze_indexes

MongoDB index coverage heuristics

analyze_async_patterns

Async/await performance patterns

analyze_connection_pooling

Connection lifecycle anti-patterns

analyze_dependencies

package.json / lockfile hygiene

Tool reference: docs/tools.md

Installation

npm install ai-backend-performance-mcp

Or run directly:

npx ai-backend-performance-mcp

MCP configuration

Add to your MCP client config (example for Cursor / Claude Desktop):

{
  "mcpServers": {
    "backend-performance": {
      "command": "npx",
      "args": ["-y", "ai-backend-performance-mcp"],
      "env": {}
    }
  }
}

For local development:

{
  "mcpServers": {
    "backend-performance": {
      "command": "node",
      "args": ["/absolute/path/to/ai-backend-performance-mcp/dist/index.js"]
    }
  }
}

Usage

Invoke any tool with a projectPath pointing to a Node.js backend repository:

{
  "projectPath": "/path/to/your/api"
}

Example output (truncated)

{
  "projectPath": "/app/examples/sample-node-api",
  "technologies": ["express", "mongodb"],
  "metadata": {
    "packageName": "sample-node-api",
    "packageVersion": "1.0.0",
    "sourceFileCount": 4
  },
  "findings": [
    {
      "category": "pooling",
      "severity": "critical",
      "title": "Connection or client created in request handler",
      "evidence": {
        "kind": "confirmed",
        "snippet": "const client = await MongoClient.connect(...)"
      },
      "confidence": 0.9,
      "recommendation": "Create a shared client/pool at module scope and reuse it."
    }
  ],
  "summary": {
    "totalFindings": 6,
    "confirmedCount": 3,
    "potentialCount": 3
  }
}

Try the included demo project at examples/sample-node-api.

Safety

  • Read-only: never writes to analyzed projects

  • Path validation: prevents traversal outside projectPath

  • No code execution: parses source text only; does not run repository code

  • Untrusted input: treat analyzed repos as untrusted

Limitations

  • Static analysis only; findings that depend on cluster state stay potential

  • Dynamic require() / runtime-generated queries are not fully tracked

  • Index analysis compares in-repo createIndex calls only (not Atlas/ops-managed indexes) and stays silent when the repo defines none

  • Array.find, batched $in/ANY(), _id lookups, and module-scope DB clients are not treated as issues

  • Sequential awaits are flagged only when they do not consume prior bindings; Promise.all is never reported as a finding

  • Dependency unused detection is import-scan based

  • Redis-specific rules are planned but not implemented in v0.1.0

Development

git clone https://github.com/robinafaruqia/ai-backend-performance-mcp.git
cd ai-backend-performance-mcp
npm install
npm run typecheck
npm run lint
npm test
npm run build

See docs/development.md.

Testing

npm test

Fixture projects under tests/fixtures/ pair problematic and valid code for N+1 queries, indexes, async, pooling, and dependencies so analyzers do not fire on every await, query, loop, or connection.

Roadmap

  • Redis/cache analyzer

  • Prisma/TypeORM-specific query rules

  • ProjectContext caching

  • SARIF/JSON report export

  • Configurable severity thresholds

Contributing

Contributions are welcome! See CONTRIBUTING.md and CODE_OF_CONDUCT.md.

License

MIT — see LICENSE.

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