ai-backend-performance-mcp
Analyzes MongoDB query patterns for performance anti-patterns such as N+1 queries and unbounded finds, and checks index coverage for filter/sort fields based on in-repo createIndex calls.
Analyzes PostgreSQL query patterns for performance anti-patterns such as N+1 queries and ineffective batching, providing evidence-backed findings for database optimization.
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@followed by the MCP server name and your instructions, e.g., "@ai-backend-performance-mcpanalyze my Node.js backend at /app for performance anti-patterns"
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Here is a step-by-step guide with screenshots.
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
EXPLAINanalysis
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 --> ClientSee docs/architecture.md for layer details.
Analyzers
Analyzer | Detects |
Database queries | N+1 patterns, unbounded finds/queries |
MongoDB indexes | Filter/sort fields without matching |
Async patterns |
|
Connection pooling | Client/pool creation in handlers or loops |
Dependencies | Unused deps, dev/prod misclassification, lockfile stats |
MCP Tools
Tool | Description |
| Full scan with grouped findings and summary |
| MongoDB/PostgreSQL query patterns |
| MongoDB index coverage heuristics |
| Async/await performance patterns |
| Connection lifecycle anti-patterns |
|
|
Tool reference: docs/tools.md
Installation
npm install ai-backend-performance-mcpOr run directly:
npx ai-backend-performance-mcpMCP 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
projectPathNo 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
potentialDynamic
require()/ runtime-generated queries are not fully trackedIndex analysis compares in-repo
createIndexcalls only (not Atlas/ops-managed indexes) and stays silent when the repo defines noneArray.find, batched$in/ANY(),_idlookups, and module-scope DB clients are not treated as issuesSequential awaits are flagged only when they do not consume prior bindings;
Promise.allis never reported as a findingDependency 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 buildSee docs/development.md.
Testing
npm testFixture 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.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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