contextl-mcp 0.1.2

Repository Intelligence Engine — MCP server for AI coding agents

contextl

Context-selection engine for AI coding assistants.
Finds the most relevant files in your codebase for a natural-language change request — no LLM, no embeddings, no vector database. Pure graph + text scoring.

"fix the upload error" → [FileUploader.tsx, lib/upload.ts, UploadSection.tsx]

Instead of feeding your entire repo to an AI, contextl reduces 5 000 files down to the 5 most relevant ones in milliseconds.

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Installation

pip install contextl

Python 3.9+ required. networkx and mcp are installed automatically as dependencies.

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Quick start — connect to your IDE

Paste this into your IDE's MCP config file:

{
  "mcpServers": {
    "contextl": {
      "command": "contextl"
    }
  }
}

Config file locations

IDE	Config file
Antigravity	~/.gemini/antigravity/mcp/ (MCP server directory)
Cursor	~/.cursor/mcp.json
Windsurf	~/.codeium/windsurf/mcp_config.json
Claude Code	~/.claude.json (or run claude mcp add)
VS Code	.vscode/mcp.json in your workspace root

Use it

Just talk to your IDE's AI normally:

You:  "fix the file upload error handler"
IDE:  calls query_repo → gets [FileUploader.tsx, lib/upload.ts, …]
IDE:  reads only those 5 files instead of the whole repo

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Tools exposed

query_repo(repo_path, query, top_n?)

Ranks the most relevant files for a change request.

Parameters:

• repo_path — absolute path to the repository root
• query — natural-language description of the change (e.g. "change the download button color")
• top_n — max results to return (default 5, max 20)

Returns:

{
  "query": "change the download button",
  "repo": "/path/to/repo",
  "total_files_scanned": 142,
  "results": [
    {
      "rank": 1,
      "path": "components/DownloadButton.tsx",
      "score": 0.9800,
      "confidence": "high",
      "matched_terms": ["button", "download"],
      "reasoning": "Filename strongly matches query terms; file contents heavily reference query terms."
    }
  ]
}

scan_repo(repo_path)

Lists all source files the engine can see in a repository.

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How it works

The engine runs entirely locally — no network calls, no AI APIs, no data leaves your machine.

Scoring uses four signals:

Signal	Weight	Description
Keyword match	0.5	Query terms in the file path / name
Content match	0.5	Query terms inside the file source
Neighbor bonus	+0.15	Files near high-scoring files in the import graph
PageRank	0.05	Tiebreaker: more connected files rank slightly higher

Supports Next.js / React / TypeScript repos (.ts, .tsx, .js, .jsx). Automatically detects @/ path aliases from tsconfig.json.

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Also available as an npm package

npx contextl

For IDE configs that prefer npx over a Python command.

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License

MIT