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Context Lens MCP Server

@cornelcroi

基本情報

カテゴリ

その他

ライセンス

MIT

トランスポート

stdio

作者

cornelcroi

投稿者

Corneliu

Context Lens MCP Server について

Semantic search knowledge base for MCP-enabled AI assistants. Index local files or GitHub repos, query with natural language. Built on LanceDB vector storage. Works with Claude Desktop, Cursor, and other MCP clients.

Context Lens MCP Server の設定

以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。

{
  "mcpServers": {
    "context-lens": {
      "command": "uvx",
      "args": [
        "context-lens"
      ]
    }
  }
}

Context Lens MCP Server のツール

ツール一覧はまだありません

この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。

上のボタンで一時的なサンドボックスでこのサーバーを実行し、ツール一覧を取得できます。

概要

What is Context Lens?

Context Lens is a self-contained MCP server with built-in serverless vector storage (LanceDB) that transforms any content—codebases, documentation, contracts, or text files—into a searchable knowledge base for AI assistants. It enables semantic understanding over keyword matching, allowing the AI to answer questions about contextually relevant information even when the exact terms are absent.

How to use Context Lens?

Add Context Lens to your MCP client configuration (Kiro IDE, Cursor, Claude Desktop, Continue.dev) using uvx context-lens. For programmatic use, invoke the server with StdioServerParameters(command="uvx", args=["context-lens"]) and interact via the Python SDK. After setup, add content by asking the AI (e.g., "Add all Python files from ./src") and query naturally (e.g., "How does the authentication system work?").

Key features of Context Lens

  • Semantic search that understands meaning, not just keywords
  • Zero setup: no installation, configuration, or API keys required
  • Serverless vector storage with LanceDB (file-based, no cloud)
  • 100% local and private: all data stays on your machine
  • Index local files or public GitHub repositories
  • Smart parsing: language-aware chunking for code and documents

Use cases of Context Lens

  • Query a codebase: ask how authentication works, find database models, or compare frameworks without manual browsing
  • Learn from open source: index a GitHub repository and ask in-depth technical questions about its internals
  • Analyze documents: scan contracts for termination clauses, payment terms, or specific obligations
  • Fetch files from URLs: instantly add a remote file (e.g., a README) and ask questions about its content

よくある質問

1

What makes Context Lens different from keyword search tools?

Context Lens uses semantic embeddings (384-dimensional vectors) to capture meaning. It finds "authentication"-related code even if the word never appears, unlike traditional grep or keyword search.

2

What are the runtime requirements and dependencies?

Python 3.11+ and the `uvx` runner. The embedding model (~90MB) downloads on first use. All processing is local with no external API calls.

3

Where does my data live?

Entirely on your machine—stored as a LanceDB file on disk. No data is sent to the cloud; the server is fully offline.

4

What are the known limitations?

Maximum file size per document is 10 MB (configurable via `MAX_FILE_SIZE_MB`). Only text-based file types are supported (25+ extensions). The system works via stdio transport; no HTTP or custom authentication.

5

Is Context Lens free?

Yes, it is completely free and open source (MIT license). No subscriptions, cloud services, or API keys are required.

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