MCP.so
登录

Context Lens MCP Server

@cornelcroi

基本信息

分类

其他

许可证

MIT

传输方式

stdio

作者

cornelcroi

提交者

Corneliu

关于 Context Lens MCP Server

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 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

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

Context Lens MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。

点击上方按钮,在临时沙箱中运行此服务器并实时获取工具列表。

概览

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

常见问题

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.

评论

其他 分类下的更多 MCP 服务器