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Contextual MCP Server

@ContextualAI

代码仓库

基本信息

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其他

传输方式

stdio

作者

ContextualAI

关于 Contextual MCP Server

暂无概览

Contextual 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": {
    "contextual-mcp-server": {
      "command": "python",
      "args": [
        "-m",
        "venv",
        ".venv"
      ]
    }
  }
}

Contextual 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。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Contextual MCP Server?

A Model Context Protocol (MCP) server that provides Retrieval-Augmented Generation (RAG) capabilities using Contextual AI. It bridges AI interfaces like Cursor IDE and Claude Desktop to a dedicated Contextual AI agent, enabling query processing, intelligent retrieval from a knowledge base, and context-aware responses with citations.

How to use Contextual MCP Server?

Install with Python 3.10 or higher, clone the repo, create a virtual environment, and run pip install -e .. Configure by setting your Contextual AI API key and agent ID in a .env file (or directly), then modify the MCP configuration file (.cursor/mcp.json or Claude Desktop config) with the path to uv and the server script. Invoke the query tool from any MCP client.

Key features of Contextual MCP Server

  • Query processing via a dedicated Contextual AI agent
  • Intelligent retrieval from a comprehensive knowledge base
  • Context-aware responses grounded in source documentation
  • Responses include citations and attributions
  • Maintains conversation context for follow-up questions
  • Flexible tooling; new tools can be added with @mcp.tool()

Use cases of Contextual MCP Server

  • Domain-specific research, e.g., financial data on large US firms
  • Technical documentation queries, e.g., semiconductor datasheets
  • Code retrieval and explanation within an IDE like Cursor
  • General knowledge-base Q&A with source attribution

常见问题

1

What prerequisites are needed?

Python 3.10 or higher, Cursor IDE and/or Claude Desktop, a Contextual AI API key, and an MCP-compatible environment.

2

How do I configure the API key and agent ID?

Set the `API_KEY` and `AGENT_ID` environment variables in a `.env` file or directly in your shell before starting the server.

3

Where should I place the MCP configuration file?

For Cursor, project-specific: `.cursor/mcp.json`; global: `~/.cursor/mcp.json`. For Claude Desktop, use the same format in its configuration directory.

4

Can I extend the server with my own tools?

Yes. Create functions decorated with `@mcp.tool()`, define parameters with Python type hints, and provide a clear docstring describing the tool's purpose.

5

What are the current limitations?

The server runs locally and may not work in remote development environments. Tool responses are subject to Contextual AI API limits and quotas. Only stdio transport mode is currently supported.

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