Contextual MCP Server

Name: Contextual MCP Server
Author: ContextualAI

@ContextualAI

リポジトリ

Contextual MCP Server について

概要はまだありません

基本情報

カテゴリ

その他

ランタイム

python

トランスポート

stdio

公開者

ContextualAI

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

ツール

ツールは検出されませんでした

ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。

概要

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

FAQ from Contextual MCP Server

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.

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.

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.

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.

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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