What runtime and dependencies does Vonage AI Code Assist require?
Python 3.13+ is required. Dependencies are managed via `uv` (from `uv install`) and include FastMCP, httpx, BeautifulSoup, and python-dotenv.
@micahman33
基本情報
MCP server to assist with AI code generation using Claude Desktop, Claude Code or any coding tool that supports MCP servers to ensure you're always working from the most current Vonage SDKs and APIs
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": {
"VonageAICodeAssist": {
"command": "python",
"args": [
"main.py"
]
}
}
}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。
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。
Vonage AI Code Assist is a Model Context Protocol (MCP) server that helps developers integrate Vonage API capabilities into their applications. It provides AI-assisted access to Vonage documentation, enabling quick searches of Vonage’s communication APIs. The server is built for developers using Claude or other MCP-compatible AI assistants.
Install automatically via Smithery (npx -y @smithery/cli install @micahman33/VonageAICodeAssist --client claude) or manually with Python 3.13+, set the SERPER_API_KEY environment variable, run uv install to install dependencies, then python main.py. Once running, use the Vonage-Assist tool with parameters query and library (currently only "vonage").
Vonage-Assist tool for MCP-compatible AI assistantsPython 3.13+ is required. Dependencies are managed via `uv` (from `uv install`) and include FastMCP, httpx, BeautifulSoup, and python-dotenv.
A `SERPER_API_KEY` from Google Serper is required for web searches. No other authentication is needed.
The server follows the MCP protocol, so it can be used with any AI assistant that supports the MCP standard. The README specifically mentions Claude.
Only the `"vonage"` library is supported at this time. The `library` parameter accepts only `"vonage"`.
The server searches within the Vonage developer documentation domain (`developer.vonage.com/en/documentation`) using the Google Serper API. It fetches and extracts text content from the result pages. No local data is stored.

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