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An MCP server providing AI-powered search and querying of Vercel AI SDK documentation using the Google Gemini model, enabling contextualized responses.
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An MCP server providing AI-powered search and querying of Vercel AI SDK documentation using the Google Gemini model, enabling contextualized responses.
This server is relatively safe for querying documentation. The primary risks stem from reliance on the Google Gemini API and the potential for the AI agent to generate incorrect information. Ensure proper API key management and validate agent responses.
Performance depends on the speed of the Google Gemini API and the size of the documentation index. Indexing can be time-consuming.
Cost is primarily driven by Google Gemini API usage. Monitor API usage to avoid unexpected charges.
{
"mcpServers": {
"vercel-ai-docs": {
"command": "node",
"args": ["ABSOLUTE_PATH_TO_PROJECT/dist/main.js"],
"env": {
"GOOGLE_GENERATIVE_AI_API_KEY": "your-google-api-key-here"
}
}
}
}GOOGLE_GENERATIVE_AI_API_KEYagent-queryQueries the Vercel AI SDK documentation using an AI agent to synthesize information.
AI agent can generate inaccurate or misleading responses; relies on external API.
direct-queryPerforms a direct similarity search against the Vercel AI SDK documentation index.
Read-only access to documentation; no side effects.
clear-memoryClears the conversation memory for a specific session or all sessions.
Potentially impacts user experience; no direct security risk.
API Key
cloud
This server is relatively safe for querying documentation. The primary risks stem from reliance on the Google Gemini API and the potential for the AI agent to generate incorrect information. Ensure proper API key management and validate agent responses.
The agent primarily provides read-only access to documentation. Autonomy is limited by the capabilities of the AI agent and the available tools.
Production Tip
Implement robust error handling and monitoring to detect and address issues with the Google Gemini API.
Obtain a key from the Google AI Studio (makersuite.google.com/app/apikey).
Run `npm run build:index` to rebuild the index.
The server is designed to work with the Google Gemini model.
Use the `clear-memory` tool with the session ID or omit the ID to clear all sessions.
Yes, this server is compatible with any client that implements the Model Context Protocol.
The agent service will return errors. Implement retry logic and error handling in your client application.
Submit a pull request with your changes.