Vertex AI MCP Server

This project implements a Model Context Protocol (MCP) server that provides a comprehensive suite of tools for interacting with Google Cloud's Vertex AI Gemini models, focusing on coding assistance and general query answering.

Security boundary

This server includes filesystem write and terminal-execution tools in addition to model and research tools. Run it with a dedicated low-privilege identity, limit its filesystem and network access, and keep consequential tools behind explicit approval. Do not treat an MCP client confirmation prompt as server-side authorization.

For the implementation tradeoffs this project exposed alongside six other public MCP codebases, see What building seven MCP servers taught me about production MCP. The article uses this repository as public engineering evidence; it does not claim that every production control discussed there is already implemented here.

Features

• Provides access to Vertex AI Gemini models via numerous MCP tools.
• Supports web search grounding (answer_query_websearch) and direct knowledge answering (answer_query_direct).
• Configurable model ID, temperature, streaming behavior, max output tokens, and retry settings via environment variables.
• Uses streaming API by default for potentially better responsiveness.
• Includes basic retry logic for transient API errors.
• Minimal safety filters applied (BLOCK_NONE) to reduce potential blocking (use with caution).

Tools Provided

Query & Generation (AI Focused)

• answer_query_websearch: Answers a natural language query using the configured Vertex AI model enhanced with Google Search results.
• answer_query_direct: Answers a natural language query using only the internal knowledge of the configured Vertex AI model.
• explain_topic_with_docs: Provides a detailed explanation for a query about a specific software topic by synthesizing information primarily from official documentation found via web search.
• get_doc_snippets: Provides precise, authoritative code snippets or concise answers for technical queries by searching official documentation.
• generate_project_guidelines: Generates a structured project guidelines document (Markdown) based on a specified list of technologies (optionally with versions), using web search for best practices.