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.

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 & Answer

• answer_query_websearch: Answers query using the configured Vertex AI model + Google Search grounding.
• answer_query_direct: Answers query using the configured Vertex AI model's internal knowledge.
• answer_doc_query: Finds official documentation for a topic and answers a query based primarily on that documentation, supplemented by web search for coding issues, using the configured Vertex AI model.

(Note: Input/output details for each tool can be inferred from the ListToolsRequestSchema handler in src/index.ts or dynamically via MCP introspection if supported by the client.)

Prerequisites

• Node.js (v18+)
• Bun (npm install -g bun)
• Google Cloud Project with Billing enabled.
• Vertex AI API enabled in the GCP project.
• Google Cloud Authentication configured in your environment (Application Default Credentials via gcloud auth application-default login is recommended, or a Service Account Key).

Setup & Installation

1. Clone/Place Project: Ensure the project files are in your desired location.
2. Install Dependencies:

   bun install
   

3. Configure Environment:
   • Create a .env file in the project root (copy .env.example).
   • Set the required and optional environment variables as described in .env.example. Ensure GOOGLE_CLOUD_PROJECT is set.
4. Build the Server:

   bun run build
   

   This compiles the TypeScript code to build/index.js.

Running with Cline

1. Configure MCP Settings: Add/update the configuration in your Cline MCP settings file (e.g., .roo/mcp.json).

   {
     "mcpServers": {
       "vertex-ai-mcp-server": {
         "command": "node",
         "args": [
           "/full/path/to/your/vertex-ai-mcp-server/build/index.js" // Use absolute path or ensure it's relative to where Cline runs node
         ],
         "env": {
           // Required: Ensure these match your .env or are set here
           "GOOGLE_CLOUD_PROJECT": "YOUR_GCP_PROJECT_ID",
           "GOOGLE_CLOUD_LOCATION": "us-central1",
           // Required if not using ADC:
           // "GOOGLE_APPLICATION_CREDENTIALS": "/path/to/your/service-account-key.json",
           // Optional overrides:
           "VERTEX_AI_MODEL_ID": "gemini-2.5-pro-exp-03-25",
           "VERTEX_AI_TEMPERATURE": "0.0",
           "VERTEX_AI_USE_STREAMING": "true",
           "VERTEX_AI_MAX_OUTPUT_TOKENS": "65535",
           "VERTEX_AI_MAX_RETRIES": "3",
           "VERTEX_AI_RETRY_DELAY_MS": "1000"
         },
         "disabled": false,
         "alwaysAllow": [
            // Add tool names here if you don't want confirmation prompts
            // e.g., "answer_query_websearch"
         ],
         "timeout": 3600 // Optional: Timeout in seconds
       }
       // Add other servers here...
     }
   }
   

   • Important: Ensure the args path points correctly to the build/index.js file. Using an absolute path might be more reliable.
   • Ensure the environment variables in the env block are correctly set, either matching .env or explicitly defined here. Remove comments from the actual JSON file.

2. Restart/Reload Cline: Cline should detect the configuration change and start the server.

3. Use Tools: You can now use the extensive list of tools via Cline.

Development

• Watch Mode: bun run watch
• Linting: bun run lint
• Formatting: bun run format