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 & 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.

Filesystem Operations

• read_file_content: Reads the complete contents of a single file.
• read_multiple_files_content: Reads the contents of multiple files simultaneously.
• write_file_content: Creates a new file or completely overwrites an existing file with new content.
• edit_file_content: Makes line-based edits to a text file, returning a diff preview or applying changes.
• create_directory: Creates a new directory (including nested directories).
• list_directory_contents: Lists files and directories directly within a specified path (non-recursive).
• get_directory_tree: Gets a recursive tree view of files and directories as JSON.
• move_file_or_directory: Moves or renames files and directories.
• search_filesystem: Recursively searches for files/directories matching a name pattern, with optional exclusions.
• get_filesystem_info: Retrieves detailed metadata (size, dates, type, permissions) about a file or directory.

Combined AI + Filesystem Operations

• save_generate_project_guidelines: Generates project guidelines based on a tech stack and saves the result to a specified file path.
• save_doc_snippet: Finds code snippets from documentation and saves the result to a specified file path.
• save_topic_explanation: Generates a detailed explanation of a topic based on documentation and saves the result to a specified file path.
• save_answer_query_direct: Answers a query using only internal knowledge and saves the answer to a specified file path.
• save_answer_query_websearch: Answers a query using web search results and saves the answer to a specified file path.

(Note: Input/output schemas for each tool are defined in their respective files within src/tools/ and exposed via the MCP server.)

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