Content
# 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:**
```bash
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:**
```bash
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`).
```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`
