MCP Server for Vertex AI Search

# MCP Server for Vertex AI Search This is a MCP server to search documents using Vertex AI. ## Architecture This solution uses Gemini with Vertex AI grounding to search documents using your private data. Grounding improves the quality of search results by grounding Gemini's responses in your data stored in Vertex AI Datastore. We can integrate one or multiple Vertex AI data stores to the MCP server. For more details on grounding, refer to [Vertex AI Grounding Documentation](https://cloud.google.com/vertex-ai/generative-ai/docs/multimodal/ground-with-your-data).  ## How to use There are two ways to use this MCP server. If you want to run this on Docker, the first approach would be good as Dockerfile is provided in the project. ### 1. Clone the repository ```shell # Clone the repository git clone git@github.com:ubie-oss/mcp-vertexai-search.git # Create a virtual environment uv venv # Install the dependencies uv sync --all-extras # Check the command uv run mcp-vertexai-search ``` ### Install the python package The package isn't published to PyPI yet, but we can install it from the repository. We need a config file derives from [config.yml.template](./config.yml.template) to run the MCP server, because the python package doesn't include the config template. Please refer to [Appendix A: Config file](#appendix-a-config-file) for the details of the config file. ```shell # Install the package pip install git+https://github.com/ubie-oss/mcp-vertexai-search.git # Check the command mcp-vertexai-search --help ``` ## Development ### Prerequisites - [uv](https://docs.astral.sh/uv/getting-started/installation/) - Vertex AI data store - Please look into [the official documentation about data stores](https://cloud.google.com/generative-ai-app-builder/docs/create-datastore-ingest) for more information ### Set up Local Environment ```shell # Optional: Install uv python -m pip install -r requirements.setup.txt # Create a virtual environment uv venv uv sync --all-extras ``` ### Run the MCP server This supports two transports for SSE (Server-Sent Events) and stdio (Standard Input Output). We can control the transport by setting the `--transport` flag. We can configure the MCP server with a YAML file. [config.yml.template](./config.yml.template) is a template for the config file. Please modify the config file to fit your needs. ```bash uv run mcp-vertexai-search serve \ --config config.yml \ --transport <stdio|sse> ``` ### Test the Vertex AI Search We can test the Vertex AI Search by using the `mcp-vertexai-search search` command without the MCP server. ```bash uv run mcp-vertexai-search search \ --config config.yml \ --query <your-query> ``` ## Appendix A: Config file [config.yml.template](./config.yml.template) is a template for the config file. - `server` - `server.name`: The name of the MCP server - `model` - `model.model_name`: The name of the Vertex AI model - `model.project_id`: The project ID of the Vertex AI model - `model.location`: The location of the model (e.g. us-central1) - `model.impersonate_service_account`: The service account to impersonate - `model.generate_content_config`: The configuration for the generate content API - `data_stores`: The list of Vertex AI data stores - `data_stores.project_id`: The project ID of the Vertex AI data store - `data_stores.location`: The location of the Vertex AI data store (e.g. us) - `data_stores.datastore_id`: The ID of the Vertex AI data store - `data_stores.tool_name`: The name of the tool - `data_stores.description`: The description of the Vertex AI data store