MCP Server
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Serversearch for recent developments in quantum computing"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Server
This project is an MCP (Multi-Agent Conversation Protocol) Server for the given OpenAPI URL - https://raw.githubusercontent.com/exa-labs/openapi-spec/refs/heads/master/exa-openapi-spec.yaml, auto-generated using AG2's MCP builder.
Prerequisites
Python 3.9+
pip and uv
Related MCP server: Exa MCP Server
Installation
Clone the repository:
git clone <repository-url> cd mcp-serverInstall dependencies: The .devcontainer/setup.sh script handles installing dependencies using
pip install -e ".[dev]". If you are not using the dev container, you can run this command manually.pip install -e ".[dev]"Alternatively, you can use
uv:uv pip install --editable ".[dev]"
Development
This project uses ruff for linting and formatting, mypy for static type checking, and pytest for testing.
Linting and Formatting
To check for linting issues:
ruff checkTo format the code:
ruff formatThese commands are also available via the scripts/lint.sh script.
Static Analysis
To run static analysis (mypy, bandit, semgrep):
./scripts/static-analysis.shThis script is also configured as a pre-commit hook in .pre-commit-config.yaml.
Running Tests
To run tests with coverage:
./scripts/test.shThis will run pytest and generate a coverage report. For a combined report and cleanup, you can use:
./scripts/test-cov.shPre-commit Hooks
This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:
pre-commit installThe hooks will run automatically before each commit.
Running the Server
The MCP server can be started using the mcp_server/main.py script. It supports different transport modes (e.g., stdio, sse, streamable-http).
To start the server (e.g., in stdio mode):
python mcp_server/main.py stdioThe server can be configured using environment variables:
CONFIG_PATH: Path to a JSON configuration file (e.g., mcp_server/mcp_config.json).CONFIG: A JSON string containing the configuration.SECURITY: Environment variables for security parameters (e.g., API keys).
Refer to the if __name__ == "__main__": block in mcp_server/main.py for details on how these are loaded.
The tests/test_mcp_server.py file demonstrates how to start and interact with the server programmatically for testing.
Building and Publishing
This project uses Hatch for building and publishing. To build the project:
hatch buildTo publish the project:
hatch publishThese commands are also available via the scripts/publish.sh script.
This server cannot be deployed
Maintenance
Related MCP Connectors
Connect AI agents to Exa for web search, content fetching, and multi-step research.
Exa MCP — neural/semantic web search + content retrieval (exa.ai)
Capability registry for the agentic economy. Semantic search over verified MCP server listings.
Agent-native search engine with live web research optimized for AI agents.
Related MCP Servers
- AlicenseNot gradedqualityCmaintenanceAn MCP server that integrates real-time web search capabilities into AI assistants using the Exa API, providing both basic and advanced search functionality with formatted markdown results.143MIT
- FlicenseAqualityDmaintenanceA Model Context Protocol server that enables AI assistants like Claude to perform real-time web searches using the Exa AI Search API in a safe and controlled manner.222,768 npm-
- AlicenseAqualityCmaintenanceA server that enables AI assistants like Claude to perform web searches using the Exa AI Search API, providing real-time web information in a safe and controlled way.222,768 npmMIT
- FlicenseNot gradedqualityDmaintenanceA Multi-Agent Conversation Protocol Server that provides access to Google's Local Services API, enabling interaction with local service businesses information through natural language commands.-