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ag2-mcp-servers

Authorized Buyers Marketplace API MCP Server

MCP Server

This project is an MCP (Model Context Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/googleapis.com/authorizedbuyersmarketplace/v1/openapi.json, auto-generated using AG2's MCP builder.

Prerequisites

  • Python 3.9+

  • pip and uv

Related MCP server: Real-Time Bidding MCP Server

Installation

  1. Clone the repository:

    git clone <repository-url>
    cd mcp-server
  2. Install 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 check

To format the code:

ruff format

These commands are also available via the scripts/lint.sh script.

Static Analysis

To run static analysis (mypy, bandit, semgrep):

./scripts/static-analysis.sh

This script is also configured as a pre-commit hook in .pre-commit-config.yaml.

Running Tests

To run tests with coverage:

./scripts/test.sh

This will run pytest and generate a coverage report. For a combined report and cleanup, you can use:

./scripts/test-cov.sh

Pre-commit Hooks

This project uses pre-commit hooks defined in .pre-commit-config.yaml. To install the hooks:

pre-commit install

The 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 stdio

The 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 build

To publish the project:

hatch publish

These commands are also available via the scripts/publish.sh script.

Related MCP Connectors

  • The Mercado Pago MCP Server implements the Model Context Protocol to provide AI agents and LLMs with access to Mercado Pago's APIs and tools within compatible development environments. It acts as an intermediary that translates Mercado Pago resources into executable functions (tools) that AI applications can invoke to perform actions and automate flows. The server simplifies integration, enables using documentation to implement or improve code, and optimizes operations through natural language interactions without manual implementations.

  • The BigQuery remote MCP server is a fully managed service that uses the Model Context Protocol to connect AI applications and LLMs to BigQuery data sources. It provides secure, standardized tools for AI agents to list datasets and tables, retrieve schemas, generate and execute SQL queries through natural language, and analyze data—enabling direct access to enterprise analytics data without requiring manual SQL coding.

  • MCP server for building and testing AI agents with multi-model experimentation and insights.

  • MCP-native ad server. Monetize AI chatbots and agents with conversational ads.

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