Vimeo MCP Server
Provides tools for interacting with Vimeo's API (v3.4), enabling access to Vimeo's video hosting and management capabilities through the OpenAPI specification at https://api.apis.guru/v2/specs/vimeo.com/3.4/openapi.json.
This server does not currently have a deployable release on Glama. Follow the repository's setup instructions to install and configure it for your MCP client, including any required credentials and local dependencies.
MCP Server
This project is an MCP (Model Context Protocol) Server for the given OpenAPI URL - https://api.apis.guru/v2/specs/vimeo.com/3.4/openapi.json, auto-generated using AG2's MCP builder.
Prerequisites
Python 3.9+
pip and uv
Related MCP server: Asana 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
MCP server for Clipkit — gives AI agents a video toolbox via the Clipkit schema.
MCP server for QPost — lets AI agents publish video and image posts to YouTube, TikTok, Instagram.
MCP server exposing the YourImageShare upload API (upload/list/delete) as tools for AI agents.
Hosted MCP server for AI agents to post to LinkedIn profiles and X and upload to YouTube.
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