Giphy

MCP Server generated by mcp.ag2.ai

python

0

GitHub Stars

python

Language

7 months ago

First Indexed

3 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "qingyun-ag2-giphy-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{ /* JSON config string */ }",
        "SECURITY": "YOUR_API_KEY",
        "CONFIG_PATH": "mcp_server/mcp_config.json"
      }
    }
  }
}

You run an MCP Server to enable programmatic, multi-agent conversations against a specified API. This server hosts an MCP endpoint locally or remotely and lets clients connect in standardized ways, enabling you to orchestrate multiple agents against the Giphy API workflow with consistent transport, configuration, and security options.

How to use

Start the server in stdio mode so you can run it alongside local scripts or tests. This mode runs as a local process that speaks via standard input and output, making it straightforward to integrate with your development tools and test harnesses.

Once the server is running, connect your MCP client using the standard MCP client interface you normally use. You’ll point your client at the local stdio endpoint and supply any required configuration via environment variables or a configuration file. The server exposes a structured configuration space that your client can reference to control routing, security, and behavior.

How to install

Prerequisites you need before installing:

• Python 3.9+

• pip and uv (optional alternative runner)

Install steps you should follow exactly as shown:

# 1. Clone the MCP server repository
git clone <repository-url>
cd mcp-server

# 2. Install development dependencies
pip install -e ".[dev]"

# If you prefer using uv to manage Python environments:
uv pip install --editable ".[dev]"

Additional setup and run

Run the server in stdio mode to interact locally. The command to start the server uses the Python interpreter and points to the main entry script for the MCP server.

python mcp_server/main.py stdio

You can control how the server loads its configuration using environment variables. The following are supported in this setup:

• CONFIG_PATH: Path to your JSON configuration file (for example, mcp_server/mcp_config.json) • CONFIG: JSON string containing the configuration • SECURITY: Environment variables for security parameters (for example, API keys)

Configuration notes

The server reads configuration settings at startup from the environment or a configuration file. If you supply a JSON string via CONFIG, the server will parse it and apply the settings accordingly. Security parameters should be provided through SECURITY so that your MCP clients can authenticate requests.

Troubleshooting and tips

Ensure Python 3.9+ is installed and accessible as python
Verify that CONFIG_PATH points to a valid JSON configuration if you are using a file-based config
Check that required security keys are provided when starting the server
Review any startup logs for hints about missing environment variables or misconfigurations

Available tools

lint

Run code quality checks using ruff to catch syntax and style issues.

format

Automatically format code with ruff format to ensure consistent styling.

static-analysis

Perform static analysis including mypy, bandit, and semgrep to catch type issues and security vulnerabilities.

test

Execute unit tests with pytest and generate a coverage report.

build

Build the project outputs using the configured build tool (Hatch) for distribution.

publish

Publish built artifacts using the configured publishing workflow.