Vision

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": {
    "ag2-mcp-servers-cloud-vision-api": {
      "command": "python",
      "args": [
        "mcp_server/main.py",
        "stdio"
      ],
      "env": {
        "CONFIG": "{\"key\":\"value\"}",
        "SECURITY": "your_security_parameters",
        "CONFIG_PATH": "path/to/mcp_config.json"
      }
    }
  }
}

You run an MCP (Model Context Protocol) Server locally to bridge your client applications with the Vision API described by the OpenAPI model. This server lets your tools talk to the API through a consistent, MCP-based interface, enabling streamlined development, testing, and integration without embedding API details in every client.

How to use

Start the MCP server in stdio mode so your client applications can communicate over standard input/output. Begin by launching the server process, then connect your MCP client to it. The server is designed to be controlled from your development environment, and it accepts configuration through environment variables or a config file.

How to install

Prerequisites you need before installing and running the MCP server:

Python 3.9+
pip
uv

Install and set up your development environment with these steps:

git clone <repository-url>
cd mcp-server
pip install -e ".[dev]"
# Alternatively install with uv if you prefer
uv pip install --editable ".[dev]"

Additional sections

Configuration is passed through environment variables or a JSON configuration file. You can control the server’s startup and security parameters via these options.

Troubleshooting and notes

If you encounter issues starting the server, verify that Python 3.9+ is present, dependencies are installed, and the environment variables are correctly configured. Review any error messages from the startup sequence and ensure you are invoking the exact command to launch the server in stdio mode.

Available tools

lint

Run linting to enforce code quality and formatting.

format

Apply code formatting to ensure consistent style.

static_analysis

Perform static analysis checks (mypy, bandit, semgrep) to catch issues early.

tests

Execute test suite with coverage to verify functionality.

start_server

Launch the MCP server in the requested transport mode (stdio in this setup).

connect_client

Connect an MCP client to the server using the MCP protocol over the chosen transport.