mcpField
M
MCP Server for Gravatar

MCP Server for Gravatar

Local
github.com/andrewdmontgomerySocial Media0Updated 78w ago

About MCP Server for Gravatar

MCP Server for Gravatar is a stdio-based server for accessing Gravatar profile and avatar data. It is intended for MCP clients that need to retrieve profiles, selected fields, or avatar images.

Key features

  1. 01Fetch profiles by email or SHA256 hash
  2. 02Retrieve selected profile fields
  3. 03List user avatars
  4. 04Fetch avatar image bytes

Use cases

  1. 01Look up a Gravatar profile from an email
  2. 02Retrieve specific fields from a profile
  3. 03Access a user's avatar image

MCP support

Provides tools over stdio that MCP clients can discover and invoke to retrieve Gravatar profile and avatar data.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

MCP Server for Gravatar

A stdio-based Model Context Protocol (MCP) server that provides access to Gravatar profile and avatar data. Clients (e.g., Claude Desktop, custom IDE plugins) can discover and invoke tools via MCP to fetch Gravatar profiles and avatars.

Features

  • Fetch Gravatar profile by email or SHA256 hash
  • Retrieve specific profile fields
  • List user avatars and fetch avatar image bytes
  • Works over stdio for easy integration with MCP-aware clients

Prerequisites

  • Python 3.10 or later
  • uv CLI tool installed (provides uv sync and uv run)
  • (Optional) Docker, if you need to regenerate the OpenAPI client via make generate

Setup

  1. Clone the repository

    git clone https://github.com/andrewdmontgomery/mcp-server-gravatar.git
    cd mcp-server-gravatar
    
  2. Install dependencies

    uv sync
    

    This will install all Python dependencies specified in pyproject.toml into your active virtual environment.

Regenerating the OpenAPI client

(if you’ve updated openapi.yaml)

Caution

Regenerating the client will cause problems. There are manual changes that have been applied to the generated code in order to fix issues. If you re-generate the openapi_client, those changes should be retained. Ideally these changes would by applied as a post-generation step.

make generate

This runs the OpenAPI Generator Docker image and synchronizes the generated gravatar_api_client/openapi_client into src/openapi_client.

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Using uvx
"mcpServers": {
  "git": {
    "command": "uvx",
    "args": ["--from", "git+https://github.com/andrewdmontgomery/mcp-server-gravatar", "mcp-server-gravatar"]
  }
}

Or if you want to load a specific branch (e.g. add/my-feature):

"mcpServers": {
  "git": {
    "command": "uvx",
    "args": ["--from", "git+https://github.com/andrewdmontgomery/mcp-server-gravatar@add/my-feature", "mcp-server-gravatar"]
  }
}

Running the MCP Server

You can start the server directly:

# Run the MCP server
uv run mcp-server-gravatar

This can be helpful for debugging startup issues.

With uvx, you can also start the server from the repo:

uvx --from git+https://github.com/andrewdmontgomery/mcp-server-gravatar mcp-server-gravatar

You can specify a branch, too (e.g. add/my-feature):

uvx --from git+https://github.com/andrewdmontgomery/mcp-server-gravatar@add/my-feature mcp-server-gravatar

Debugging with MCP Inspector

You can use the MCP Inspector to trace and debug prompt and tool executions. For example:

  1. Run Inspector:

    make inspector
    

    Or manually:

    npx @modelcontextprotocol/inspector uv run mcp-server-gravatar
    Starting MCP inspector...
    ⚙️ Proxy server listening on port 6277
    🔍 MCP Inspector is up and running at http://127.0.0.1:6274
    
  2. Open the Inspector UI in your browser

    • In the example above: http://localhost:6274.

For more details, see the official docs:
https://modelcontextprotocol.io/docs/tools/inspector

Makefile Targets

  • make generate — regenerate and sync the OpenAPI client
  • make clean — remove generated client files

Environment Variables

  • GRAVATAR_API_TOKEN — your Gravatar API key

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.