Coin MCP Server

What does “Coin MCP” MCP Server do?

The Coin MCP Server is a Model Context Protocol (MCP) server that provides seamless access to CoinMarketCap’s cryptocurrency data. It enables AI-powered applications and assistants to retrieve up-to-date cryptocurrency listings, real-time market quotes, and detailed information about various coins. By acting as a bridge between AI clients and the CoinMarketCap API, the Coin MCP Server simplifies workflows such as querying market data, fetching coin details, and integrating cryptocurrency analytics into development environments. This makes it an essential tool for developers and AI agents who need to access and interact with comprehensive cryptocurrency data for research, trading, or financial analysis.

List of Prompts

No prompt templates were mentioned in the repository. This section is intentionally left empty.

List of Resources

No explicit resources are documented in the repository for the Coin MCP Server. This section is intentionally left empty.

List of Tools

• listing-coins
  Fetches a paginated list of all active cryptocurrencies with the latest market data. Supports filters such as price, market cap, currency conversion, sorting, and pagination.

• get-coin-info
  Retrieves detailed information about a specific cryptocurrency by ID, slug, or symbol.

• get-coin-quotes
  Fetches the latest market quotes for one or more cryptocurrencies, identified by ID, slug, or symbol.

Use Cases of this MCP Server

• Cryptocurrency Market Analysis
  Developers and analysts can retrieve up-to-date listings and detailed market data for thousands of cryptocurrencies, supporting research and financial analysis.

• Portfolio Tracking
  Integrate live coin quotes and detailed coin information into portfolio management tools, enabling real-time tracking of asset values and market trends.

• Automated Trading Bots
  Power trading algorithms with real-time market data, allowing bots to make informed decisions based on the latest cryptocurrency prices and market conditions.

• Educational Apps
  Enhance educational platforms or chatbots with the ability to answer queries about cryptocurrencies, their market caps, and historical performance.

• Custom Dashboards
  Build custom dashboards that visualize current listings, detailed coin info, and price movements for a range of digital assets.

How to set it up

Windsurf

No setup instructions for Windsurf were provided in the repository.

Claude

1. Ensure Python is installed and install the Coin MCP Server:

   pip install .
   

2. Obtain a CoinMarketCap API key from CoinMarketCap’s website.
3. Add the following to your Claude settings:

   "mcpServers": {
     "coin_api": {
       "command": "python",
       "args": ["-m", "coin_api_mcp"]
     },
     "env": {
       "COINMARKETCAP_API_KEY": "your_api_key_here"
     }
   }
   

4. Save your settings and restart Claude.
5. Verify by running a coin query through Claude.

Securing API keys:
You can secure your CoinMarketCap API key via environment variables:

"env": {
  "COINMARKETCAP_API_KEY": "your_api_key_here"
}

Cursor

No setup instructions for Cursor were provided in the repository.

Cline

No setup instructions for Cline were provided in the repository.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "coin_api_mcp": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “coin_api_mcp” to whatever the actual name of your MCP server is and replace the URL with your own MCP server URL.

Overview

Our opinion

The Coin MCP Server provides essential cryptocurrency data tooling for AI and developer workflows with clear API key handling and well-documented tools. However, the absence of documented prompt templates, explicit resource types, and platform-specific setup details lowers its completeness for broader integration scenarios.

MCP Score

Rating: Based on the two tables, this MCP implementation would score a 6/10. It is strong in core tool exposure and licensing but lacks completeness in prompt/resource documentation and broad platform support.