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Tokens MCP
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Tokens MCP

This project implements an MCP server for connecting to the TokenMetrics cryptocurrency data API, providing market data analysis, trading strategy backtesting, and visualization functions, and supporting the development of algorithmic trading systems.
FinanceDeveloper tools#Cryptocurrency#Trading strategy#Data API#Algorithmic trading.Python
2 points
9.4K
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Overview

Installation

Content Details

Alternatives

What is the Tokens MCP service?

This is a middleware service based on the Model Control Protocol (MCP), providing standardized cryptocurrency data access interfaces for AI systems and algorithmic trading. By connecting to the TokenMetrics API, you can obtain real-time market data, test trading strategies, and generate visual analysis reports.

How to use this service?

It can be accessed through simple API calls or an IDE compatible with the MCP protocol (such as Cursor), supporting integration with the Python environment and connection to automated trading systems.

Applicable scenarios

Scenarios such as algorithmic trading strategy development, cryptocurrency market research, automated trading system construction, and investment decision - making auxiliary analysis.

Main features

Cryptocurrency data access
Provide real - time market data, historical prices, and trading volume indicators for over 2000 cryptocurrencies.
Strategy backtesting
Support automated backtesting and performance evaluation of common trading strategies such as moving average crossovers.
Visual analysis
Automatically generate strategy performance charts and visual reports containing key indicators.
IDE integration
Natively support development environments such as Cursor that support the MCP protocol to achieve AI - assisted data analysis.
Advantages
Standardized interfaces simplify the integration of AI systems with financial data.
Built - in strategy backtesting framework accelerates the development of trading algorithms.
Visualization tools intuitively display strategy performance.
Support seamless connection with mainstream development environments.
Limitations
Manual configuration of API keys and path parameters is required.
The testing framework is not fully automated yet.
Some advanced functions depend on the permission level of the TokenMetrics API.

How to use

Environment preparation
Ensure that Python 3.8+ and the uv toolchain are installed.
Get the code
Clone the GitHub repository to the local development environment.
Configure authentication
Set your TokenMetrics API key in the.env file.
Start the service
Run the MCP server process.

Usage examples

Moving average strategy analysis
Compare the crossover signals of the 50 - day and 200 - day moving averages of BTC.
Exchange liquidity analysis
Evaluate the depth data of ETH trading pairs on Binance.

Frequently Asked Questions

What kind of TokenMetrics API permissions are required?
How to solve the 'Invalid path configuration' error?
Which cryptocurrency exchanges' data is supported?

Related resources

Demo video
ETH Hackathon project demo video
TokenMetrics API documentation
Official API reference documentation
MCP protocol specification
Model Context Protocol technical specification

Installation

Copy the following command to your Client for configuration
{
  "mcpServers": {
    "TmaiAPI": {
      "command": "uv",
      "args": ["--directory", "/path/to/tokens-mcp", "run", "mcp", "run", "run.py"],
      "env": {
        "TOKEN_METRICS_API_KEY": "your-api-key-here"
      }
    }
  }
}

{
  "mcpServers": {
    "TmaiAPI": {
      "command": "uv",
      "args": ["--directory", "/path/to/tokens-mcp", "run", "mcp", "run", "run.py"],
                              ^^^^^^^^^^^^^^^^^^^ 
                              Update this to your actual path
      "env": {
        "TOKEN_METRICS_API_KEY": "your-api-key-here"
      }
    }
  }
}
Note: Your key is sensitive information, do not share it with anyone.

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