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Tokens MCP Server

@antonkulaga

代码仓库

基本信息

分类

其他

许可证

Apache-2.0 license

传输方式

stdio

作者

antonkulaga

关于 Tokens MCP Server

MCP server for token metrics

Tokens MCP Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "tokens-mcp": {
      "command": "uv",
      "args": [
        "sync"
      ]
    }
  }
}

Tokens MCP Server 工具

工具列表暂未提供

当前收录暂时没有可展示的工具列表，不代表服务没有工具。请查阅项目文档了解支持的能力。

工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。

概览

What is Tokens MCP?

Tokens MCP is a Model Context Protocol server that provides a standardized interface for AI systems to access the Token Metrics API. It enables access to comprehensive cryptocurrency market data, backtesting trading strategies, generating visual performance metrics, analyzing token performance across timeframes, and building automated trading systems.

How to use Tokens MCP?

Install by cloning the repository and running uv sync. Configure your Token Metrics API key in a .env file and update the mcp_server_config.json file with the key and correct absolute path. Run the server with uv run mcp run run.py. For IDE use, Cursor natively supports MCP and will automatically detect the server when the project is opened.

Key features of Tokens MCP

  • Access comprehensive cryptocurrency market data
  • Implement and backtest trading strategies (e.g., moving average crossovers)
  • Generate visual performance metrics for strategies
  • Analyze token performance across different timeframes
  • Build automated trading systems with consistent API access
  • Integrates with IDEs supporting the Model Context Protocol

Use cases of Tokens MCP

  • Algorithmic trading bot development with crypto data access
  • Market research through token performance analysis
  • Strategy backtesting and visual performance evaluation
  • Automated trading systems with standardized API access

常见问题

1

What does Tokens MCP do?

It serves as an MCP server for the Token Metrics API, allowing AI systems to retrieve cryptocurrency market data, backtest strategies, and generate charts.

2

What are the dependencies and runtime requirements?

The project requires Python, `uv` for dependency management, and a Token Metrics API key. Configuration is done via `.env` and `mcp_server_config.json`.

3

How do I configure the API key?

Copy `.env.example` to `.env` and add your Token Metrics API key. Also update `mcp_server_config.json` with the key and the correct absolute path to the project directory.

4

How does it integrate with IDEs?

IDEs like Cursor that support the Model Context Protocol can automatically detect the server for direct AI interaction with the Token Metrics API.

5

Are there any known limitations?

The configuration file uses absolute paths that must be manually edited. Test files are manual scripts without assertions. Some Token Metrics API endpoints are implemented custom because they are unavailable in the existing library. The repository may contain experimental unused code.

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