Tokens MCP
@antonkulaga
About Tokens MCP
MCP server for token metrics
Config
Add this server to your MCP-compatible client using the configuration below.
{
"mcpServers": {
"tokens-mcp": {
"command": "uv",
"args": [
"sync"
]
}
}
}Tools
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Overview
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
FAQ from Tokens MCP
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.
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.
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.
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.
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.
Frequently asked questions
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.
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`.
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.
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.
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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