MCP4Meme

Provides bonding curve analytics, market data, and discovery endpoints for meme tokens via MCP.

python

0

GitHub Stars

python

Language

5 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "hawkli-1994-mcp4meme": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "mcp4meme"
      ],
      "env": {
        "BITQUERY_API_KEY": "YOUR_API_KEY_HERE"
      }
    }
  }
}

MCP4Meme is a lightweight MCP server that exposes token bonding curve analytics, trading data, and discovery capabilities for meme-related assets. It lets you query bonding curve progress, token migrations, latest trades, prices, volumes, and trends, all through a simple MCP client workflow.

How to use

You interact with MCP4Meme using an MCP client that connects to the server, requests data endpoints, and renders results in your UI or tooling. You can explore token bonding curve progress, check token migration status, view recent trades and price data, track top traders and liquidity events, and perform discovery searches for tokens by progress or trending signals. Use the available tool endpoints to retrieve structured data, and combine multiple calls to build dashboards, alerts, or automated analyses.

Typical usage patterns include: - Retrieve the bonding curve progress for a token to see how close it is to graduation. - Query the latest trades and OHLCV-style volume data to analyze market activity. - Get top traders and liquidity events to understand who is moving markets. - Search tokens by bonding curve progress to find tokens near a specific completion window.

How to install

Prerequisites: you need Docker to run the MCP server in a contained environment. If you prefer a local Python run, you can also run the server directly with Python if you install the dependencies, but using Docker is the recommended path for consistency.

Option A: Run via Docker (recommended) - Without an API key (mock data): run the container in interactive mode using the standard image name.

Option B: Run via Docker with API key (real data): run the container in interactive mode and pass your API key through an environment variable.

Option C: Local Python run (alternative): you can start the Python server directly if you want to test without Docker, using the same codebase and dependencies.

Additional notes

Configuration is driven by environment variables and simple runtime flags. If you provide an API key, the server will fetch real data; without it, mock data is returned for testing. Environment variables are described in the configuration examples, and you can adapt them to your deployment environment.

Security considerations: expose only the necessary endpoints to trusted clients, and rotate API keys if you’re using real data sources. Monitor for unusual activity in trading data and token discovery signals. Regularly update the Docker image or Python environment to keep dependencies secure.

Config and usage details

The server exposes the following core data endpoints via MCP calls. You can integrate these into your client application or dashboards.

Troubleshooting

If you encounter connectivity issues, ensure the container is running and listening on the expected port, and verify that your MCP client is pointed at the correct address. Check logs for initialization errors, missing dependencies, or API key misconfigurations.

Examples and tips

When testing locally, you can start with mock data to verify your client integration, then switch to real data by providing a valid API key. Use consistent token addresses in your queries to compare results across calls (bonding curve progress, price data, trades, and volumes) and build coherent dashboards.

Available tools

get_bonding_curve_progress

Return the bonding curve completion percentage for a given token address to gauge how far it is from graduation.

get_token_migration_status

Check whether a token has migrated from the bonding curve to the DEX, including status and timestamps.

get_latest_trades

Fetch the most recent trading records for a token, with an optional limit.

get_token_price_usd

Retrieve the current price of a token in USD.

get_trading_volume

Provide trading volume statistics for a token over a specified timeframe.

get_top_traders

Identify the top traders by volume within a timeframe.

get_liquidity_events

List liquidity additions and removals affecting a token.

search_tokens_by_progress

Find tokens by bonding curve progress within a specified range.

get_trending_tokens

List tokens currently trending by volume, trades, or progress.

add

Legacy demo tool: add two numbers.

multiply

Legacy demo tool: multiply two numbers.

get_greeting

Legacy demo tool: return a personalized greeting.