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Wormhole Metrics
- python
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python
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7 months ago
First Indexed
3 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": {
"kukapay-wormhole-metrics-mcp": {
"command": "uv",
"args": [
"--directory",
"/path/to/wormhole-metrics-mcp",
"run",
"main.py"
]
}
}
}Wormhole Metrics MCP is an event-driven server that analyzes cross-chain activity on the Wormhole protocol and presents insights like transaction volumes, top assets, source-destination chain pairs, and KPI data. It exposes a set of tools via MCP to fetch, transform, and display this data in clear Markdown tables, making it easy to monitor cross-chain movements and performance at a glance.
How to use
You interact with the Wormhole Metrics MCP through an MCP client. After your MCP server is running, you can request specific data views and receive results as Markdown tables. Use the available tools to focus on the metrics you care about, such as cross-chain activity, money flow, top assets by volume, chain pairs, symbols, corridors, and KPIs. Combine parameters like time span, source/destination chains, and app identifiers to tailor the data to your needs. The results are ready-to-paste Markdown tables, suitable for dashboards, reports, or collaboration channels.
How to install
Prerequisites you need before installing are Python 3.10 or higher and the uv runtime manager.
Step by step installation and setup:
Clone the repository and navigate into the project directory.
Install dependencies and prepare the MCP environment.
If you are using Claude Desktop, install the MCP server as a Claude Desktop application.
Install commands and configuration
# Prerequisites
# Ensure you have Python 3.10+ and uv installed
# Setup
git clone https://github.com/kukapay/wormhole-metrics-mcp.git
cd wormhole-metrics-mcp
# Install/prepare MCP environment
uv sync
# Optional: Install as a Claude Desktop MCP server (if you are using Claude Desktop)
uv run mcp install main.py --name "Wormhole Metrics"
MCP server configuration example
{
"mcpServers": {
"Wormhole Metrics": {
"command": "uv",
"args": [ "--directory", "/path/to/wormhole-metrics-mcp", "run", "main.py" ]
}
}
}
Notes on runtimes and starting the server
The final start command shown in the configuration uses the uv runtime to run the main MCP module from the specified directory. Make sure to replace "/path/to/wormhole-metrics-mcp" with your actual installation path.
Additional notes
The server exposes a suite of tools described below. Each tool returns data in Markdown table format for easy consumption in reports and dashboards.
Tools overview
-
get_cross_chain_activity: fetches cross-chain activity data and returns a pivot table of volumes between source and destination chains.
-
get_money_flow: retrieves transaction counts and volumes for a given period.
-
get_top_assets_by_volume: lists top assets by volume including emitter and token chains.
-
get_top_chain_pairs_by_num_transfers: returns top source-destination chain pairs by number of transfers.
-
get_top_symbols_by_volume: fetches top symbols by volume and transaction count.
-
get_top100_corridors: lists the top 100 token corridors by number of transactions.
-
get_kpi_list: retrieves key performance indicators (KPIs) for the Wormhole protocol.
Available tools
get_cross_chain_activity
Fetches cross-chain activity data and returns a pivot table showing volumes between source and destination chains.
get_money_flow
Retrieves transaction counts and volumes for a specified period.
get_top_assets_by_volume
Lists top assets by volume, including emitter and token chains.
get_top_chain_pairs_by_num_transfers
Returns top source-destination chain pairs by the number of transfers.
get_top_symbols_by_volume
Fetches top symbols by volume and transaction count.
get_top100_corridors
Lists the top 100 token corridors by number of transactions.
get_kpi_list
Retrieves key performance indicators (KPIs) for Wormhole metrics.