bridge-metrics-mcp

Purpose

bridge-metrics-mcp is an MCP (Model Context Protocol) server designed to provide AI agents with real-time cross-chain bridge metrics, enabling them to analyze liquidity, transaction flow, and performance between blockchain networks.

Main capabilities

The server provides the following tools, accessible via JSON-RPC or natural language prompts from compatible clients:

• list_chains: Lists the slugs of all chains from DeFiLlama, sorted in descending order by total value locked (TVL). No parameters. The output table includes the columns Name, Gecko ID, Token Symbol, Chain ID, TVL.
• list_bridges: Lists all bridges, sorted by 24-hour trading volume, optionally including chain information. Parameter include_chains(boolean, default False). The output table includes the columns ID, Name, Display Name, Last 24h Volume, Chains.
• get_historical_volumes: Retrieves historical trading volume data for a chain or bridge, with dates in the format YYYY-MM-DD HH:MM:SS. Parameter chain(string, required),bridge_id(integer, optional). The output table includes the columns Date, Deposit USD, Withdraw USD, Deposit Txs, Withdraw Txs.
• get_day_stats: Retrieves 24-hour token trading volume details, sorted by deposit USD value. Parameter timestamp(integer, required),chain(string, required),bridge_id(integer, optional). The output table includes the columns Token ID, Symbol, Decimals, Deposit Amount, Deposit USD Value, Withdraw Amount, Withdraw USD Value.
• get_transactions: Retrieves transaction summaries for a specific bridge, sorted by timestamp. Parameter bridge_id(integer, required),start_timestamp、end_timestamp、source_chain、address、limit(all optional). The output table includes the columns Tx Hash, Timestamp, Block, From, To, Token, Amount, Type, Chain, Bridge Name, USD Value.

In addition, it also includes prompt examples:

• analyze_bridge_volume: Analyzes trading volume data for a specific bridge.
• compare_bridges: Compares two bridges based on specified metrics (such as weeklyVolume).

Usage

Environment requirements

• Python: version 3.10 or higher.
• uv: Python's package and virtual environment manager (recommended for dependency management).

Installation steps

1. Clone the repository:

   git clone https://github.com/kukapay/bridge-metrics-mcp.git
   cd bridge-metrics-mcp
   

2. Install dependencies:

   uv sync
   

3. Install to Claude Desktop: Install the server as a Claude Desktop application:

   uv run mcp install main.py --name "Bridge Metrics"
   

   Configuration file reference:

   {
     "mcpServers": {
       "Bridge Metrics": {
         "command": "uv",
         "args": ["--directory", "/path/to/bridge-metrics-mcp", "run", "main.py"]
       }
     }
   }
   

   Replace /path/to/bridge-metrics-mcp with the actual installation path.

Example interactions

The following are example interactions using natural language prompts in MCP-compatible clients:

• Example 1: List all chains (call list_chains)
• Example 2: List bridges and chain information (call list_bridges(include_chains=True))
• Example 3: Retrieve historical trading volume for a chain (call get_historical_volumes(chain="ethereum"))
• Example 4: Retrieve daily token statistics for a bridge (call get_day_stats(timestamp=1752499200, chain="ethereum", bridge_id=1))
• Example 5: Analyze bridge trading volume (call get_historical_volumes(chain="polygon", bridge_id=1), then call analyze_bridge_volume)

Applicable scenarios

Suitable for AI agent analysis scenarios that require real-time cross-chain bridge data, such as monitoring liquidity, comparing the performance of different bridges, and analyzing historical trading volume and transaction flow.

Limitations

The project uses the MIT license. Other limitation information is not currently provided.