Drop-in observability for MCP servers — track tool calls, latency, errors, and usage with one line of code
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# mcp-pulse Drop-in observability for MCP servers. Track tool calls, latency, errors, and usage with one line of code. ## Install ```bash pip install mcp-pulse ``` ## Quick Start ```python from mcp_pulse import ObserveMCP # Replace FastMCP with ObserveMCP — that's it mcp = ObserveMCP("my-server") @mcp.tool() def my_tool(param: str) -> str: """My tool description.""" return f"result for {param}" mcp.run(transport="stdio") ``` Every tool call is now automatically tracked — tool name, duration, success/failure, response size. ## Dashboard ```bash pip install mcp-pulse[dashboard] mcp-pulse ``` Opens a web dashboard at `http://localhost:8020` showing: - Total calls, error rate, avg latency - Calls per hour chart - Per-tool breakdown (call count, p50/p95 latency, error rate) - Recent call log ## Alternative: Wrap an existing server ```python from mcp.server.fastmcp import FastMCP from mcp_pulse import observe mcp = FastMCP("my-server") @mcp.tool() def my_tool(param: str) -> str: return f"result for {param}" observe(mcp) # instruments all registered tools mcp.run(transport="stdio") ``` ## Options ```python mcp = ObserveMCP( "my-server", db_path="/path/to/custom.db", # default: ~/.mcp-pulse/observe.db log_params=True, # log input parameters (default: False) ) ``` ## License MIT
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