Mcp Tracker
What is Mcp Tracker
MCP Tracker is a monitoring server designed to pull metrics and logs, providing debugging suggestions for users of the MCP system.
Use cases
Use cases for MCP Tracker include monitoring server performance metrics, analyzing application behavior over time, and providing insights for debugging and optimization.
How to use
To use MCP Tracker, implement the ‘record_metric’ function to log metrics related to your server. You can also run the Streamlit dashboard using the command ‘streamlit run dashboard.py’ and access it via ‘http://localhost:8501’.
Key features
Key features of MCP Tracker include the ability to record various metrics with associated metadata, a user-friendly Streamlit dashboard for visualizing metrics, and debugging suggestions to enhance server performance.
Where to use
MCP Tracker can be used in various fields such as software development, system monitoring, and data analysis where tracking performance metrics is essential.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Tracker
MCP Tracker is a monitoring server designed to pull metrics and logs, providing debugging suggestions for users of the MCP system.
Use cases
Use cases for MCP Tracker include monitoring server performance metrics, analyzing application behavior over time, and providing insights for debugging and optimization.
How to use
To use MCP Tracker, implement the ‘record_metric’ function to log metrics related to your server. You can also run the Streamlit dashboard using the command ‘streamlit run dashboard.py’ and access it via ‘http://localhost:8501’.
Key features
Key features of MCP Tracker include the ability to record various metrics with associated metadata, a user-friendly Streamlit dashboard for visualizing metrics, and debugging suggestions to enhance server performance.
Where to use
MCP Tracker can be used in various fields such as software development, system monitoring, and data analysis where tracking performance metrics is essential.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
MCP Tracker
MCP server
MCP tracker server - monitor.py
Supposed to pull metrics, logs, provide debugging suggestions
In user MCP, add function to record metric -
def record_metric(server_name: str, metric_name: str, value: float, unit: str = "", tags: dict[str, Any] = {}) -> str: supabase.table("mcp_metrics").insert({ "server_name": server_name, "metric_name": metric_name, "value": value, "unit": unit, "tags": tags }).execute()
Record metric from mcp.tool function as -
# Send temperature as metric record_metric( server_name="weather-mcp", metric_name=f"forecast_temp_{name}", value=temp, unit=unit, tags={"wind": wind, "description": desc} }
Streamlit dashboard for metrics
To run -
streamlit run dashboard.py
Open http://localhost:8501 to view metrics dashboard
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
