Understand How LLMs and Agents Use Your MCP Server
Track tool usage, agent sessions, and AI behavior at your interface.
Your Model Context Protocol server is now an AI entry point. Edgee gives you clarity on how LLMs call your tools, what they request, and how users and agents interact with your capabilities.
What It Does
📡 Track LLM & Agent Requests
See which AI models call your MCP, what tools they invoke, and how often.
🧩 Analyze Tool-Level Behavior
Understand which tools drive value, how they’re used, and where optimization is needed.
👥 Monitor Users & Sessions
Track how users (human or agent) interact with your MCP interface — with events, session IDs, and durations.
📊 Identify AI Usage Patterns
Spot peaks, trends, and model-specific behavior across all agent flows.
Key Metrics
🟦 MCP Call Volume & Activity
- Total MCP Calls
- Active Tools Count
- MCP Call Trend Over Time
🟩 Tool Usage & Performance
- Calls per Tool (Frequency)
- Tool Success / Error Rate
- Popular Tools Ranking
🟪 LLM & Agent Behavior
- LLMs Calling MCP (User-Agent Breakdown)
- Model-Specific Call Patterns
- Error Patterns by LLM / Tool
🟧 User & Session Insights
- Number of Users
- Number of Sessions
- Avg Session Duration
Why It Matters
🔬 Understand AI-powered usage of your MCP tools
See which LLMs and agents rely on your MCP server, how they use your tools, and what drives the highest levels of activity or value.
🚦 Improve the performance and reliability of your MCP endpoint
Identify errors, slowdowns, and unexpected usage patterns. Optimize tool performance and ensure smooth workflows.
🤖 See which LLMs depend on your capabilities — and how they behave
Break down behavior by model or agent and understand tool usage and trends over time.
📈 Unlock insights to enhance your AI agent ecosystem
Use usage trends, session data, and tool-level metrics to improve MCP design and agent behaviors.
