How GibsonAI Boosted MCP Server Observability with AgentCat

At GibsonAI, our mission is to make database-driven AI agents reliable, powerful, and easy to integrate. GibsonAI databases are created and managed by our AI Agents, and currently ~30% of databases are provisioned using our MCP server. Our MCP server is at the heart of how developers interact with our platform. It powers everything from database schema generation to SQL agent execution.

But as our adoption grew, a pressing question surfaced:

Do we really know how developers and AI agents are using our MCP server?

While we had basic metrics and logs, we lacked a comprehensive view of why tools were called, how sessions unfolded, and where users (or agents) got stuck. This visibility gap led to longer debugging cycles and missed opportunities to optimize the developer experience.

That’s when we discovered AgentCat.

Why We Chose AgentCat

AgentCat is not just a monitoring solution—it’s a product analytics layer built specifically for MCP servers.

What hooked us instantly was its ability to provide session replay and user intent tracking. Unlike traditional metrics dashboards, AgentCat answers questions like:

• Why are people or agents calling specific tools?
• What workflows are they attempting?
• Where do they encounter friction or errors?

Its one-line integration promise was hard to ignore. Within minutes, we had a complete debugging, tracing, and analytics platform that helped us better understand every MCP interaction.

Our Integration Journey

Integrating AgentCat into our MCP server was incredibly simple:

Step 1: Install the SDK

Since our MCP server is built in Python, we just ran:

$uv add agentcat

Step 2: Track the Server

We added one line of code right after registering our tools:

import agentcat
...
agentcat.track(server, "proj_GIBSONAI_PROD")

Step 3: Session Replay and Intent Tracking

With AgentCat’s schema enhancement, every tool call now carries context. This means when an AI agent calls deploy_project, we know not just the parameters but also why the agent made the call—e.g. “Deploying the staging database environment to test new schema changes before rolling out to production.”

Key Wins with AgentCat

1. Debugging Tool Calls in Seconds

Previously, diagnosing a failed deploy_project call was like finding a needle in a haystack. We’d have to dig through the logs for our backend service and analyze stacktraces with limited context. Now, with AgentCat's request tracing, we see real-time logs of every MCP protocol event, along with arguments and timing data.

2. Session Replay = Developer Empathy

The session replay feature is a game-changer. We can literally “watch” the journey of an AI agent or developer through our MCP server—seeing every tool call, context, and error along the way. This helped us identify a deployment step that was causing agents to retry unnecessarily during multi-environment rollouts.

3. Data-Driven Product Decisions

AgentCat analytics revealed that most of our MCP traffic came from core tools like create_project and deploy_project. We optimized these tools, improving response times by 45%. Additionally, we identified underused tools that either needed better documentation or could be deprecated.

What We Love Most About AgentCat

• One-Line Setup: No heavy config, no complex infrastructure.
• Context-Aware Analytics: We’re no longer blind to user intent.
• Zero Performance Impact: Events are processed asynchronously—our MCP server’s low latency didn’t budge.
• Privacy Controls: We can redact sensitive data before it leaves our servers.

The Bottom Line

AgentCat has transformed how we think about observability in the MCP ecosystem. We’ve gone from passively collecting logs to actively understanding user behavior, debugging faster, and building better tools.

If you’re running an MCP server, we can’t recommend AgentCat enough. It’s like having a real-time flight recorder and product analytics dashboard rolled into one.