MCPcat provides analytics, issue tracking, and session replay for MCP servers that run AI agents, capturing every tool call and enriching sessions with inferred agent goals. Its platform lets developers replay agent actions, monitor per‑tool performance, and prioritize errors such as hallucinations or crashes, with OpenTelemetry export for integration into existing observability stacks.
Funding
Funding not disclosed
Founders
Product
Problem
AI agents interacting with MCP servers generate large volumes of tool calls, but existing analytics and observability solutions are designed for human users and lack insight into agent intent, error patterns, and performance at the tool level.
Solution
MCPcat provides a dedicated analytics and debugging layer for MCP servers that captures every agent session, enriches it with inferred agent goals, and stores detailed request/response data. The platform offers session replay so developers can step through each tool call, view inputs, outputs, and errors, and understand why agents made specific calls. Built-in issue tracking surfaces frequent errors, hallucinations, and crashes, prioritizing them by impact. Per‑tool performance monitoring highlights latency and error rates, enabling quick identification of problematic tools. All telemetry can be forwarded via OpenTelemetry to existing observability stacks such as Datadog, Sentry, or PostHog, allowing teams to integrate MCPcat insights into their current workflows.
Target Audience
Primary customers are developers and product teams that operate MCP servers for AI agents, including SaaS platforms, AI‑enabled tooling providers, and internal AI infrastructure groups seeking observability and product analytics.
Features
- One‑line SDK integration for TypeScript and Python that automatically instruments MCP servers and begins tracking sessions
- Session replay UI that visualizes each tool call with request payload, response, and error details
- Automatic inference of agent goals from activity patterns, presented in cohort and goal‑specific reports
- Issue detection that aggregates errors, hallucinations, and crashes, with impact‑based prioritization
- Per‑tool performance dashboards showing latency, success rates, and error trends over time
- Customizable filtering by client, server, location, model, and user‑defined attributes for cohort analysis
- OpenTelemetry export to third‑party monitoring platforms (Datadog, Sentry, PostHog, etc.)