The observability layer for MCP servers
ContextPulse helps engineering teams understand what happens after an AI client connects to an MCP server: which tools are discovered, what gets called, where execution fails, and whether the agent's goal actually gets accomplished.
Bridging the gap between intelligent models and production software
MCP servers are rapidly becoming crucial product and data surfaces for the next generation of computing. Yet most engineering teams still see only fragmented slices of their usage: raw process stdout logs, HTTP status codes, or generic downstream traces.
Those legacy APM signals do not answer which AI clients are using the server, which tools are being completely ignored, where model parameter hallucination causes retries, or why an agent workflow stopped mid-turn.
ContextPulse turns the Model Context Protocol lifecycle into an actionable view of adoption, reliability, and outcomes—while keeping the server and its tools under complete developer control.
Purpose-built telemetry primitives for AI agents
Our focus is sharp and dedicated: analytics and observability purpose-built for Model Context Protocol servers.
Developer-first, privacy-native, fail-open
We design software for developers shipping production systems. Our SDKs are fail-open, asynchronous, and non-blocking. Payload redaction is built into process memory so that sensitive end-user prompts, tokens, and credentials never cross the boundary to external networks.
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