From Data Dumps To Smart Context: Building MCP Servers That AI Can Actually Use
summary
Most MCP servers dump every metric; this one instead captures full‑stack traces, logs, requests and headers and pre-correlates sessions so agents get a compact, actionable view. It measures a fixability score and assesses fixability and curates intent‑driven context to produce far fewer but higher‑quality PRs rather than tens of thousands of wasted tokens (one naive session hit ~100k tokens). The debugger runs locally and runs locally so code never leaves the developer's machine, and the approach reduces hallucinations and wrong-tool calls.
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chapters (12)
- 0:00MCP and AI observability
- 1:02Future of observability
- 3:30MCP struggles with data
- 8:04Debugging workflow pain points
- 9:50MCP server development lessons
- 11:57Intent-aware AI tools
- 13:00Skills and MCP convergence
- 14:46Debugging agent requirements
- 16:33Future AI connectivity stack
- 18:21Custom debugging tools
- 19:04AI-ready observability
- 19:45Path to self-healing systems
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