{"name":"Bento","description":"Production infrastructure for AI agents. Monitor what runs. Improve what fails. Compound learnings.","url":"https://docs.bentolabs.ai/","version":"1.0.0","protocolVersion":"0.3","preferredTransport":"HTTP+JSON","supportedInterfaces":[{"url":"https://docs.bentolabs.ai/","protocolBinding":"HTTP+JSON","protocolVersion":"0.3"}],"provider":{"url":"https://docs.bentolabs.ai/","organization":"Bento"},"documentationUrl":"https://docs.bentolabs.ai/","capabilities":{"streaming":false,"pushNotifications":false},"defaultInputModes":["text/plain"],"defaultOutputModes":["text/plain"],"skills":[{"id":"bentolabs","name":"Bentolabs","description":"Use when instrumenting AI agents for production monitoring, tracking LLM calls and tool execution, analyzing agent failures, creating detectors for failure patterns, and triaging issues from the CLI or dashboard. Agents should reach for this skill when setting up observability for AI applications, sending traces from Python code, querying runs and signals, or investigating production issues.","tags":[],"url":"https://docs.bentolabs.ai/.well-known/agent-skills/bentolabs/skill.md"}]}