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Write up an AI incident

review_ai_incident
Read-onlyIdempotent

A blameless post-incident review for an AI feature: where it failed (input, retrieval, prompt, model, tools, output checks), why evals and monitoring missed it, actions, and the new eval cases that would catch it next time. Returns instructions to follow with the user's text.

Input Schema

NameRequiredDescriptionDefault
incidentNoWhat the feature does, what went wrong, and what's known so far

Output Schema

NameRequiredDescriptionDefault
textYesThe answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text
linksYesThe AI PM Lab pages the answer links to, where each idea can be tried hands-on

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.7/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already establish the safe read-only, idempotent, non-destructive profile, so the bar is lower. The description adds a genuinely useful behavioral fact: it "returns instructions to follow with the user's text" rather than performing the review itself, which explains the readOnlyHint. It stops short of noting any output size or length limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with what the review covers and followed by the key behavioral note about returned instructions. No wasted words, though the mid-sentence enumeration is dense.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With annotations covering safety, full schema coverage on the one parameter, and an output schema present, the description need only carry the concept and the instruction-returning behavior, both of which it does. Only explicit sibling routing is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100% and the single "incident" parameter is fully documented as "what the feature does, what went wrong, and what's known so far." The description's list of failure dimensions adds framing but does not extend the parameter's meaning beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource: "blameless post-incident review for an AI feature," and enumerates the dimensions the review covers (input, retrieval, prompt, model, tools, output checks). It implicitly separates itself from review_ai_spec and review_launch_readiness by being incident-scoped, but never names those alternatives explicitly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

"Post-incident" implies the trigger condition (an incident has occurred), which is reasonable implied usage. However, there is no explicit when-to-use guidance, no when-not-to-use, and no routing to sibling review_ai_spec/review_launch_readiness, leaving the agent to infer.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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