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Write acceptance criteria for an AI feature

write_acceptance_criteria
Read-onlyIdempotent

Acceptance criteria for an AI feature or user story that can actually be tested: Given/When/Then with measurable pass bars on a test set, must-pass safety criteria, latency and cost budgets, and example test cases, ready to paste into a ticket. Returns instructions to follow with the user's text.

Input Schema

NameRequiredDescriptionDefault
featureNoThe AI feature or user story, who uses it, and what it must never do

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. Changed1 schema field changed
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "$schema": "https://json-schema.org/draft/2020-12/schema",
+  "additionalProperties": false,
+  "properties": {
+    "links": {
+      "description": "The AI PM Lab pages the answer links to, where each idea can be tried hands-on",
+      "items": {
+        "type": "string"
+      },
+      "type": "array"
+    },
+    "text": {
+      "description": "The answer in markdown, as shown to the user; for the guides, instructions to follow with the user's text",
+      "type": "string"
+    }
+  },
+  "required": [
+    "text",
+    "links"
+  ],
+  "type": "object"
+}
  2. Added

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already carry the safety profile (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds non-obvious context: the tool 'Returns instructions to follow with the user's text', i.e. it does not itself emit the final artifact. That is real behavioral value beyond the annotations, though it stops short of describing limits or formatting of the returned instructions.

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?

One dense but front-loaded sentence: the deliverable leads, the content list follows, and the return behavior closes it. No filler, though the long comma-separated inventory pushes it toward list-like packing rather than crisp prose.

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 an output schema present the description needn't explain return values, and it still sketches what the criteria will contain plus the note that it returns follow-up instructions. For a single-parameter generation tool this is close to complete; only explicit routing against sibling review/design tools 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%, so the single 'feature' parameter is already fully documented in the schema, and the description's 'with the user's text' only loosely maps to it. Baseline 3 applies when the schema does the heavy lifting.

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 concrete deliverable (acceptance criteria for an AI feature/user story) and enumerates its contents — Given/When/Then, pass bars, safety criteria, latency/cost budgets, example test cases — so the agent knows exactly what artifact this produces. It does not name or distinguish itself from nearby siblings like design_eval_suite or review_ai_spec, which keeps it below a 5.

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?

Usage is implied rather than stated: the criteria are 'ready to paste into a ticket' and require 'the user's text', which frames the context, but there is no explicit when-to-use, when-not-to-use, or pointer to an alternative such as review_ai_spec for critiquing an existing spec. Minimum viable but with clear gaps.

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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