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review_qa_document

Review QA documents (test plans, strategies, test cases) against a six-dimension ISTQB/IEEE-829 rubric, returning scores and findings with citations.

Instructions

Deterministically review an existing QA document (Test Plan, Test Strategy, or a test case list) against a six-dimension ISTQB/IEEE-829- grounded rubric (structure completeness, objectives & scope clarity, entry/exit criteria, traceability, measurability, risk coverage) — no LLM anywhere in this call path; write your own narrative from the returned findings. doc_type must be one of "auto", "test_plan", "test_strategy", "test_cases" — "auto" runs a cheap heading-keyword classifier and reports which type it assumed; an unrecognized value returns a structured {"error": "invalid_argument", ...} rather than raising. Documents under ~200 characters (after stripping this app's own AI-disclosure front matter/footer) return doc_type="insufficient_content" with overall_score=0 rather than an error. Each finding carries kb_citations resolved from the knowledge base for its citation queries — a finding with no resolvable source is returned with an empty kb_citations list rather than a fabricated one. Returns {doc_type, overall_score, dimension_scores, findings, stats, kb_version}.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
doc_typeNoauto
document_textYes
Behavior5/5

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

With no annotations provided, the description fully discloses behavioral traits: determinism, no LLM involvement, auto-classification behavior, error handling for invalid doc_type, handling of short documents, and how kb_citations are resolved. This leaves no ambiguity about the tool's operation.

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?

The description is detailed and well-structured, with the main purpose front-loaded. Each sentence adds value, but it is somewhat verbose. Considering the complexity of the tool, it achieves good conciseness while covering all necessary information.

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

Completeness5/5

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

Given the absence of an output schema and annotations, the description covers all aspects: input parameters, behavior, edge cases, error handling, and return structure. It is fully complete for an AI agent to use the tool correctly.

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

Parameters5/5

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

The schema has 0% description coverage, but the description compensates fully by explaining the doc_type parameter's values, default, and auto behavior, as well as the document_text parameter's role. It adds critical meaning about valid inputs, error cases, and special conditions like the length threshold.

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

Purpose5/5

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

The description clearly states the tool reviews an existing QA document against a six-dimension ISTQB/IEEE-829-grounded rubric. It specifies acceptable document types and distinguishes itself from sibling tools like retrieve_qa_knowledge or estimate_qa_effort, which serve different purposes.

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

Usage Guidelines4/5

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

The description provides detailed usage guidance for the doc_type parameter, including valid values, the behavior of 'auto', and error handling for unrecognized values. It also mentions edge cases like short documents. However, it does not explicitly contrast with sibling tools to indicate when to use this tool versus alternatives.

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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