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sumo-qa

by sumithr
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Python
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sumo_qa_capture_review_feedback

Capture, update, or list recurring QA review lessons to build an advisory memory of common findings. Helps teams remember frequent issues without automatic learning.

Instructions

Manage an EXPLICIT, user-confirmed review-feedback memory of recurring QA findings.

Promotes a recurring review lesson (e.g. "we always miss timezone boundaries in billing") into a local, inspectable, reversible memory that future planning/review skills consult as an ADVISORY hint — NOT automatic learning. action selects the operation:

  • 'capture' — add a new lesson (or replace one with the same id). Requires entry with scope, trigger_signal, recommended_probe, source_note, and optional last_reviewed (ISO-8601; defaults to now).

  • 'update' — replace the fields of an existing lesson; needs entry_id plus entry.

  • 'delete' — remove a lesson by entry_id.

  • 'list' (default) — return stored lessons, advisory-flagged. The scope default is the literal 'project', so it lists the current repo; pass scope='global' for the cross-repo set. An unrecognised scope returns an error envelope (it is never coerced to project).

NEVER persist without explicit user confirmation, and NEVER auto-capture from a review/prompt/trace. That confirmation gate is the HOST/skill's responsibility, not enforced by a tool parameter — the deliberate writer-local data-ownership model shared with the risk-ledger and AC tools; the sumo-qa-feedback CLI correspondingly exposes only list/delete, so a capture can never be a fire-and-forget flag. Sensitive input — a raw diff hunk, a secret, a code snippet, or a pasted full issue/PR body — is REJECTED; only the user's own summary is stored, and a rejected entry is never echoed to the debug-capture sink either. Storage reuses the #92 user-writable pack location (project = /.sumo-qa, global = the user data dir) under a feedback/ subdir, so it is NOT a second hidden tree. Memory-derived probes are ADVISORY: cite them SEPARATELY from bundled ISTQB/rules content; they never override canonical classifications or change-rules.

Common natural-language phrasings that map to this tool: "remember that we always miss X in Y", "save this review lesson", "promote this recurring finding to team memory", "what review lessons have we saved?", "forget the timezone-billing lesson".

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
entryNo
scopeNoproject
actionNolist
entry_idNo

Tool Definition Quality

A4.9/5.0
Behavior5/5

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

Annotations are sparse (readOnlyHint=false, etc.), but the description adds extensive behavioral context: memory is advisory, not automatic; sensitive input is rejected; storage location is specified; and memory-derived probes must be cited separately. No contradiction with annotations.

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 thorough but well-structured with clear sections for each action, constraints, and examples. It is front-loaded with a concise purpose statement. A slight reduction because it is longer than necessary, but every sentence provides value.

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?

For a CRUD-like tool with no output schema, the description fully covers all operations, including what list returns (stored lessons), error cases (unrecognized scope), and safety constraints. It is complete for an AI agent to use 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?

Schema has 0% description coverage, so the description fully compensates by explaining each action's required fields: capture requires entry with specific subfields; update requires entry_id plus entry; delete needs entry_id; list defaults. It also explains the scope parameter defaults and behavior.

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 manages an explicit, user-confirmed review-feedback memory of recurring QA findings. It lists four actions (capture, update, delete, list) and distinguishes this tool from sibling tools by its unique purpose as a memory storage tool for review lessons.

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

Usage Guidelines5/5

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

Provides explicit guidance: never persist without explicit user confirmation, never auto-capture, and lists common natural-language phrasings to help the agent map user intent. It also describes error handling for unrecognized scope and differentiates from other tools by mentioning the shared data-ownership model with risk-ledger and AC tools.

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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