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sumo-qa

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sumo_qa_capture_review_feedback

Capture user-confirmed recurring QA review findings into advisory memory. List, update, or delete stored lessons to inform future planning and reviews.

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

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.42.0

TDQS

A4.9/5.0
Behavior5/5

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

Annotations only give the generic read/write/idempotency flags; the description adds the critical behavioral facts an agent needs: the confirmation gate is the host's responsibility and not enforceable via a parameter, sensitive input (diff hunks, secrets, PR bodies) is rejected and never echoed, and memory-derived probes are advisory only. This is rich context beyond the structured fields.

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?

It is well front-loaded (purpose, then action list, then caveats) and every section is substantive, but the ALL-CAPS emphasis and the trailing natural-language-phrasing block make it longer than strictly necessary for an agent reader.

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 four-parameter, no-output-schema mutation tool of this complexity, the description covers operations, defaults, storage locations, error behavior, and the advisory/confirmation model. Nothing an agent needs to call it correctly is missing.

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 description coverage is 0%, so the description carries the full burden and does so: it defines the action enum values, the required fields of `entry` (scope, trigger_signal, recommended_probe, source_note, optional last_reviewed with ISO-8601 default), the role of `entry_id`, and the scope default plus the error behavior for an unrecognised scope.

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 states a specific verb+resource ('manage an explicit, user-confirmed review-feedback memory of recurring QA findings') and immediately distinguishes it from automatic learning, risk-ledger, AC tools, and the sumo-qa-feedback CLI. An agent can identify the tool's scope without reading any sibling schema.

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?

It enumerates all four operations with their required arguments, states the default action ('list') and scope ('project' vs 'global'), and even maps natural-language user phrasings to the tool. Exclusions are explicit (never persist without confirmation, never auto-capture).

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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