AIWorkhub

by shrec

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

aiworkhub_manager_learning_commit

MANAGER WRITE: persist and project one verified Learning Commit.

Instructions

MANAGER WRITE: persist and project one verified Learning Commit.

SHORT FORM -- the one to use: task_id, request_id and the lesson. Everything mechanical is resolved server-side from the card's OWN decision event: outcome is read from the adjudication (never asserted), repo_area from the paths that decision was taken over, evidence_ids from the sealed FIXED_AND_VERIFIED acceptance reference, and idempotency_key/provenance are the deterministic strings the learning_commit_owed payload in your accept/reject reply already carried. failure_category is always derived server-side from the card's structured terminal evidence and is never caller-suppliable. repo_area omitted is derived from the decision's paths; any explicitly supplied value is normalized and honored, while an empty or whitespace value is refused with learning_commit_repo_area_invalid:empty.

Add your own references with extra_evidence_ids. An evidence id must carry an allowed scheme -- file:, http: or https:. A sha256: receipt id is refused, and any refusal here returns allowed_evidence_id_schemes.

outcome is accepted, rejected or inconclusive; only the first two may promote, and each promotion needs what its store demands (a lesson for AI Memory, an invariant for KB, an edge plus an acceptance for Context Graph).

LONG FORM (compatibility): supply repo_area, outcome, evidence_ids, idempotency_key and provenance yourself and nothing is resolved. The lesson text is always the caller's; nothing is written without this call.

Input Schema

Name	Required	Description	Default
`outcome`	No
`task_id`	Yes
`repo_area`	No
`promote_kb`	No
`provenance`	No
`request_id`	Yes
`evidence_ids`	No
`edge_candidates`	No
`idempotency_key`	No
`lesson_candidate`	No
`promote_ai_memory`	No
`extra_evidence_ids`	No
`invariant_candidate`	No
`root_cause_candidate`	No
`promote_context_graph`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 20, 2026

TDQS

A4.5/5.0

Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so well: it states that outcome, repo_area, evidence_ids, idempotency_key, provenance, and failure_category are server-derived rather than caller-asserted. It also discloses evidence-scheme rules, refusal behavior, and that nothing is written without this call.

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 front-loaded with the recommended short form and then structured into short-form, evidence, outcome, and long-form sections. It is long but dense and mostly earns its space, though it could be tightened around repeated derivation details.

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?

This is a complex write tool with 15 parameters, no annotations, and no schema descriptions, so the description needs to carry substantial context. It covers core behavior, derivation rules, error handling, and promotion requirements, and an output schema exists; remaining gaps are secondary parameter semantics rather than missing core behavior.

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

Parameters4/5

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

Schema description coverage is 0% across 15 parameters, so the description must compensate. It explains most important parameters and derivation rules, including task_id, request_id, lesson, outcome, repo_area, evidence_ids, extra_evidence_ids, idempotency_key, and provenance, but it leaves some parameters such as root_cause_candidate and the promote_* booleans less explicitly defined.

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 and resource: persist and project one verified Learning Commit. It also clarifies the short-form usage and distinguishes it from the long-form compatibility path, so an agent can understand the operation without opening the schema.

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?

It explicitly says the short form is the one to use and long form is for compatibility, and it lists prerequisites for promotion to AI Memory, KB, and Context Graph. It does not explicitly compare this tool against sibling write tools, but the usage context is clear.

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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