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commit_assessment

Stores the confirmed outcome of an audit_knowledge_status dialogue as a linked entry, recording what the audit found and what the review concluded.

Instructions

Store the confirmed outcome of an audit_knowledge_status() dialogue.

Call this after the dialogue concludes and the user has reviewed the
assessment. Only the assessment — what the audit found and what came
of the dialogue, not a re-listing of every open question — is stored
as the entry's text.

Never changes the audited REV page's own claimed status — that stays
physical-authority, exactly like commit_distillation never rewrites a
SYN page. Stores with source='ai_extract' (excluded from journal_health
KPIs) using the AIEX sequence, and links to the REV page with an
asserted 'assesses' edge.

Args:
    assessment_json: JSON string with fields:
      rev_template_id (required) — the REV page this assesses.
      assessment       (required) — 2-5 sentences: what the audit found
                       and what came of the dialogue. This is the artifact.
      reaffirmed, revised — lists of topic strings (both optional,
                       default empty).
      tags             — optional list of "#topic"/"@source"/etc strings.
      date             — optional ISO date; defaults to today.

Returns a confirmation with the assigned AIEX ID and the assesses edge.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
assessment_jsonYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv3.9.0

TDQS

A4.7/5.0
Behavior5/5

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

With no annotations, the description carries the full burden and does so well: it discloses that it never changes the REV page's claimed status, stores with source='ai_extract' (excluded from journal_health KPIs), uses the AIEX sequence, and links via an asserted 'assesses' edge. It also notes the return shape.

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?

Front-loaded with the core action and the behavioral constraints next, then the argument documentation. Every sentence earns its place, though the description is on the long side for a single-parameter tool.

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?

An output schema exists, so return-value explanation is not required, and the description nonetheless summarizes the confirmation. Combined with the rich behavioral and parameter detail, an agent has everything needed to invoke this 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 coverage is 0%, so the description must compensate, and it does: it fully documents the assessment_json fields including which are required (rev_template_id, assessment), the semantics of each optional field (reaffirmed, revised, tags, date), and defaults. This adds substantial meaning beyond the bare schema.

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?

States a specific verb and resource: storing the confirmed outcome of an audit_knowledge_status() dialogue. It distinguishes itself from siblings by referencing audit_knowledge_status (the upstream) and commit_distillation (a contrasting sibling that never rewrites a SYN page).

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?

Explicitly states the trigger condition: call after the dialogue concludes and the user has reviewed the assessment. It names the related upstream tool, giving strong context. It does not state explicit when-not-to-use conditions, so it falls just short of a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.