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commitlore_verify_capture

Verify a capture draft by checking citations against the transcript and diff, then store the validated result for the next stage.

Instructions

Verify a capture draft against the transcript and diff that were hashed at prepare time. Evidence citations are checked mechanically (verbatim match); fabricated quotes are discarded. Stores the verified result in the pending transaction for stage to consume.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
diffYesthe staged diff (same content hashed at prepare time)
draftYesThe agent's draft, as the harvest contract specifies it: a JSON object with a "records" array. A bare JSON array of records is also accepted.
nonceYesthe 32-character lowercase hex nonce returned by prepare_capture
transcriptYesthe session transcript (same content hashed at prepare time)

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4/5.0
Behavior4/5

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

Annotations already set readOnlyHint=false and destructiveHint=false. The description adds valuable behavioral context: it mechanically checks citations, discards fabricated quotes, and stores the verified result in a pending transaction. It does not contradict annotations, though it omits failure behavior.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two sentences, front-loaded with the core action, and every clause earns its place. The workflow context is succinctly conveyed without fluff.

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

Completeness2/5

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

There is no output schema, yet the description never states what the tool returns (e.g., success/failure, verified draft, or error codes). It also doesn't cover what happens when verification fails or if the nonce is invalid. For a verification step in a multi-tool pipeline, this is a notable gap.

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

Parameters3/5

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

Schema coverage is 100%, with each parameter already documented (e.g., nonce 'returned by prepare_capture', transcript and diff 'same content hashed at prepare time'). The description reinforces the hashed-at-prepare constraint but adds little beyond the schema, so baseline 3 is appropriate.

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 a specific verb (verify) and resource (capture draft) against the transcript and diff, and distinguishes itself from siblings like prepare_capture and stage_capture by its role in the pipeline. The mention of mechanical evidence checking and discarding fabricated quotes further clarifies its unique purpose.

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 implies the workflow: it verifies against content 'hashed at prepare time' and stores results 'for stage to consume', indicating it sits between prepare and stage. However, it does not explicitly state when NOT to use it or name alternative tools, leaving some inference required.

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