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Record a claim

record_claim
Destructive

Put one measured number the founder will stand behind in public into the claim library, so every later reply and growth marketing post can cite it with its caveat. WHEN: a real measurement lands and the founder says they would defend it. Record the NUMBER and the caveat together; a number without its caveat is what this library exists to prevent. Never record an estimate, a projection, a rival's figure, or anything you inferred rather than measured. Free; requires the 'act' scope. Recording the same claim twice replaces your earlier wording; a pinned claim is left as it is. WHAT COMES BACK: their top moves with move ids.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
heldNoTrue to record it and block its use.
textYesThe claim in one sentence.
caveatYesWhat it does not prove, against your own interest.
numberYesThe measurement, with its unit and comparison.
triggerYesKeywords saying when this claim is relevant.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.9/5.0
Behavior5/5

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

Adds substantial behavior beyond the annotations: cost ('Free'), required auth scope ('act'), and dedup/replacement semantics ('Recording the same claim twice replaces your earlier wording; a pinned claim is left as it is'), which directly explains the destructiveHint=true. It also states the return payload ('top moves with move ids').

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?

Four tight sentences using WHEN / Never / WHAT COMES BACK structure, front-loaded with the record action and its purpose. Each sentence carries distinct load — purpose, eligibility, exclusions, side effects, return — with no filler.

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 destructive-record tool with no output schema, it covers everything an agent needs: cost, scope, exclusions, replacement behavior, and the return shape. There is no output schema, and the description compensates by describing what comes back.

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 coverage is 100%, so the baseline is 3, but the description adds a real semantic constraint by insisting the NUMBER and its caveat be recorded together ('a number without its caveat is what this library exists to prevent'). It does not address the 'held' or 'trigger' parameters, so it stops short of a 5.

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?

Specific verb+resource ('Put one measured number ... into the claim library') with an explicit scope qualifier (one number, measured, founder-defensible). It clearly separates itself from read-side siblings like get_claims and from the other record_* tools by naming the claim library and the measurement requirement.

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?

Explicit WHEN clause (a real measurement lands and the founder would defend it in public) plus a hard when-not list: never estimates, projections, rival figures, or inferences. Nothing is left to inference about applicability.

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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