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list_claims

Extract and list claims from a draft, showing the evidence or substantiation each category requires before a policy check. Use it as a first pass to verify your evidence folder matches the claims.

Instructions

List the claims in a draft and what each one needs, without judging them.

Useful as a first pass: it shows what the checker will demand substantiation for, so you can judge whether your evidence folder is even the right shape. Categories include statistic, percentage, superlative, comparative, guarantee, efficacy, temporal, attribution, absolute and causal.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
draftNo
draft_pathNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the full behavioral burden, and it does disclose the key trait that the tool does not judge or reject anything (a non-evaluating read). It also names the categories it will classify into, which helps interpret results. However, it says nothing about permissions, input size limits, or error behavior, leaving real gaps for an unannotated tool.

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?

Purpose is front-loaded in the first clause, and the follow-on sentences add genuine routing value. The trailing category enumeration is longer than strictly necessary but is informative rather than padding.

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

Completeness3/5

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

An output schema exists, so return values need not be described, and the non-mutating nature is clear. The remaining hole is the unresolved draft vs draft_path choice, which an agent must get right to call the tool at all, and which nothing in the description or schema addresses.

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

Parameters2/5

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

Schema description coverage is 0% and there are two parameters (draft, draft_path) offering two distinct input modes, yet the description never explains the difference or when to use one over the other. It only implies that 'a draft' is the input, so it does not compensate for the documentation gap.

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 ('List the claims in a draft and what each one needs') and immediately scopes it against the checking siblings with 'without judging them.' An agent can tell this is the inventory step rather than the evaluation step without opening any 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?

Explicitly positions the tool as 'a first pass' whose purpose is to reveal 'what the checker will demand substantiation for,' which gives clear situational context relative to check_draft and its variants. It stops short of naming the alternative tools explicitly or stating when not to use it, so it is strong but not complete routing guidance.

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