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Check remembered claims against their sources

check_memory
Read-onlyIdempotent

check_quote over a batch, for auditing what an agent wrote down earlier. Give it up to 10 remembered claims, each with the source it is attributed to, and it returns the same verdict and receipt for every one: FOUND, CLOSE MATCH, DISTILLED MATCH, NOT FOUND or CAN'T ESTABLISH, with where, the speaker and the class of that claim, the passage, which representation was read and how much of it, and when. Reach for it when a note, a memory store or a shared workspace has accumulated claims nobody re-checked, and you want the ones no source supports. The summary counts verdicts and lists the claims that came back NOT FOUND; it is deliberately not a score, a percentage or a trust rating, because a NOT FOUND is bounded to the representation Yank read and is never a claim that the words were not said. A claim whose source is audio Yank would have to transcribe returns CAN'T ESTABLISH rather than being paid for. No model is consulted anywhere in this: every verdict resolves against the representation itself, and none of them is a judgement on whether a claim is true.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimsYesThe remembered claims to check, up to 10 per call. Each one is checked against its own source.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A4.4/5.0
Behavior5/5

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

Annotations already cover read-only/idempotent/open-world safety, and the description adds substantially more: the full verdict taxonomy, what the summary counts and lists, the deliberate refusal to produce a score or trust rating, the CAN'T ESTABLISH rule for audio sources, and the guarantee that no model is consulted. It also bounds the meaning of NOT FOUND to the representation read, which is exactly the kind of caveat an agent needs before acting on a negative result.

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 identity of the tool and the batching rule, and most sentences carry unique information (verdict list, summary shape, audio caveat). The closing pair of clauses about 'not a score' and 'not a judgement on whether a claim is true' restate the same caveat and could be merged, costing it the top mark.

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?

There is no output schema, so the description carries the full burden of describing return values — and it does, enumerating every verdict plus the receipt fields (where, speaker, class, passage, representation read, timestamp) and the summary's contents. Nothing an agent needs in order to interpret the response is missing.

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% and the single nested param is fully documented, so baseline 3 applies. The description restates the 10-claim cap and the statement/source pairing but adds no format or syntax detail beyond what the schema already says.

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+resource ('check_quote over a batch') and names the exact scope: auditing remembered claims against attributed sources. It directly distinguishes itself from the sibling check_quote by framing itself as the batch form, so an agent can route single vs. bulk checks without opening either 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?

'Reach for it when a note, a memory store or a shared workspace has accumulated claims nobody re-checked, and you want the ones no source supports' gives a concrete triggering situation. The batch framing implies check_quote is the single-claim alternative, but the description never states that exclusion explicitly, so routing is inferred rather than spelled out.

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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