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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.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "claims": "<claims>"
    },
    "name": "check_memory"
  }
}

Input 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. Changed1 schema field changed
    • addedInput schema / properties / claims / items / properties / prior
      Added value: +{
      +  "description": "Optional. An earlier check of this claim: its inspected.sha256 and checker. The answer then says since: unchanged (same text, same rules), source-changed (the text read is not the text read then) or checker-changed (same text, new matching rules).",
      +  "properties": {
      +    "checker": {
      +      "description": "checker from the earlier check.",
      +      "type": "string"
      +    },
      +    "sha256": {
      +      "description": "inspected.sha256 from the earlier check: 64 hex characters.",
      +      "type": "string"
      +    }
      +  },
      +  "required": [
      +    "sha256",
      +    "checker"
      +  ],
      +  "type": "object"
      +}
  2. Added

TDQS

A4.3/5.0
Behavior5/5

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

Annotations already declare readOnly/idempotent/openWorld, but the description adds substantial behavioral context on top: the full verdict vocabulary, that NOT FOUND is bounded to the representation read and is never a claim the words were not said, that audio-only sources return CAN'T ESTABLISH rather than being transcribed, and that no model is consulted. It also warns that the summary is deliberately not a score or trust rating — exactly the kind of misuse guard an agent needs.

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 purpose and scale, then conditions, then caveats — a sensible ordering. It is a single dense paragraph and some of the return-field enumeration ('the passage, which representation was read and how much of it, and when') is wordier than needed, but nearly every sentence carries an operational constraint.

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

Completeness4/5

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

With no output schema, the description carries the return-value burden and does so well: verdicts, per-claim fields, and summary contents are all described. The remaining gap is the undocumented `prior` round-trip, which an agent would only discover by opening the schema, plus no mention of failure handling for invalid or unreachable sources beyond the audio case.

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%, so the nested claims/statement/source/id/prior structure is already documented in detail; the description adds only the batching scale and the one-source-per-claim pairing. Notably it never mentions the optional `prior` parameter, whose presence changes the response by adding a `since: unchanged/source-changed/checker-changed` field — a meaningful omission given how non-obvious that mechanism is.

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?

Opens with a precise verb+resource framing ('check_quote over a batch') that both states what it does and anchors it to the sibling single-claim tool. The scale (up to 10 claims) and the per-claim source mapping make the unit of work unambiguous.

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, and 'check_quote over a batch' implicitly routes single-claim checks to check_quote. It stops short of stating an explicit when-not (e.g. use check_quote for one claim, or when no source URL exists), leaving that to inference.

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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