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query_claims

Read-only

Audit the analyst's claim ledger: check evidence class, source backing, and restatement chains. Identify weakened assertions before acting.

Instructions

Read the LEDGER of claims this key has been given — every factual assertion the Analyst made, with its evidence class (CONFIRMED / REPORTED / PARTY_CLAIM / ASSESSMENT), how many INDEPENDENT source families backed it, and the publishers. The point is the time axis: when a later answer restated the same assertion, the claim carries the link and says which way the evidence moved — restated_only surfaces those chains, and downgraded_only isolates the cases where a later answer was LESS sure than an earlier one, which is where this product contradicted itself. Use it to audit what you were told before acting on it, or to check whether an assertion has since weakened. Scoped to your own key; no other caller’s claims are visible. Free of token charges.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoHow many claims to return (default 50).
sinceNoOnly claims asserted on or after this date (YYYY-MM-DD or ISO 8601).
restated_onlyNoOnly claims that sit in a restatement chain.
evidence_classNoRestrict to one evidence class.
downgraded_onlyNoOnly claims a later answer restated with WEAKER evidence — read these first.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
claimsYes
countsNo
summaryNoOne-line natural-language summary of the result, ready to relay to a user.
Behavior5/5

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

Beyond the readOnlyHint and non-destructive annotations, the description discloses important behavioral traits: the time-axis/evidence-movement semantics, the fact that claims track restatements and downgrades, scoping to the caller's own key, and that it is 'Free of token charges.' This adds significant context beyond the annotations.

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?

The description is long but every sentence contributes value, explaining the ledger concept, evidence classes, restatement chains, use cases, and scope. It is front-loaded with the main purpose. The length is justified by the tool's conceptual complexity, though it could be tightened.

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?

Despite lacking an output schema in the provided definition, the description thoroughly covers what the tool returns (evidence class, source family count, publishers) and its unique value proposition (time-axis tracking, downgrade detection). It leaves no major ambiguity about the tool's behavior and purpose.

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 descriptions cover 100% of parameters, so baseline is 3. The description adds extra meaning for restated_only and downgraded_only by explaining their purpose in identifying chains and weakened assertions, and it clarifies evidence classes with examples. This goes beyond the schema's straightforward definitions.

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 verb 'Read' plus the resource 'LEDGER of claims' clearly states the tool's function. It specifies the content (factual assertions, evidence class, source families, publishers) and explicitly distinguishes itself from sibling tools by focusing on the claims ledger and its time-based evidence chains.

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?

Provides explicit use cases: 'Use it to audit what you were told before acting on it, or to check whether an assertion has since weakened.' It also notes scope restrictions (only your own key) and the meaning of restated_only and downgraded_only. However, it does not explicitly contrast with alternatives like query_signals or query_stats.

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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