GroundedKit Marketing Stats Checker

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

Annotations already cover safety (readOnly, idempotent, non-destructive, closed-world), so the bar is lower, and the description still adds real behavior: the full set of returned fields, the as_of/last_checked metadata, close alternatives, and critically the failure mode 'match: none when the stat is not in the database'.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with how to supply input, then the return payload. The second sentence is a long enumerated run-on, but since there is no output schema, that enumeration is doing necessary work rather than padding.

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

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

With one parameter, no output schema and annotations carrying safety, the description must carry the return contract, which it does comprehensively (verdict, confidence, source quote, rephrasing, URL, kind, dates, alternatives, no-match case). The only gap is any indication of when to prefer this over its sibling lookup tools.

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

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

Schema coverage is 100% for the single 'text' parameter, so the baseline is 3. The description goes beyond the schema by clarifying that input may be a full paragraph or a phrased question rather than only a literal statistic, which meaningfully widens how the agent should populate the parameter.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a concrete verb+resource: checking a marketing/influencer statistic and returning a matched checked claim with verdict and source. It is unambiguous what the tool does, but it never differentiates itself from siblings get_claim, search_claims or list_verdicts, so an agent must open the schemas to route correctly.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by describing two input modes (paste a sentence/paragraph, or ask a reference-number question like 'what share of US adults use TikTok'), which is useful context. It gives no when-not-to-use and names no alternative, so the agent is left to infer that search_claims or get_claim are the fallbacks.

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

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

Annotations already declare readOnly, idempotent, non-destructive and closed-world, so the safety profile is covered. The description adds genuinely useful behavioral context beyond that: it tells the agent the tool returns a full record and enumerates the payload contents (verdict, confidence, origin, quote, method/sample, drift, sources), which matters because no output schema exists.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

One sentence, front-loaded with the action and identifier, followed by the return contents. The field enumeration is dense but each item conveys real information, and there is no filler.

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

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 discharges it well by listing the record's fields. Minor gaps remain: behavior on an unknown id, and whether only groundedkit.com URLs are accepted.

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

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

Schema description coverage is 100% and the single id parameter is already documented as accepting a slug or groundedkit.com URL, so the description's mention of 'id (slug) or claim page URL' largely restates the schema. Baseline 3 applies when the schema does the heavy lifting.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Full record for one checked claim') plus the two accepted identifiers (slug or claim page URL), which cleanly separates it from the plural search_claims and list_verdicts siblings. It stops short of naming those siblings, so the differentiation is implied rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied: look up a single claim when you already have its id or URL. There is no explicit statement of when to prefer this over search_claims or list_verdicts, and no prerequisites or exclusion conditions are given.

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

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

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false, and openWorldHint=false, so the safety profile is fully covered. The description adds useful context about what is returned (definitions, per-verdict counts, confidence levels, dataset info), but does not clarify that the tool takes no arguments or that the result is static reference data.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

A single dense sentence that front-loads the four verdicts and then layers in the supplementary content. Every clause (definitions, counts, confidence levels, dataset info) contributes distinct information; the parenthetical enumeration is efficient rather than padded.

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

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

With no input parameters, no output schema, and annotations covering the safety profile, the description carries the burden of explaining return content and does so adequately. A brief note that the call is argument-free and returns a fixed reference set would close the remaining gap.

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

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

The tool takes zero parameters, so the baseline of 4 applies. The description correctly implies a no-argument, catalog-style call and does not misrepresent any parameter surface.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description enumerates exactly what the tool provides: the four named verdict types, their definitions, claim counts per verdict, plus confidence levels and dataset info. That is specific enough to distinguish it from siblings like get_claim or search_claims, though it never states an explicit verb (e.g., 'returns' or 'lists') and leans on the title.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to call this versus check_stat, get_claim, or search_claims, and no stated conditions or prerequisites. The reference-lookup nature is only implied by the word 'List' and the enumerated static content.

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

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

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so safety is covered. The description adds meaningful context beyond that: results are short summaries rather than full records, and the full record requires get_claim. Pagination limits are left to the schema, but the return-shape disclosure is the valuable addition.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three compact sentences with zero filler. Search scope comes first, the optional filter second, and the hand-off to get_claim last, which matches the order an agent needs the information.

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

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

For a three-parameter read-only search tool with no output schema, the description covers what is searched, how results can be narrowed, and what the response looks like (short summaries). Combined with annotations and a fully documented schema, nothing needed to invoke it correctly is missing.

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

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

Schema description coverage is 100%, so the schema already documents query, limit, and verdict, including the enum values and the 'empty string lists everything' behavior. The description's mention of the verdict filter and searchable fields largely restates the schema, so baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (keyword search) and resource (checked marketing statistics) and names the exact searchable fields (claim text, common phrasing, origin publisher). It also distinguishes itself from get_claim, so an agent can tell the two apart 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.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives clear context: search by keyword, optionally narrow with a verdict filter, and use get_claim when the full record for an id is needed. It stops short of stating when-not to use it (e.g. when to prefer list_verdicts or a direct lookup), so it is clear but not exhaustive.

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