Citation Verification

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

Despite no annotations, the description discloses the three verification actions, the optional claim parameter yielding a match_score, and the batch limit. It implies a read-only operation without explicitly stating it.

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?

Two efficient sentences followed by a concise list of checks. Every clause adds value, no redundancy, and the critical purpose is immediately clear.

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?

Lacks details on the full return format (e.g., whether it returns per-item status, error handling, or response fields beyond match_score). For a tool with no output schema, this leaves agents guessing about the response structure.

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 description adds meaningful context beyond the schema: explains that DOIs are accepted, that claim elicits a match_score, and caps at 25. Schema descriptions already cover param roles, so this is additive.

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 clearly states the tool verifies cited URLs against three criteria (resolve, match claim, not retracted). This specific verb-resource combination distinguishes it from siblings fact_check and source_freshness.

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?

Provides a concrete use case ('when an LLM produced inline citations') and input format guidance (DOIs or URLs, up to 25). Does not explicitly mention when not to use, but the context is clear.

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?

The description discloses key behaviors: it returns a verdict, confidence score, and list of sources with excerpts. It also mentions that longer claims are truncated to 200 characters. Since no annotations are provided, the description adequately covers the behavioral traits, though it could mention any rate limits or authentication requirements if applicable.

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?

The description is concise, consisting of three front-loaded sentences. Each sentence adds unique value: purpose, return values, and usage guidance. There is no redundancy or wasted words.

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 simple tool with one required parameter and no output schema, the description is complete. It explains the tool's function, return values, and when to use it. It could briefly mention error handling or edge cases for completeness, but it is largely adequate.

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 input schema covers the single parameter 'claim' with a clear description. The tool description does not add additional semantic context beyond what the schema already provides. With 100% schema coverage, a baseline score of 3 is appropriate.

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 clearly states the tool's purpose: verify a factual claim against authoritative sources (Wikipedia, Wikidata, Crossref). It lists the verdict types and sources, making the purpose specific. However, it does not explicitly differentiate from sibling tools like cite_check or source_freshness, which could clarify when this tool is the best choice.

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 provides explicit guidance on when to use the tool: when an agent is unsure about a fact or when post-processing LLM output to detect hallucinated claims. This is clear and actionable. It does not specify when not to use it or mention alternatives, but the guidance is still strong.

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?

With no annotations, the description carries full burden. It discloses return values (last-modified, Wayback snapshot, flag) but omits potential side effects like network dependencies, rate limits, or error handling. Adequate but could be more detailed.

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 sentences, each purposeful: first states purpose and returns, second details returns, third gives use case. No unnecessary words. Front-loaded with the verb.

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?

Given a single parameter, no output schema, and no annotations, the description covers purpose, expected returns, and a concrete use case. Slightly lacking on potential failure modes or prerequisites, but still fairly complete.

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%, so baseline is 3. The description adds context about what is checked (live, modified) but does not add new meaning about the 'url' parameter beyond the schema's 'URL to check'.

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 uses a specific verb 'Check' and resource 'source URL', and clearly distinguishes from siblings (cite_check, fact_check) by focusing on liveness and modification. It also lists the exact return fields.

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 explicitly states when to use: 'for RAG hygiene — flagging citations...', providing a clear context. It does not explicitly exclude cases, but the specificity is sufficient.

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