MERCATOR Verify

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

With no annotations, the description carries the full transparency burden and handles it well. It discloses that unevidenced fields return UNKNOWN rather than a guess, that explicit conflicts are surfaced, that sources and freshness are included, and it even states the billing/free condition. This gives the agent accurate expectations beyond the schema.

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 two dense sentences with no filler. The core value proposition is front-loaded, and the second sentence adds important edge-case and pricing behavior. Every clause earns its place.

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 4 parameters, no output schema, and no annotations, the description is underspecified for correct invocation. It does not clarify whether name/domain is required or how they interact, what fields should contain, or how max_age_days affects results. The 'identifier you supplied' line also conflicts with the schema's optional identifier fields.

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 0%, so the description must compensate for the 4 parameters. It never maps fields, name, domain, or max_age_days to concrete usage; 'field-level sources' and 'freshness' only weakly hint at fields and max_age_days. The agent cannot infer identifier requirements or allowed field values from the text.

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 identifies the tool as providing evidence-backed company facts and current company information for enrichment/verification, with concrete output characteristics like field-level sources, freshness, and conflicts. It lacks an explicit verb like 'retrieve' and does not directly name sibling entity_evidence_validate, but it is specific and not a tautology.

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 frames when to use it: 'for company enrichment and company verification.' This gives clear context for selection. However, it does not state exclusions or compare itself to entity_evidence_validate, so it misses the top band.

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 provided, the description carries the full burden and does disclose key behavioral traits: it is free, performs no retrieval, and incurs no cost. This gives an agent a reasonable safety profile. It could further explain what 'guarantees' means or any side effects, but the core behavior is transparent.

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 sentences with no filler. The key differentiators ('FREE', 'preflight', 'Retrieves nothing and costs nothing') are front-loaded, and every clause adds useful 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?

While an output schema exists, the description still lacks essential context: parameter semantics, definition of 'guarantees', and explicit guidance on how this relates to the sibling call. For a non-trivial 4-parameter tool with no annotations and 0% schema coverage, this is incomplete.

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 0%, and the description mentions no parameter names or meanings. The agent is left to guess what 'fields', 'name', 'domain', and 'max_age_days' represent or how they affect the validation. Given the low coverage, the description must compensate and fails to do so.

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-plus-resource phrase ('FREE preflight for entity_evidence') and clarifies the action: check a request and see price and guarantees before paying. It also explicitly differentiates from the sibling tool by stating 'Retrieves nothing and costs nothing.'

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 positions this as a preflight step to run 'before paying,' which clearly implies using it prior to the paid sibling entity_evidence. It stops short of explicitly stating when to use entity_evidence instead, so the guidance is clear but not fully exclusionary.

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