Nameidea

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 new behavior: the scoping of failure (not_found is confined to a 3,000-record subset) and the data provenance of the ID. No auth or rate-limit detail, so not a 5.

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 compact sentences with the core purpose front-loaded and the failure-scope caveat trailing. The embedded list of returned fields (Census rank, count, source, collection pages) mildly duplicates the output schema rather than earning its place, which keeps it from a 5.

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 an output schema present, the description need not explain return values, and annotations cover the safety profile; the description correctly fills in provenance and failure semantics instead of repeating them. Remaining gaps (e.g., why the subset is limited to 3,000 records) are minor for a single-parameter read tool.

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% and the single 'id' parameter is already documented with format, pattern, and case-insensitivity in the schema. The description only reaffirms that the ID is stable and originates from search_surnames, which is marginal added meaning. 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 (look up) and resource (stable surname ID), and explicitly ties provenance to the sibling tool search_surnames, so an agent can separate this detail-lookup from the search tool. It also names the payload it returns (Census rank, count, source, collection pages), making the tool's role unambiguous.

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 phrase 'returned by search_surnames' makes the workflow explicit: call search first, then use this tool with the resulting ID. The caveat that not_found applies only to the 3,000-record subset gives further usage context. It stops short of an explicit when-not statement, so 4 rather than 5.

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 cover the safety profile (readOnly, idempotent, non-destructive, closed world), so the bar is lower. The description adds real operational context beyond them: the dataset size (3,000 records), result ordering (rank-ordered), pagination, and explicit scope exclusions (no meanings, no availability checks).

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 dense sentences, front-loaded with purpose and results, then the scope limitation. No filler or repetition.

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?

The presence of an output schema means return values need not be explained, and the annotations cover safety. But for a 10-parameter tool with only 10% schema coverage, the description leaves the bulk of parameter meaning unaddressed, which is a meaningful completeness 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?

Ten parameters at 10% schema description coverage means the description must carry the semantics, but it only alludes to pagination and rank ordering generally. It never distinguishes query from prefix, explains the scope enum values, or clarifies minRank/maxRank/initial/minLength/maxLength, so most parameters are undocumented everywhere.

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 (filter) and resource (2010 U.S. Census surname records and curated collections), plus a result profile (rank-ordered, paginated). It does not differentiate itself from the sibling get_surname_record, so it falls short of a 5.

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 meanings or availability checks" bounds what this tool is for, implying it is a lookup/filter rather than a semantic or availability service. However, it never says when to choose this over get_surname_record, leaving the routing inference to the agent.

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