dol_get_dataset

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

Annotations only declare readOnlyHint=true and openWorldHint=true, so the description carries the behavioral burden — and it delivers richly: full error taxonomy (401/403→invalid_input with key guidance, 429→rate_limited THROWS honoring Retry-After, 5xx→upstream_unavailable, 200 non-JSON→schema_drift), the verbatim-data honesty policy (genuine 0 stays 0, missing stays null), and the guarantee that `returned` is never passed off as `totalAvailable`. It even specifies key transport (X-API-KEY header only, never URL/_meta).

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 long, but it is front-loaded (purpose first, then the critical auth requirement) and every sentence carries operational information — error mapping, pagination semantics, honesty guarantees — rather than filler. The repeated 'honest/honesty' emphasis is stylistically redundant across several sentences, which costs it the top score, but nothing here is wasted.

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 fully specifies the return contract: { records: […] verbatim rows }, honest _meta with the totalAvailable-null-if-unknown rule, and hasMore semantics for paging forward. All failure modes, the auth requirement, and the keyless alternative are covered. Given the tool's complexity (7 params, external API, auth, multiple error classes), nothing an agent needs to call it correctly is left to guesswork.

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 the input schema already documents all 7 parameters, including the paired filterField/filterValue constraint and the regex validation. The description adds marginal value beyond this — mainly that parameters 'ride the PATH' and that inputs derive from dol_list_datasets fields — but most of its parameter text mirrors what the schema already states, so the baseline 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 opens with a specific verb+resource pair — 'Fetch records from ONE US DOL dataset' — and pinpoints the endpoint shape (/v4/get/{agency}/{endpoint}/json). It also names the keyless sibling dol_list_datasets inside the description, so an agent can immediately tell this is the data-fetching tool as opposed to the dataset-discovery one.

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 key precondition is stated explicitly and without ambiguity: 'REQUIRES a free DOL_API_KEY... the DOL DATA endpoint has NO keyless tier,' with a registration URL and the note that 'dol_list_datasets stays keyless.' It also tells the agent exactly where its inputs come from (agencyAbbr and apiUrl from dol_list_datasets), giving clear when-to-use and dependency guidance.

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