query_dataset

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

With no annotations, the description carries the full burden of behavioral disclosure — and it delivers. It reveals the API-key auth requirement, the upstream 100-row hard cap, the paged-past-the-end signature (zero rows with positive total), and the case-sensitivity pitfall for filter fields. This is exactly the kind of non-obvious behavior an agent needs before calling.

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 structure is effective: a crisp opening sentence, then operational details, a highlighted IMPORTANT warning, and a per-parameter Args block. There is minor redundancy — the 100-row cap and the 1-100 limit are stated in both the prose and the Args — and the description runs long because it is dense with value; every sentence earns its place, but a tighter consolidation would be possible.

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 5-parameter tool with 0% schema coverage and no annotations, the description covers everything an agent needs to call it correctly: auth requirements, pagination pitfalls, filter capitalization traps, and full parameter semantics. The output schema exists, so not explaining return values is acceptable. The description is complete enough that an agent could perform correct data pulls on the first generic run.

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 fully compensate — and it does. The Args block explains what the schema cannot: where resource_id comes from (search_datasets or portal), the exact shape of filters with a concrete example ({'city': 'Delhi'}), the semantic meaning of fields as a projection, and the effective 1-100 range for limit, given that pagination semantics only exist in the description.

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: 'Pull actual rows from a data.gov.in dataset, optionally filtered.' This clearly differentiates it from siblings: search_datasets (discovery), list_sectors (catalog navigation), and get_dataset_info (metadata), by explicitly positioning it as the tool that retrieves the live data rows themselves.

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 gives clear contextual usage guidance: it explains pagination semantics, when a zero-row result means you have paged past the end, and the dependency flow for filters ('must match a field id from get_dataset_info exactly'). It does not explicitly state 'use X instead when...' selection criteria, so exclusions between siblings are only implied, not spelled out, so a 4 is appropriate.

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