query_resource_data

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: 'Each call retrieves up to 200 rows (the maximum allowed by the API)', 'The Tabular API has size limits (CSV > 100 MB, XLSX > 12.5 MB are not supported)', and pagination behavior with the page parameter. It doesn't mention rate limits or authentication needs, but covers most critical operational constraints.

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 appropriately sized and well-structured, with clear sections (overview, limitations, workflow, args, returns). Every sentence adds value, though the workflow section is somewhat detailed. It's front-loaded with the core purpose, but could be slightly more concise in the workflow explanation.

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 the tool's complexity (querying tabular data with pagination), no annotations, 0% schema coverage, but with an output schema present, the description is remarkably complete. It covers purpose, limitations, workflow with siblings, parameter semantics, and behavioral constraints. The output schema handles return values, so the description appropriately focuses on usage context.

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. It provides clear semantics for all three parameters: 'question: The question or description of what data you're looking for (for context)', 'resource_id: Resource ID (use list_dataset_resources to find resource IDs)', and 'page: Page number to retrieve (default: 1)... Each page contains up to 200 rows.' This adds substantial meaning beyond the bare schema.

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: 'Query data from a specific resource (file) via the Tabular API' and 'fetches rows from a specific resource using this API.' It specifies the verb ('query', 'fetch'), resource ('resource', 'tabular files'), and distinguishes from siblings by mentioning the Tabular API and contrasting with download_and_parse_resource for larger files.

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 this tool versus alternatives: 'For larger files or unsupported formats, use download_and_parse_resource' and 'You can use get_resource_info to check if a resource is available via Tabular API.' It also includes a detailed recommended workflow with sibling tools, clearly outlining the context and prerequisites.

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