datasets_sec_institutional_positions_facets

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

No annotations are provided, so the description carries the burden. It discloses the return type (terms-aggregation counts) and the one-facet constraint, but doesn't provide details on response format, pagination, permissions, or edge cases. Some useful context is provided, but it's not comprehensive.

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 sentences with no fluff, but the dataset name is repeated across the first two sentences. Otherwise, it's well-structured and front-loaded with the action.

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 simplicity, the description covers the core purpose, the facet enum, and the scoping relationship to search. It could mention the return structure more explicitly, but it's largely complete for an aggregation tool with four parameters documented by the schema.

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 100%, so each parameter is already documented. The description adds no new parameter-level information beyond what the schema provides, so it earns the baseline score.

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 it facets the SEC institutional positions dataset and returns terms-aggregation counts. It mentions the facet enum and scoping to search filters, which distinguishes it from the related search tool, though it doesn't explicitly name the sibling.

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 'scoped to the same filters as search' implies when to use this tool for faceted counts, but it doesn't explicitly state when to choose this over the search tool or other alternatives. Usage context is implied rather than explicit.

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