datasets_sec_companies_facets

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

No annotations are present, so the description carries full burden. It discloses the return type (terms-aggregation counts), the scoping to search filters, and details the revenue_band buckets. This adds value beyond the schema by explaining behavior and the revenue_band breakdown.

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 extremely concise: two sentences that front-load the purpose and then provide essential details (facet enum and revenue_band buckets). No redundant information; every word is purposeful.

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 description covers the main behavioral aspect (facet aggregation) and scoping to filters. It lists all facet options. However, it does not elaborate on how the filter parameters (q, sic, etc.) affect the results, though the schema covers them. Given the tool's simplicity and the absence of an output schema, this is reasonably complete.

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 baseline is 3. The description lists the facet enum values (already in schema) and explains revenue_band buckets (not in schema). This adds marginal value but does not significantly enhance parameter understanding beyond what the schema already provides.

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: to return terms-aggregation counts for a single facet of the SEC companies dataset. It lists the facet enum values and explains the revenue_band buckets, making it distinct from sibling tools like search or item.

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?

Usage is implied by context: the tool is for faceting, not full search. It mentions 'scoped to the same filters as search' but does not explicitly state when to use this vs. other tools like search or financials. No direct alternatives or when-not-to-use guidance is provided.

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