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

Beyond the annotations (readOnlyHint, idempotentHint, destructiveHint), the description discloses significant behavioral traits: it fans out to SEC EDGAR, GDELT/GNews, and USPTO in parallel; details the fallback order and failure mode (PatentsView API sunset soft-fails); and notes the source grouping in results. This adds substantial transparency beyond what annotations provide.

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 dense but every sentence adds value. It opens with relatable query examples, then concisely explains data sources, parameter formats, return structure, and the alternative tool. The use of punctuation (dashes, parentheses) packages information efficiently without being verbose.

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 (fans out to multiple sources, no output schema), the description adequately explains the high-level return structure: 'structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'. However, it does not detail the shape of individual change objects, which would be useful given the lack of an output schema. Still, the description covers the core behavior and limitations.

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?

The schema covers all parameters 100%, but the description adds practical detail: 'since' accepts ISO date or relative shorthand with examples ('7d', '30d', '3m', '1y'), 'value' can be ticker or CIK, and 'type' is restricted to 'company'. This enhances usability over raw schema descriptions.

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 defines the tool as a change feed for a company over a time window, using specific verbs like 'What's new' and 'latest on'. It explicitly distinguishes itself from the sibling tool entity_profile by stating when to use that instead, making the purpose unambiguous.

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 usage context with example queries ('What's new with X', 'updates on Acme') and directly names the alternative tool: 'Use entity_profile instead when you want the static profile'. It also explains the internal fallback logic (GDELT preferred, GNews when rate-limited), which guides appropriate usage.

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