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

Beyond the annotations (read-only, idempotent, non-destructive), the description reveals rich behavioral details: it fans out in parallel to three APIs, handles GDELT→GNews fallback on rate limits, discloses USPTO PatentsView sunset and soft-fail behavior, and describes the output structure (changes[] grouped by source + total_changes + citation URIs). This gives the agent a clear picture of side effects, failure modes, and return shape.

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 clause contributes actionable information: examples, data sources, fallback logic, parameter syntax, return value, and a pointer to an alternative tool. It is front-loaded with user intent and structured logically, earning a top score for conciseness and structure.

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 (multiple APIs, fallback logic, date-range handling) and absence of an output schema, the description is remarkably complete. It explicitly states what is returned (structured changes[] grouped by source + total_changes + URI citations) and covers edge cases (rate limits, API sunset), so an agent can confidently decide to use it and understand its behavior.

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 input schema already covers 100% of parameters with descriptions (type enums, since format, value format). The description mostly restates the `since` format and adds a 'typical monitoring' recommendation, but it does not add significant new meaning beyond the schema. Therefore the baseline of 3 is appropriate; the schema does the heavy lifting.

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 natural-language examples that capture the tool's intent, then defines it precisely as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It lists concrete data sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly distinguishes from the sibling tool entity_profile, so purpose is unambiguous and well-differentiated.

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: 'Use entity_profile instead when you want the static profile... regardless of window.' It also recommends typical values for the `since` parameter ('Use 30d or 1m for typical monitoring') and explains fallback behavior for GDELT/GNews, so the agent knows exactly when and how to invoke it.

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