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

The description goes well beyond the annotations by disclosing the parallel fan-out to SEC EDGAR, GDELT→GNews fallback behavior, rate-limit/5xx handling, the USPTO PatentsView sunset issue, and that the tool 'soft-fails until reactivated.' It also describes the return shape with changes[], total_changes, and citation URIs, giving the agent accurate expectations for a read-only, idempotent call.

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 earns its place: query triggers, core capability, data sources, fallback logic, date format, return structure, and sibling routing. Important details are front-loaded and grouped coherently, with no filler or redundant restatement of the title.

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?

For a tool with no output schema, the description sufficiently explains inputs, date handling, source behavior, failure modes, and return structure. The explicit pointer to entity_profile covers the main alternative, so an agent has enough context to invoke recent_changes correctly without additional lookup.

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 coverage is 100%, so the schema already documents type, since, and value. The description adds a useful recommendation ('Use "30d" or "1m" for typical monitoring') and examples of ticker/CIK formats, but these are minor enrichments over schema definitions rather than substantial new meaning.

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 concrete natural-language triggers and then states the core purpose: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It specifies the resource (company change feed), the window behavior, and clearly differentiates this from the sibling entity_profile by noting the alternative for static profiles.

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 made explicit through query examples and a direct routing instruction: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also clarifies the fallback behavior across data sources, so an agent knows when this tool is appropriate and when it should select a sibling.

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