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

Beyond the annotations (readOnly, openWorld, idempotent), the description discloses detailed runtime behavior: it fans out to multiple sources in parallel, uses a specific fallback chain, handles a known API sunset, and returns structured grouped changes with citations. No contradiction with annotations; it adds substantial behavioral context.

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 efficiently organized: user-intent phrases, core capability, source breakdown, parameter examples, return shape, and alternative tool. Every sentence earns its place without repetition. It front-loads the most important information and uses compact punctuation to keep related details together.

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 multi-source complexity and lack of an output schema, the description covers the essential context: what sources are queried, how failures are handled, how `since` formats work, what the return structure includes (changes[], total_changes, citation URIs), and when to prefer a sibling. This is complete for an agent to select and invoke the tool correctly.

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 already documents all three parameters (100% coverage), so the baseline is 3. The description adds value by giving concrete format examples for `since` (ISO vs relative shorthand), a recommendation ('Use "30d" or "1m" for typical monitoring'), and clarifying acceptable `value` forms (ticker or zero-padded CIK). This surpasses baseline.

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 user intents ('What's new with X' / 'latest on Y') and then defines the tool as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It clearly names the resources involved (SEC EDGAR, GDELT/GNews, USPTO) and distinguishes itself from the sibling tool entity_profile, 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?

It provides explicit when-to-use guidance through natural-language query examples and directly names an alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' It also explains fallback behavior (GDELT→GNews on rate limits/5xx) and notes the USPTO soft-fail, giving agents clear decision criteria.

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