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

The description discloses nuanced behavior beyond the annotations: source precedence (GDELT preferred, GNews fallback on rate-limit/5xx), the USPTO PatentsView sunset causing soft-fail, and the output shape (changes[] grouped by source, total_changes, pipeworx URIs). This is substantial operational detail.

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: example phrasings, source breakdown, failure modes, date syntax, output structure, and cross-reference. It is front-loaded with user-oriented query patterns and remains highly readable despite its length.

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 datasources, fallback logic, flexible date parsing, structured output) and lack of an output schema, the description covers all necessary context: input semantics, expected return value, and failure/limitation behavior. No critical gaps remain.

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?

While the schema already covers all parameters, the description adds critical semantics: `since` accepts ISO dates or relative shorthand with examples, and recommends '30d' or '1m' for typical monitoring. It also clarifies `value` formats (ticker or zero-padded CIK). This exceeds schema information.

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 identifies the tool as a 'change feed for a company in the last N days/weeks/months' with concrete example queries. It enumerates the fan-out sources (SEC EDGAR, GDELT→GNews, USPTO) and distinguishes itself from entity_profile, making its scope 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?

Provides explicit guidance on when to use this tool ('What's new with X' / 'latest on Y' etc.) and when to choose an alternative ('Use entity_profile instead when you want the static profile'). This directly addresses the decision boundary.

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