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

Annotations already declare readOnlyHint/idempotentHint/destructiveHint, so the description doesn't need to restate safety. It adds substantial behavioral context: multi-source fan-out (SEC EDGAR, GDELT→GNews, USPTO), rate-limit fallback, soft-fail for patents, and the return structure. This goes well beyond annotations, giving the agent a clear model of what happens when the tool runs.

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?

Despite being long, every sentence contributes: user intents, core function, source details, fallback logic, parameter syntax, return shape, and alternative. It's a single dense paragraph that front-loads the most important information (what it does) before getting into specifics. No filler or redundancy.

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?

The tool is complex (multi-source, fallback, flexible date parsing, citation URIs), and the description covers all these aspects without needing an output schema. It explains the return structure, source grouping, and limitations (patents soft-fail). It also positions the tool within the sibling set via the entity_profile contrast, making it fully contextualized.

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 baseline is 3. The description adds extra value by clarifying `since` formats (ISO vs relative shorthand) with examples and a recommended usage ('Use "30d" or "1m" for typical monitoring'). This goes beyond the schema's basic description, though it doesn't add much for `type` and `value` since those are already well-described.

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 clearly defines the tool: 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It also explicitly contrasts with sibling tool entity_profile, which targets static profiles. This is a specific verb+resource+scope definition that distinguishes it from alternatives.

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 when-to-use triggers (queries about recent changes, news, updates) and a direct when-not-to-use recommendation: 'Use entity_profile instead when you want the static profile... regardless of window.' Also describes fallback behavior (GDELT→GNews, PatentsView sunset) which helps an agent decide if this tool fits the request.

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