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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description adds rich behavioral context beyond those hints: parallel fan-out to SEC EDGAR, GDELT/News fallback on rate limits or 5xx, USPTO soft-fail until reactivated, and the structured return shape with citation URIs.

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?

Though dense, the description earns its length for a multi-source, multi-parameter tool. It is front-loaded with natural-language query examples, then explains sources, parameters, and output in a compact flow. No filler or redundant repetition.

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?

With no output schema, the description compensates by naming the output fields: changes[], total_changes, and citation URIs. It also covers all relevant sources, fallback logic, and soft-fail behavior, making it complete enough for invocation and result interpretation.

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 covers 100% of parameters, so baseline is 3. The description adds practical guidance on 'since' formats ('7d', '30d', '3m', '1y') and even recommends '30d' or '1m' for typical monitoring, which goes beyond the schema text.

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?

Description opens with concrete user-intent examples and states 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It explicitly contrasts with entity_profile, which is for static profiles, making the purpose and scope clear.

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?

Gives explicit when-to-use context with sample phrasings and clearly names the alternative: 'Use entity_profile instead when you want the static profile...' It also describes source fallbacks and soft-failure behavior, helping 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.