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

Beyond the read-only/idempotent annotations, the description discloses fallback behavior (GDELT preferred, GNews on rate-limit/5xx), soft-fail for USPTO due to API sunset, and return structure. This gives the agent realistic expectations about failures and data grouping.

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 queries, source fallbacks, parameter format, return shape, and sibling distinction. It is front-loaded with natural language examples and remains structured 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?

With no output schema, the description fully explains the return value ('structured changes[] grouped by source + total_changes count + citation URIs'). It covers all relevant context: sources, fallbacks, parameter syntax, and alternative tool—making it complete for a complex tool.

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 all parameters (100% coverage), so baseline is 3. The description adds extra semantic value by explaining `since` relative shorthand with examples and a monitoring tip ('Use "30d" or "1m" for typical monitoring'), which goes beyond the schema.

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 defines the tool as a change feed for a company over a time window, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO). It uses specific verbs and resource scope ('change feed for a company') and explicitly distinguishes from sibling tool entity_profile.

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 with example queries and a direct alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' This tells the agent exactly when to choose this tool over a sibling.

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