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

The description adds significant behavioral context beyond the annotations: it discloses the fan-out to multiple external APIs (SEC EDGAR, GDELT, GNews, USPTO), the fallback mechanism, and the PatentsView sunset issue (soft-fail). It also outlines the return structure (changes[] grouped by source, total_changes count, citation URIs). No contradictions with annotations (readOnlyHint, idempotentHint, etc.).

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 efficiently structured, front-loaded with example queries that immediately convey purpose. Every sentence serves a purpose (sources, fallback, date formats, return structure, sibling differentiation). It is somewhat lengthy but justified by the tool's complexity.

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 no output schema, the description fully covers the return structure (changes[] by source, total_changes count, citation URIs). It addresses multiple data sources, fallback logic, date format options, and a known limitation (PatentsView sunset). This is comprehensive for a tool with 3 parameters and moderate complexity.

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% (all parameters described). The description adds value by providing usage examples for the `since` parameter (ISO date and relative shorthand like '7d', '30d', '3m', '1y') and explaining that `value` can be a ticker or CIK. It also suggests typical monitoring window ('30d' or '1m'). This goes beyond the schema's minimal descriptions.

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's purpose with specific verbs and resources: 'change feed for a company in the last N days/weeks/months'. It provides example queries ('What's new with X', 'latest on Y') and lists the data sources (SEC EDGAR, GDELT/GNews, USPTO). It also distinguishes itself from sibling tool 'entity_profile' by specifying a different use case.

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?

The description gives explicit guidance on when to use this tool vs. an alternative: 'Use entity_profile instead when you want the static profile...' It also explains fallback behavior (GDELT→GNews). However, it does not explicitly state when not to use this tool beyond the sibling comparison, but the context is clear.

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