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

The annotations already declare read-only, open-world, idempotent, and non-destructive behavior. The description adds valuable operational details beyond that: it fans out to SEC EDGAR, GDELT with GNews fallback on rate limits/5xx, USPTO with a soft-fail caveat due to PatentsView sunset, and it returns changes[] with citation URIs. This is substantial context, though it doesn't cover every edge case (e.g., pagination or empty results).

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 long but front-loaded with examples and flows logically from purpose to sources to fallback logic, parameters, output, and alternative tool. Every sentence carries necessary information; no filler. Its density is appropriate for a tool with this complexity, though it could be slightly trimmed without loss.

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?

For a multi-source aggregation tool with no output schema, the description covers the full picture: input formats, source-specific fallback behavior, known API caveat (PatentsView sunset), return structure (changes[], total_changes, citation URIs), and when to use an alternative. This is sufficient for an agent to invoke the tool correctly without further clarification.

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 enhances `since` by giving concrete examples of ISO dates and relative shorthands ('7d', '30d', '1y'), and it implies the `value` format through the source list (ticker/CIK). The `type` restriction to 'company' is also reinforced. This added context justifies an above-baseline score.

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 over a recent time window, listing specific data sources (SEC EDGAR, GDELT/GNews, USPTO) and return format. It also distinguishes itself from entity_profile by explicitly directing users to that tool for static profiles. The verb 'returns' and the concrete examples make the purpose unmistakable.

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 provides explicit usage triggers through natural language examples (e.g., 'What's new with X') and a clear alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' This tells the agent both when to use this tool and when to route to a sibling, meeting the 5-level criteria.

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