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

Annotations already mark the tool as read-only and idempotent. The description adds significant behavioral context: fans out to multiple sources (SEC EDGAR, GDELT, GNews fallback, USPTO), notes rate-limiting fallback behavior, and mentions a soft-fail for the patent API. It does not elaborate on error handling or edge cases, but the added context is valuable.

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 densely packed. It front-loads user intent examples and then covers sources, parameter formats, return structure, and alternatives. Every sentence provides useful information; however, the initial list of example phrases could be shortened 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?

With no output schema, the description compensates by explaining the return structure (changes[] grouped by source, total_changes count, citation URIs). It also covers the fan-out behavior, fallback logic, and the `since` format. The description is complete for the tool's complexity and provides sufficient context for an agent to invoke it correctly.

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 semantic detail beyond the schema, such as concrete examples for `since` ('7d', '30d', '3m', '1y'), the recommendation to use '30d' or '1m' for monitoring, and clarification that `value` accepts tickers or zero-padded CIKs, which slightly overlaps with schema but reinforces understanding.

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 states the tool's function: 'change feed for a company in the last N days/weeks/months' with examples of natural language queries. It distinguishes itself from the sibling 'entity_profile' by explicitly noting when to use that alternative for static profiles.

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 context with example queries and specifies the `since` parameter format. It gives an explicit alternative: 'Use entity_profile instead when you want the static profile...' and even recommends typical values ('Use "30d" or "1m" for typical monitoring').

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