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

Annotations already declare readOnly/openWorld/idempotent/non-destructive, but the description adds substantial behavioral detail: fan-out to three sources, GDELT-to-GNews fallback conditions ('rate-limited or 5xx'), USPTO soft-fail due to API sunset, and the output shape ('structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'). No contradiction with annotations.

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 a single dense paragraph, but it is front-loaded with natural-language invocations, then flows logically through sources/fallbacks, parameters, return shape, and alternative tool. Every sentence contributes new information without redundancy, making it appropriately sized for 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?

Despite having no output schema, the description specifies the exact return structure and includes important context like source-specific failure modes (GDELT→GNews fallback, USPTO soft-fail). It also covers parameter usage and suggests when to use a sibling tool, making it complete for an agent to select and invoke 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% with each parameter described. The description adds value beyond schema by elaborating on `since` values ('Use "30d" or "1m" for typical monitoring'), providing ticker and CIK examples for `value`, and noting that `type` is only 'company'. This lifts the score above the baseline 3.

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 uses a specific verb phrase ('change feed for a company in the last N days/weeks/months') and anchors with common queries like 'What's new with X'. It explicitly distinguishes from the sibling 'entity_profile' by stating the difference between change feed and static profile, satisfying sibling differentiation.

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?

Provides a clear when-to-use context via example queries and explicitly states the alternative: 'Use entity_profile instead when you want the static profile...'. It also explains fallback behavior between GDELT and GNews, giving the agent context for expected results. This exceeds minimal guidance.

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