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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses important behavioral details: it fans out to multiple sources (SEC, GDELT/GNews, USPTO), explains fallback logic (GDELT preferred, GNews when rate-limited or 5xx), and mentions USPTO soft-failure due to API sunset. This context is not available from annotations alone and sets accurate expectations for reliability and output.

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 longer than the simplest examples but each sentence earns its place: it starts with the core purpose, then details the multi-source fan-out and failure modes, parameter usage, return shape, and alternative tool. The structure is logical and front-loaded, though slightly dense, so a 4 is appropriate.

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 the complexity of the tool (multiple external APIs, fallbacks, failure modes) and the absence of an output schema, the description adequately explains what is returned ('structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs') and covers the main behavioral edge cases. It is complete enough for an agent to select and invoke the tool 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?

The schema already provides 100% coverage for all three parameters. The description enriches these by adding guidance like "Use '30d' or '1m' for typical monitoring," clarifying relative shorthand formats, and explaining how the value parameter maps to ticker or CIK. This goes beyond the schema's baseline and helps the agent choose good parameter values.

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 function as a 'change feed for a company in the last N days/weeks/months' and provides example queries. It distinguishes itself from the sibling tool entity_profile by explicitly directing users to that tool for static profile needs, leaving no ambiguity about scope.

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 when-to-use context with natural language examples like "what's new with X" and "updates on Acme." It also names an alternative (entity_profile) for cases where a static profile is needed, providing a clear decision rule.

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