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

Beyond the annotations (readOnlyHint, idempotentHint, etc.), the description discloses the fan-out to SEC EDGAR, GDELT/GNews, and USPTO, including the soft-fail due to PatentsView API sunset. It also reveals the return structure (changes[], total_changes, citation URIs), which is not available from any structured field.

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 dense but front-loaded with the primary use case, followed by source details, parameter formats, output summary, and alternative tool. Every sentence serves a purpose; there is no filler or redundancy. Its length is justified by the tool's multi-source 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?

Since there is no output schema, the description fully specifies the return payload ('structured changes[] grouped by source + total_changes count + pipeworx:// citation URIs'), lists all data sources, explains failure modes, and provides an alternative tool. It gives an agent everything needed to invoke the tool correctly without further documentation.

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 description adds behavioral meaning to the parameters: it connects `since` to 'filings since `since`' and news window, provides a concrete usage recommendation ('30d' or '1m'), and clarifies that `value` maps to 'Ticker or zero-padded CIK.' Although the schema already documents all three parameters (100% coverage), the description enriches their semantics with execution context.

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 opens with concrete natural-language queries ('What's new with X') and then states 'change feed for a company in the last N days/weeks/months in ONE parallel call,' making the specific verb+resource+scope clear. It also distinguishes from the sibling tool entity_profile by explicitly naming it as the 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?

It explicitly says to use entity_profile when static profile data is needed regardless of window, providing a clear when-not-to-use rule. It also details the fallback behavior among data sources (GDELT preferred, GNews on rate-limit/5xx), which informs the agent about expected reliability and selection criteria.

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