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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses significant behavioral context: it fans out to three external sources, describes a fallback chain (GDELT→GNews), notes the PatentsView API sunset causing soft-fail until reactivated, and describes the return structure (changes[] grouped by source, total_changes count, pipeworx:// URIs). This goes well beyond what annotations provide.

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?

Though longer than typical descriptions, it is dense and logically organized: query examples → core function → source fan-out → parameter syntax → return value → alternative tool. Every sentence provides unique information; no filler or repetition of annotations/schema.

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 tool with no output schema, the description explains what the tool returns, the sources and fallbacks, parameter formats, and known limitations (USPTO soft-fail). It also names the sibling tool to use for a different use case. This is complete enough 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%, so baseline is 3. The description adds value by giving concrete examples for `since` ('7d', '30d', '3m', '1y'), clarifying that `value` can be a ticker or zero-padded CIK (with example '0000320193'), and recommending a default parameter ('30d' or '1m'). This practical context enhances the schema.

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 examples ('What's new with X', 'updates on Acme') then clearly defines the tool as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It specifies the exact sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly differentiates from the sibling `entity_profile` by naming it as the static-profile alternative.

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 explicitly states when to use this tool vs. alternatives: 'Use entity_profile instead when you want the static profile...'. It also provides practical guidance like typical monitoring windows ('Use "30d" or "1m" for typical monitoring') and describes fallback behavior (GDELT preferred, GNews when rate-limited or 5xx), giving the agent clear decision criteria.

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