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

Despite strong annotations (readOnlyHint, idempotentHint, etc.), the description adds rich behavioral disclosures: it fans out to SEC EDGAR, GDELT→GNews fallback with specific rate-limit/5xx conditions, and USPTO with a PatentsView API sunset causing soft-fail. It also explains the return structure (changes[] grouped by source, total_changes, pipeworx:// URIs) and `since` format, going well beyond what annotations state.

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?

Although lengthy, every sentence contributes essential operational detail: source orchestration, fallback logic, parameter formats, and return payload. It is front-loaded with examples and uses compact parentheticals. No filler or repetition of schema/annotation content.

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 effectively explains the return shape (structured changes[] grouped by source + total_changes + citation URIs). It also covers important edge cases (rate limiting, API sunset) and provides the key parameter guidance. The tool is complex, and the description addresses all necessary aspects for correct invocation.

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 extra value by explaining the `since` formats with examples ('2026-04-01', '7d', '30d', '3m', '1y') and recommending '30d' or '1m' for typical monitoring. It also clarifies that `value` accepts ticker or CIK, reinforcing the schema but with additional usage nuance.

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 user intents ('What's new with X', 'updates on Acme') and immediately identifies the tool as a change feed for a company over a time window. It explicitly contrasts with entity_profile (static profile), distinguishing it from that sibling.

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 explicit usage context: 'change feed for a company in the last N days/weeks/months in ONE parallel call' and offers an explicit alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' Also clarifies the combined data sources and fallback behavior, helping the agent decide when to invoke this tool.

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