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

Beyond the readOnly/idempotent annotations, the description discloses fallback behavior (GDELT preferred, GNews on rate-limit/5xx), the USPTO soft-fail due to PatentsView API sunset, that it fans out to multiple sources in a single parallel call, and the exact return structure (changes[] grouped by source, total_changes, pipeworx citation URIs). This is far richer than the annotations alone.

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 long but every sentence earns its place by adding distinct details about sources, fallbacks, or parameters. It opens with the user-facing query examples to immediately clarify the tool's purpose, making it front-loaded. The density is justified by the tool's complexity, though it could be slightly tightened without losing value.

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 explicitly defines the return shape (structured changes[] grouped by source, total_changes count, pipeworx:// citation URIs). It also explains the API dependencies and failure modes, making the tool's behavior highly predictable. For a multi-source data feed tool, this is fully complete.

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 documents all parameters at 100% coverage. The description adds practical nuances: 'since' examples including relative shorthand ('7d', '30d'), 'value' examples (ticker or zero-padded CIK), and a default recommendation ('30d' or '1m' for monitoring). This supplements the schema without redundancy.

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 identifies the tool as a change feed for a company in a specified time window, with examples like 'what's new with X' and 'updates on Acme'. It enumerates the exact sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly distinguishes itself from entity_profile by contrasting windowed changes vs static profile.

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 signals via the example query phrasing and states a clear alternative: 'Use entity_profile instead when you want the static profile... regardless of window.' It also provides guidance on selecting the 'since' parameter ('Use "30d" or "1m" for typical monitoring'), reinforcing appropriate usage.

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