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

Annotations already establish read-only, idempotent, non-destructive behavior. The description goes further by detailing the parallel fan-out to SEC EDGAR, GDELT→GNews fallback logic, USPTO soft-fail due to API sunset, and the exact return shape (changes[] grouped by source, total_changes, pipeworx:// citation URIs). This is substantial behavioral context that annotations alone do not 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?

The description is dense but well-organized. It front-loads natural-language queries and the core one-call fan-out behavior, then efficiently adds fallback details, parameter syntax, return summary, and the alternative tool pointer. Every sentence earns its place and no filler is present.

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 that this is a multi-source tool with no output schema, the description is remarkably complete. It covers trigger phrases, data sources, failure/fallback behavior, parameter format, return structure, and an alternative tool. An agent has enough context to select the tool, invoke it with correct parameters, and understand the returned data.

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 description coverage is 100%, so the baseline is 3. The description adds value by giving concrete examples for `since` (ISO date vs '7d', '30d', '3m', '1y') and recommending '30d' or '1m' for typical monitoring. It also reinforces that `value` accepts a ticker or zero-padded CIK, which clarifies usage beyond the bare schema text.

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 defines the tool as a 'change feed for a company in the last N days/weeks/months' and enumerates the concrete data sources (SEC EDGAR, GDELT/GNews, USPTO). It also explicitly distinguishes this tool from the sibling entity_profile by pointing out the alternate use case, so an agent can immediately grasp the specific purpose.

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 includes natural-language trigger examples ('What's new with X', 'latest on Y') and explicitly states when to use entity_profile instead ('Use entity_profile instead when you want the static profile... regardless of window'). It also explains fallback conditions (GDELT preferred, GNews on rate limit/5xx), which is actionable guidance for choosing this tool over others.

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