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

The description goes well beyond the annotations (readOnlyHint, idempotentHint, destructiveHint) by disclosing behavioral details: parallel fan-out, GDELT preferred with GNews fallback on rate-limit/5xx, USPTO patent API sunset causing soft-fail, and the structured return shape (changes[], total_changes, citation URIs). This adds significant context not available in annotations.

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 somewhat long but every sentence conveys necessary information—examples, source details, fallback logic, and return structure. It is front-loaded with user-intent phrasing, making it easy to scan. The density is justified by the tool's multi-source complexity, though a more compact structure could improve readability.

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 the absence of an output schema, the description adequately explains the return format (structured changes[] grouped by source, total_changes count, pipeworx:// citation URIs). It also covers the tool's multi-source nature, fallbacks, and known API limitations, leaving no critical gap for an agent to invoke the tool 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%, providing a baseline of 3. The description adds value by elaborating on `since` with ISO and relative shorthand examples ('7d', '30d', '3m', '1y') and recommending '30d' or '1m' for typical monitoring. It also clarifies `value` as ticker or CIK, which the schema already mentions, so the incremental insight is moderate but still useful.

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 explicit natural-language queries ('What's new with X') and clearly defines the tool as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It distinguishes itself from the sibling entity_profile by contrasting windowed changes versus static profile, leaving no ambiguity about the tool's function.

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 provides rich usage context: it explains the fan-out to SEC EDGAR, GDELT→GNews fallback with specific failure conditions, USPTO soft-fail behavior, and the `since` parameter formats. It explicitly names an alternative tool: 'Use entity_profile instead when you want the static profile...', giving clear when-to-use vs. when-not-to-use guidance.

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