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

Annotations already mark this as read-only, idempotent, and non-destructive, so the bar is somewhat lower. The description adds valuable behavioral context: parallel fan-out to multiple sources, GDELT→GNews fallback on rate-limiting/5xx, and USPTO soft-fail due to PatentsView API sunset. It also states the return shape, making runtime behavior more predictable.

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 each sentence earns its place: query examples, source details with fallback logic, `since` format, return value summary, and sibling differentiation. It is front-loaded with the core purpose and follows a logical structure that an agent can parse efficiently.

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 tool's complexity (multi-source, fallback behavior, soft-fail, windowed queries) and the absence of an output schema, the description covers all essential aspects: input semantics, sourcing behavior, failure modes, and return structure (changes[], total_changes, citation URIs). It is sufficiently complete for an agent to select and 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%, so the baseline is 3. The description adds extra utility by expanding `since` with examples (ISO date or relative shorthand) and recommending '30d' or '1m' for typical monitoring. It also reinforces that only company type is supported, matching the schema enum, though it does not add much beyond that.

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' over a time window, listing concrete sources (SEC EDGAR, GDELT/GNews, USPTO). It also distinguishes itself from the sibling tool entity_profile by explicitly stating when to use that instead.

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?

It opens with natural-language query examples that signal appropriate use cases, explains it fans out in ONE parallel call, and provides an explicit directional alternative: 'Use entity_profile instead when you want the static profile...' This gives an agent strong 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.