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

Beyond the read-only/idempotent annotations, the description reveals the multi-source fan-out, GDELT→GNews fallback for rate limits/5xx, and the USPTO PatentsView soft-failure after May 2025. It also clarifies the return shape including citation URIs, which is not visible in any structured field.

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 and information-rich, covering query examples, sources, fallbacks, date formats, return shape, and an alternative in one paragraph. While all content is necessary, the lack of bullet points or segmentation slightly reduces scannability.

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?

For a tool with no output schema, the description explains return structure (changes[], total_changes, citation URIs) and edge cases (patent soft-fail). It also addresses multi-source complexity and gives an alternative, making it sufficiently complete for agent use.

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 three parameters, so the baseline is 3. The description adds value by explaining accepted forms for `since` (ISO or relative) and recommending '30d'/'1m' for typical monitoring, going beyond schema definitions.

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 companies, listing example queries and the underlying data sources. It explicitly contrasts with entity_profile, distinguishing its dynamic window-based scope from a 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?

It provides explicit guidance by showing natural-language triggers ('What's new with X') and specifying when to choose entity_profile instead. It also explains fallback logic and relative date syntax, equipping the agent to decide when this tool fits.

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