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

Annotations already declare readOnlyHint, openWorldHint, idempotentHint, and destructiveHint. The description adds significant behavioral context: it explains the parallel fan-out to multiple sources, the GDELT→GNews fallback on rate limits or errors, the PatentsView API sunset causing soft-failure, and the output structure (grouped by source with total_changes count and citation URIs). No contradiction with annotations. This exceeds what annotations alone 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 a single paragraph but packs a lot of information efficiently. Every sentence contributes meaning—user queries, tool behavior, fallback, parameter guidance, and comparison to sibling. It front-loads with examples. However, it could be slightly more structured (e.g., bullet points or separate sections) for easier scanning, but it remains readable.

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 (multiple data sources, fallback mechanisms, return format, parameter variants) and the absence of an output schema, the description is remarkably complete. It explains the output structure (structured changes[] grouped by source, total_changes count, pipeworx:// citation URIs), the fallback logic, and the limitations (USPTO soft-failure). Nothing essential is missing.

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%, baseline is 3. The description adds value by clarifying that 'type' only supports 'company', explaining the 'since' format with examples and a recommendation, and giving examples for 'value' (ticker or CIK). This provides practical context beyond the schema field descriptions, though it does not introduce new constraints.

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 starts with concrete user query examples ('What's new with X', 'latest on Y'), then explicitly states it is a 'change feed for a company in the last N days/weeks/months in ONE parallel call'. It names the data sources (SEC EDGAR, GDELT→GNews fallback, USPTO) and distinguishes from the sibling tool 'entity_profile' by specifying when to use that instead. This makes the purpose highly specific and unambiguous.

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 explicit guidance on when to use this tool (for recent change queries) and when not to ('Use entity_profile instead when you want the static profile'). It also explains the fallback behavior for news sources and the soft-failure for patents, and recommends a typical 'since' value ('30d' or '1m'). This is comprehensive and directly helps the AI agent choose correctly.

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