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

Annotations already state readOnly, openWorld, and idempotent hints, and the description adds rich behavioral context: fan-out to multiple APIs, GDELT→GNews fallback on rate-limited/5xx responses, USPTO soft-fail due to PatentsView API sunset, and the exact return shape (changes[], total_changes, citation URIs). No contradictions with 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 front-loaded with user intent examples, then efficiently covers sources, parameter semantics, output format, and the alternative tool in a compact, logically organized paragraph. Every sentence earns its place; no filler or redundancy.

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 logic, date parsing, no output schema), the description is remarkably complete. It specifies the output structure, source-specific behavior, failure modes, and even recommends the alternative tool for static profiles, making it fully actionable for an AI agent.

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 input schema already provides full descriptions for all three parameters (100% coverage), so baseline is 3. The description adds value by illustrating `since` with both ISO and relative examples, recommending '30d' or '1m' for typical monitoring, and showing `value` usage with a CIK example. This is helpful but not transformative beyond the schema.

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' with specific sources (SEC EDGAR, GDELT/GNews, USPTO) and explicitly contrasts it with entity_profile, making its purpose distinct from siblings. It starts with concrete natural-language triggers, so an agent can match queries like 'what's new with X' to this tool.

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 when-to-use guidance via example queries and a clear alternative: 'Use entity_profile instead when you want the static profile.' It also gives practical usage details like the `since` parameter accepting '30d' or '1m' for typical monitoring and explains fallback behavior, leaving little ambiguity.

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