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

The description goes well beyond annotations by disclosing the parallel fan-out to SEC EDGAR, GDELT, GNews, and USPTO, including fallback triggers (rate limits/5xx) and soft-fail conditions (PatentsView API sunset). It also specifies the exact return structure (changes[] grouped by source, total_changes, citation URIs).

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 every sentence delivers value: examples, core function, source details, fallback logic, parameter formats, output summary, and an explicit alternative. It is front-loaded with user-intent examples and avoids redundancy with the schema.

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, fallbacks, time-window semantics), the description covers all critical operational aspects: data sources, fallback behavior, input formats, output structure, and a clear pointer to an alternative tool. No output schema exists, so the description rightly explains return values (changes[], total_changes, citation URIs).

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%, and the description adds extra guidance for the `since` parameter (e.g., 'Use "30d" or "1m" for typical monitoring') and clarifies supported relative formats. It does not add much for `type` and `value`, but the schema already provides adequate descriptions.

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 recent window, with explicit query examples like "What's new with X". It distinguishes itself from the sibling entity_profile by contrasting window-based changes vs 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?

Provides explicit when-to-use context via query patterns, and explicitly names the alternative: 'Use entity_profile instead when you want the static profile'. Also explains the data source fallback behavior (GDELT→GNews) as a usage consideration.

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