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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses the fan-out architecture across three sources, the GDELT→GNews fallback trigger, the PatentsView API sunset causing soft-failure, and the return structure (changes[] grouped by source, total_changes, pipeworx:// citation URIs). This is rich behavioral context that annotations alone do not convey.

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 earns its place: it fronts with use cases, then explains the fan-out and fallbacks, date formats, return contract, and finally points to an alternative. No filler or repetition; the structure naturally guides the agent from intent to invocation.

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 fan-out, fallback logic, soft-fail conditions, date parsing) and the absence of an output schema, the description covers all necessary behavioral and result details: sources, fallback triggers, soft-fail rationale, return shape (changes[], total_changes), and citation URIs. It also notes the parallel-call efficiency, making it complete for safe selection and invocation.

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?

Although schema coverage is 100%, the description adds substantial parameter semantics: 'since' accepts ISO date or relative shorthand with examples ("7d", "3m") and a recommendation ("30d" or "1m"); 'value' is clarified with ticker or CIK examples; 'type' enum is explicitly narrowed to "company". These practical details go well beyond the 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 opens with concrete user intents ("What's new with X", "latest on Y") then states it is a "change feed for a company in the last N days/weeks/months in ONE parallel call." It names the specific sources (SEC EDGAR, GDELT→GNews, USPTO) and clearly distinguishes itself from the sibling tool entity_profile, making its purpose 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 when-to-use guidance with example queries and states that entity_profile should be used instead when a static profile is needed. It also explains fallback behavior (GDELT preferred, GNews when rate-limited, USPTO soft-fails) which informs the agent's decision-making.

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