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

Beyond annotations, the description discloses significant behavioral details: fan-out to SEC EDGAR, GDELT→GNews fallback logic, USPTO soft-failure due to PatentsView API sunset, and accepted `since` formats. This adds real value over the readOnly/idempotent hints.

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?

Although the description is long, every clause conveys essential information: query patterns, source fan-out, fallback behavior, soft-fail, date formats, return structure, and alternative tool. 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 upstream APIs, fallbacks, soft-fail) and lack of an output schema, the description is remarkably complete. It explains return shape (changes[] grouped by source, total_changes, citation URIs) and when to use a sibling tool, leaving few gaps.

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%, so baseline is 3. The description adds above baseline by giving concrete examples for `since` (7d, 30d, 3m, 1y), a recommendation to use '30d' or '1m' for monitoring, and clarifying the CIK zero-padding. This is helpful but not a huge leap beyond 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 over a recent window, with specific verbs like 'what's new' and 'change feed'. It also distinguishes itself from sibling entity_profile by contrasting dynamic changes with static profiles.

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 usage context with natural language query examples and an explicit alternative: 'Use entity_profile instead when you want the static profile...'. This gives both when-to-use and when-not-to-use guidance.

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