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

Beyond the annotations (readOnly, openWorld, idempotent, non-destructive), the description discloses important runtime behaviors: GDELT-to-GNews fallback on rate limits or 5xx, USPTO soft-failing due to PatentsView API sunset, supported `since` formats, and the exact return shape (changes[] grouped by source, total_changes, pipeworx:// citation URIs). This is rich, non-obvious context that annotations alone could 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 long but every sentence and clause carries operational value: user intents, source list, fallback logic, soft-fail caveat, parameter format, return structure, and pointer to alternative. It is front-loaded with the core purpose and uses punctuation effectively to pack information without 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 multi-source complexity and lack of an output schema, the description is remarkably complete. It explains not only what the tool does, but how it fails (USPTO soft-fail), how it falls back, what the response contains, and when to choose a sibling tool. No critical aspect of expected behavior or input constraints is undocumented.

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 100% parameter descriptions, including examples for `since` and `value` and the enum restriction for `type`. The description repeats most of this information (e.g., ISO/relative shorthand, ticker or CIK) and adds only minor clarifications like 'Use "30d" or "1m" for typical monitoring,' which is also in the schema. No significant new meaning is added beyond the structured 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 opens with concrete user intents ('What's new with X', 'latest on Y') and explicitly identifies the tool as a 'change feed for a company in the last N days/weeks/months in ONE parallel call.' It clearly distinguishes itself from entity_profile by stating when to use that alternative, and lists the specific data sources (SEC EDGAR, GDELT/GNews, USPTO) that define its scope.

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 gives explicit usage guidance: use for time-bound change queries across multiple sources, and use entity_profile instead for the static profile regardless of window. It also explains the fallback behavior between GDELT and GNews, which helps the agent decide when this tool is appropriate for news-related queries.

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