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

Beyond the read-only/idempotent annotations, the description reveals source-specific quirks (GDELT→GNews fallback on rate limits, USPTO soft-fail due to API sunset), the single-parallel-call nature, and the exact output structure (changes[] grouped by source, total_changes, pipeworx:// URIs). This is rich behavioral context.

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 and informative, front-loading the core purpose and query examples. While the enumeration of phrasings is somewhat repetitive, each part contributes to LLM understanding. It could be more structured, but everything earns its place.

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?

With no output schema, the description still details the return structure (changes[] grouped by source, total_changes count, citation URIs) and covers edge cases (fallbacks, soft-fail). For a tool with multiple data sources and parsing quirks, this is highly complete.

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 practical guidance on the `since` parameter (ISO date or relative shorthand like '7d', '30d', '3m', '1y') and suggests '30d' or '1m' for typical monitoring. This goes beyond the schema's property 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 explicitly identifies the tool as a change feed for a company over a time window, listing concrete data sources (SEC EDGAR, GDELT/GNews, USPTO) and giving natural-language examples. It also distinguishes itself from entity_profile, making its unique role clear.

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 guidance: 'Use entity_profile instead when you want the static profile... regardless of window.' It also includes exemplary queries and explains fallback behavior (GDELT→GNews) and the USPTO soft-fail, helping an agent decide when to invoke this tool.

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