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

Goes well beyond the readOnly/idempotent annotations by disclosing the multi-source fan-out to SEC EDGAR, GDELT→GNews fallback behavior, USPTO soft-fail due to API sunset, and the exact output shape. No contradictions with annotations exist.

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?

Though long, every sentence adds unique value: usage examples, source details, fallback logic, date formats, output structure, and an alternative. It is front-loaded with the most important usage trigger and remains structured despite its density.

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?

For a complex multi-source tool with no output schema, the description is remarkably complete: it explains the window parameter, each data source, fallbacks, output fields, and the main alternative. It leaves no critical ambiguity for a selecting agent.

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 descriptions already cover all three parameters thoroughly (type enum, since formats, value examples). The tool description repeats the `since` format and adds some context about the parallel call, but it does not meaningfully expand parameter meaning beyond the 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 starts with natural-language query patterns and then states the core function: a change feed for a company over a date window via one parallel call. It also distinguishes itself from the sibling `entity_profile` by explicitly naming when to prefer that tool instead.

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?

It gives clear example intents ("What's new with X", "latest on Y") and explicitly says to use `entity_profile` instead for static profiles regardless of window. This provides both positive and negative usage guidance.

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