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

Beyond the readOnlyHint and idempotentHint annotations, the description discloses behavioral traits: it fans out in parallel, prefers GDELT but falls back to GNews on rate-limits or 5xx, and soft-fails for USPTO due to the PatentsView API sunset. It also mentions the output structure (changes[], total_changes, citation URIs), which adds valuable context the annotations don't provide.

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 with useful information but well-structured: query examples, source behavior, parameter hints, output summary, and an explicit alternative. It front-loads the purpose and every sentence adds value; 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?

Even though there is no output schema, the description fully explains what the tool returns (changes grouped by source, total_changes, pipeworx:// citation URIs), how it behaves across multiple data sources, and when to use an alternative. It gives enough context for an agent to invoke it correctly without needing additional documentation.

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 documents all three parameters with full descriptions (100% coverage), so the baseline is 3. The description adds value by showing `since` usage examples ('ISO date or relative shorthand') and recommending '30d' or '1m' for typical monitoring. It also clarifies `value` accepts ticker or CIK, though this mirrors the schema. Overall it enriches the parameter guidance enough to warrant a 4.

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 states a clear, specific purpose: it is a change feed for a company over a recent window, with concrete natural-language triggers ('What's new with X'). It explicitly names the fan-out sources (SEC EDGAR, GDELT→GNews, USPTO) and distinguishes itself from the sibling entity_profile tool, so the agent knows exactly what it does and how it differs.

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 via example queries and clearly directs to an alternative: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also gives typical `since` values ('30d' or '1m') and notes fallback behavior, giving the agent clear decision criteria.

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