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

Beyond the annotations (readOnly, idempotent, openWorld), the description discloses significant behavioral traits: multi-source fan-out, GDELT→GNews fallback triggers, USPTO soft-fail due to API sunset, and the return structure ('changes[] grouped by source + total_changes count + pipeworx:// citation URIs'). This adds substantial context not present in annotations.

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 but well-organized. It front-loads example queries, then defines the core behavior, lists sources, explains fallback logic, and closes with an alternative tool. Every sentence contributes necessary context, with no redundancy or filler.

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?

There is no output schema, so the description takes on the burden of explaining return values—it does so clearly (structured changes[] grouped by source, total_changes count, citation URIs). Combined with the detailed source and fallback behavior, the description fully covers the tool's operational context.

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 the schema already documents all three parameters (type, since, value). The description only reiterates the `since` format and examples already in the schema, without adding new meaning. Per the rubric, the baseline is 3 when schema coverage is high, and the description does not elevate it.

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 states the tool's function: 'change feed for a company in the last N days/weeks/months'. It uses concrete example queries and explicitly lists the data sources (SEC EDGAR, GDELT→GNews, USPTO), and distinguishes itself from the sibling tool entity_profile by noting its focus on dynamic changes rather than 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?

The description provides explicit usage guidance: 'Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window.' It also gives natural language examples of appropriate queries and explains fallback behavior (GDELT preferred, GNews on rate limit/5xx), making the tool's intended use clear.

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