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

The description goes far beyond the annotations (readOnly, idempotent) by exposing fallback behavior (GDELT preferred, GNews on rate-limit/5xx), a known API sunset (USPTO PatentsView soft-fail), and return structure (changes[] grouped by source, total_changes count, citation URIs). This gives the agent crucial behavioral expectations without any ambiguity.

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 every sentence earns its place. It opens with natural language queries, then lists sources, parameter behavior, return shape, and an alternative tool, all without redundancy. Despite its length, it is tightly structured and front-loaded with the most important usage cues.

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 complexity (multiple sources, fallbacks, time window parsing) and the absence of an output schema, the description provides a complete mental model. It explains the input formats, the output structure, source-specific caveats, and when to choose a different tool. No critical information is missing for an agent to invoke it correctly.

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?

Even though the schema already covers all parameters (100% coverage), the description adds valuable context beyond the schema: it explains `since` accepts ISO or relative shorthand with concrete examples and recommends "30d" or "1m" for typical monitoring. It also clarifies that `value` can be a ticker or zero-padded CIK. This enriches the parameter semantics meaningfully.

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 purpose: a change feed for a company in the last N days/weeks/months. It uses multiple natural language triggers ("What's new", "latest", "updates") and specifies the scope (companies, time window). It distinguishes itself from entity_profile by explicitly noting that entity_profile provides the static profile.

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 context with query examples and states when to use an alternative: "Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window." It also highlights the benefit of one parallel call, making it clear this is for dynamic changes over a time window.

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