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

Beyond annotations (read-only, idempotent), the description discloses multi-source fan-out behavior, fallback logic (GDELT→GNews when rate-limited or 5xx), USPTO API sunset soft-fail, and output structure (changes[] grouped by source, total_changes, citation URIs). This is substantial behavioral context that helps the agent understand side effects and dependencies.

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, starting with practical query examples, then the core function, source fallbacks, input syntax, output, and alternative tool. It is longer than usual but every sentence provides necessary context, so it earns a 4 rather than 5 for being slightly packed.

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 no output schema, the description effectively covers the return format (changes[] grouped by source, total_changes, pipeworx:// citation URIs), input variants, and known failure modes (USPTO sunset). The complexity of multiple data sources and fallbacks is well-documented, making the description sufficient for agent invocation.

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 description coverage is 100% and already contains the parameter details for `type`, `since`, and `value`. The tool description largely repeats these (e.g., `since` formats, ticker/CIK values) without adding new meaning or edge-case behavior, so the baseline of 3 applies.

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 identifies the tool as a change feed for a company over a time window, with natural language examples like "What's new with X". It explicitly distinguishes itself from entity_profile, stating that static profiles should use that tool instead, which solidifies purpose differentiation.

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 by giving example queries and an explicit alternative: "Use entity_profile instead when you want the static profile (filings + fundamentals + LEI + patents) regardless of window." This tells the agent exactly when to select this tool versus a sibling, fulfilling the dimension fully.

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