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

Annotations already declare read-only, idempotent, and non-destructive behavior. The description adds substantial context beyond this: it details the fan-out to multiple sources (SEC EDGAR, GDELT/GNews, USPTO), explains fallback logic for rate limiting (GNews when GDELT fails), and notes soft-fail for patents due to API sunset. This is exactly the kind of behavioral depth annotations alone cannot convey.

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?

Despite its length, every sentence serves a purpose: the opening lists real-world query examples, the middle covers sources and fallbacks (including the PatentsView sunset caveat), and the closing explains return format and alternative tool. The information is front-loaded with purpose, and the structure is logical. There is no filler or redundant repetition beyond what schema already provides for parameters.

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?

The tool has no output schema, yet the description clearly specifies the return shape: structured changes[] grouped by source, total_changes count, and pipeworx:// citation URIs. It also covers the multi-source fan-out, fallback behavior, and the distinction from entity_profile, making it complete for an agent to decide when to invoke this tool and what to expect.

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 the schema already explains each parameter thoroughly (e.g., 'since' format and typical usage). The description mostly repeats this information rather than adding new meaning. It adds context about the overall window ('last N days/weeks/months') but does not materially enhance parameter understanding 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 clearly identifies the tool as a change feed for a company, with explicit examples of user queries ('What's new with X', 'latest on Y'). It distinguishes itself from sibling tools by naming entity_profile as the alternative for static profiles, making the scope and resource type unambiguous.

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?

Provides explicit usage scenarios and parameter guidance (e.g., 'since' with formats and typical values). It also explicitly names entity_profile as the alternative when static profile data is needed, offering clear exclusion criteria. The fallback behavior between GDELT and GNews is disclosed, helping the agent set expectations.

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