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

Annotations already declare safe read-only, open-world, idempotent behavior, but the description adds substantial operational detail beyond them: required sign-in, paid tier for 'thorough', parallel facet decomposition, gap[] never-invented behavior, citation_uri fetchability, semantic excerpting, contradictions[], and expected latency ranges. There is no conflict with annotations; this is the gold standard for behavioral disclosure.

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 long but front-loaded with the most decision-critical facts (account requirement, fallback, structured-data scope) and each subsequent block earns its place (alternatives, depth semantics, output guarantees, latency). It loses a point for mild redundancy: the paid requirement and contradictions[] behavior are repeated from the schema description.

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?

For a complex, high-latency tool with no output schema, the description covers everything an agent needs before invoking it: auth constraints, substitution path, input suitability, result packet structure, limitations on news/open-web topics, citation resolvability, depth-dependent behavior, and execution time. No critical gap is left to inference.

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 both parameters; the description adds value by explaining what question types fit ('Broad/multi-part is fine') and by detailing the behavioral implications of depth values (standard adds gap-recovery hop and contradictions, thorough adds lead-chasing and ~90s latency). It stops short of giving concrete example values for depth beyond what the schema already enumerates, hence not a 5.

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 opens with a specific verb and resource ('Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources') and clarifies what it is not ('this is NOT open-web search'). It also differentiates from siblings by contrast ('For a single lookup use ask_pipeworx'; 'prefer ask_pipeworx' for breaking news), so an agent can identify the tool's exact niche.

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 gives explicit when-to-use and when-not-to-use guidance: it is 'Best for broad/multi-part questions over structured data,' with the inverse conditions spelled out ('single lookup', 'BREAKING or colloquial CURRENT-NEWS'). It even provides a fallback for unsigned-in users ('use ask_pipeworx instead'). This is unusually complete routing guidance.

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