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

The description goes far beyond the readOnlyHint/idempotentHint annotations by disclosing decomposition into parallel tool calls, the findings packet format (verbatim evidence, confidence, source, fetched_at, citation), explicit gaps[] for unanswered facets, contradictions[] for standard/thorough, semantic excerpting of large records, latency estimates, and the guarantee that citations are fetchable. This is extensive, honest 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 dense and information-rich, with no truly wasted sentences; it front-loads the critical account requirement and alternative tool. However, it is one long, unbroken paragraph with some repetitive emphasis and Markdown artifacts, which slightly reduces scannability for an agent trying to parse key facts quickly.

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 there is no output schema, the description must explain return behavior, and it does: findings packet structure, gaps[], contradictions[], citation URI resolvability, and latency. It also covers authentication, paid-tier restrictions, depth semantics, and example question shapes, making the tool fully callable by an agent with no other 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 description coverage is 100%, so the baseline is 3 and the schema already documents 'question' and 'depth' well. The description adds extra meaning by explaining that 'thorough' needs a paid plan, describing the standard vs thorough follow-up/contradiction behavior in prose, and giving realistic latency tradeoffs tied to depth selection.

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 uses a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources,' and explicitly distinguishes itself as 'NOT open-web search.' It also tells the agent this is best for 'broad/multi-part questions' and contrasts with a single-lookup sibling, so the tool's role is 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?

The description gives explicit routing guidance: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx.' It also states that 'thorough' requires a paid plan and clarifies when deep_research is the right choice over alternatives, leaving no ambiguity about when to invoke it.

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