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

Although annotations already declare readOnlyHint=true and openWorldHint=true, the description adds substantial behavioral context: requires authentication (free sign-in, paid for thorough), decomposes questions into facets routed in parallel to 5,908 tools, returns findings with verbatim evidence/confidence/source/fetched_at and 'never invented' gaps[], plus hop field and fetchable citation_uri, contradictions[] for standard/thorough, semantic excerpting instead of head-truncation, and timing expectations. It also discloses the 'not open-web' constraint. No contradiction with annotations.

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 information-dense, and the most critical operational constraint (account required, alternative when signed out) is front-loaded. Every sentence adds value — grounding, decomposition, output packet, gaps, citations, depth semantics, timing. It is not bloated; it is rich, though a bit lengthy.

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 2 simple params, no output schema, and moderate complexity, the description is complete: it covers auth prerequisites, depth tiers and their behavioral differences, output shape, error/unknown handling (gaps[]), citation fetchability, and performance expectations. An agent has everything needed to select and invoke this tool 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?

Schema coverage is 100%, so the schema already documents both parameters well. The description adds meaning beyond the enum values by explaining the practical effect of depth levels: quick=single hop, standard=gap-recovery + contradictions[], thorough=paid + iterative hop. It also clarifies that the 'question' parameter accepts broad/multi-part natural language. This exceeds the baseline 3.

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?

States a specific verb+resource: 'Grounded multi-source research across Pipeworx's 1540 STRUCTURED data sources' in ONE call, and explicitly distinguishes from open-web search. Names the alternative ask_pipeworx for signed-out users, and gives concrete example questions. Clearly differentiates from siblings.

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?

Explicitly says 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead'. Also warns 'If you are not signed in, use ask_pipeworx instead'. Provides when-to-use, when-not-to-use, and the alternative tool. This is textbook usage guidance.

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