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

Annotations already declare readOnlyHint=true, openWorldHint=true, and destructiveHint=false, so the safety profile is covered. The description adds substantial behavioral context beyond that: parallel decomposition across 5,798 tools, findings packet contents, gaps[] never invented, contradictions[] behavior, semantic excerpting, resolvable citations, and latency expectations. 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 the tool is complex and every section earns its place: auth, alternatives, scope, mechanics, return format, gaps, contradictions, and timing. It is front-loaded with the most decision-critical facts (account requirement and the ask_pipeworx alternative). Slight redundancy in the depth explanation keeps it from a 5.

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 two parameters, no output schema, and a complex research tool, the description covers everything an agent needs to call it correctly: auth requirements, tiering, data scope, return packet structure, gap handling, contradiction reporting, citation resolvability, and expected latency. Example questions further anchor correct usage.

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 baseline is 3. The description goes further by explaining the practical consequences of depth choices: paid requirement for thorough, latency ranges, and which depth tiers include contradiction scans. It also clarifies that the question parameter is well-suited to multi-part natural language. This adds genuine operational meaning 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 uses a specific verb-resource pair — 'grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' — and clearly distinguishes itself from open-web search. It also names what it is not and references a sibling (ask_pipeworx) to prevent confusion.

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 explicitly states when to use the tool ('best for broad/multi-part questions over structured data'), when not to use it ('For a single lookup use ask_pipeworx instead'), and the account/paid-tier prerequisite. It even gives a fallback: if not signed in, use ask_pipeworx.

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