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

Annotations already mark the tool read-only, open-world, idempotent, and non-destructive; the description adds substantial behavior beyond that: account/paid-plan requirements, latency ranges, parallel decomposition, gap[] behavior, citation_uri fetchability, contradictions[], and semantic excerpting. No contradiction with annotations exists.

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 and dense, but it is front-loaded with account requirements and the key alternative, and nearly every sentence adds selection or behavioral value. Some parenthetical sprawl and repeated mention of ask_pipeworx cost a point, but the structure is purposeful rather than padded.

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?

There is no output schema, so the description carries the return-value burden; it covers the findings packet fields, gaps[], contradictions[], hop, citation_uri, latency, and large-record excerpting. Combined with the annotations and full parameter schema, an agent has enough context to invoke the tool correctly and interpret its results.

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, but the description adds meaning beyond the schema by explaining depth behavior ('standard' re-angles unanswered gaps, 'thorough' chases leads), giving example research questions, and clarifying that broad/multi-part questions are appropriate. This lifts the parameter guidance above the structured fields alone.

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 names a specific action ('Grounded multi-source research'), a concrete resource ('Pipeworx's 1506 STRUCTURED data sources'), and distinct output behavior (findings packet with citations and gaps[]). It also explicitly contrasts itself with open-web search and with the sibling ask_pipeworx, so an agent can differentiate it from similar tools.

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 guidance ('Best for broad/multi-part questions over structured data') and when-not-to-use guidance: use ask_pipeworx for single lookups, for breaking/current news, and when not signed in. This is strong routing that names the alternative and the condition that selects it.

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