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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses substantial behavior: account and plan requirements, parallel routing, gaps[] for unanswered facets, contradictions[] in standard/thorough modes, resolvable citation URIs, semantic excerpting, and latency expectations. It even states findings are 'never invented,' which is critical grounding context. 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 front-loaded with the most critical operational facts (account requirement, fallback tool) before the detailed mechanics. There is minor redundancy in the depth-related details that also appear in the schema, but the added nuance justifies most of the length. Slightly verbose, yet each sentence earns its place for such a complex tool.

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?

With no output schema, the description carries the full burden of explaining return semantics, and it does: findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citations, gaps[], contradictions[], and hop fields. It also covers latency, depth variants, and sourcing scope, leaving an agent with everything needed to select and invoke the 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?

Though the schema already covers both parameters (100% coverage), the description adds real value: it explains the 'depth' tiers with behavioral differences (single hop vs gap recovery vs lead chasing), paid-plan implications, and that 'question' supports broad/multi-part natural language. This goes beyond the schema's enum description and helps an agent choose the right depth.

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 precise verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' and explains it decomposes questions into facets, runs tools in parallel, and returns a findings packet. It explicitly contrasts itself with open-web search and points to ask_pipeworx for single lookups, making its niche unmistakable among 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?

Provides explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead.' It also gives account-tier conditions ('if you are not signed in, use ask_pipeworx') and details depth options, so an agent can decide both whether and how to invoke the tool.

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