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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses a great deal: account and paid-tier requirements, parallel decomposition of the question, the findings packet structure (verbatim evidence, confidence, source, fetched_at, citation), explicit gaps[] with 'never invented', citation_uri resolvability conditions, semantic excerpting behavior, and expected latency. These are material behavioral traits not available from annotations or schema.

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 densely informative; nearly every clause adds operational context such as auth, fallback tool, non-open-web scope, depth behavior, output contents, citation guarantees, and timing. It is not perfectly front-loaded because it starts with account requirements rather than the core purpose, but it is structured well enough for a high-complexity 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?

For a tool with no output schema, the description fully covers what an agent needs to invoke it correctly: auth prerequisite, paid depth tier, appropriate question types, return packet details, gap/citation guarantees, depth semantics, and expected latency. Nothing critical is missing.

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%, and the input schema already fully explains both `question` (natural language, broad/multi-part fine) and `depth` (quick=3, standard=3 with gap recovery, thorough=6 paid). The description mostly restates these semantics in prose rather than adding new parameter-level meaning, so the baseline of 3 is appropriate.

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 clearly states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call.' It distinguishes itself from open-web search ('this is NOT open-web search') and from sibling lookup tools, so an agent can identify when this tool applies.

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 says when to use it ('Best for broad/multi-part questions over structured data') and when not to ('For a single lookup use ask_pipeworx instead'). It also names a concrete fallback when the account requirement is unmet: 'If you are not signed in, use ask_pipeworx instead — it works on every tier.'

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