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

The description goes well beyond the readOnly/openWorld/idempotent annotations by disclosing account requirements, paid-tier constraints, parallel tool routing, the findings-packet structure, gaps[] for unanswered facets, contradiction scanning, citation fetchability guarantees, semantic excerpting, and latency expectations. No contradictions with the annotations were found.

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?

Although the description is long, it is dense and front-loaded: account requirements and alternatives come first, followed by core purpose, output behavior, and latency. Every sentence adds operational value, and there is no filler or repetition.

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 compensates by detailing the findings packet, citation fields, gap behavior, contradiction handling, hop semantics, and expected execution times. Combined with the thorough input schema and annotations, an agent has enough information to invoke and interpret 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 description coverage is 100%, and the schema already fully documents both `question` and `depth`, including the meaning of quick/standard/thorough. The description mostly repeats or reinforces those semantics 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 identifies the tool as grounded multi-source research over Pipeworx's structured data sources, with a specific verb and resource. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups, so an agent can tell it apart 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?

It provides explicit when-to-use guidance: best for broad/multi-part questions over structured data, with concrete examples. It also gives an explicit alternative: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx instead.'

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