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

The description goes far beyond the readOnly/openWorld/idempotent annotations, disclosing decomposition behavior, parallel routing to 5,743 tools, the findings packet structure, explicit gaps[], 'never invented' hallucination policy, contradiction[] scans, semantic excerpting, latency expectations, and auth/paywall constraints. These are exactly the behavioral traits an agent needs to invoke and interpret the tool correctly.

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 information-dense and every major behavioral claim is relevant, but it is one long run-on paragraph with duplicated guidance: 'use ask_pipeworx instead — it works on every tier' appears twice. The purpose and usage guidance are not cleanly front-loaded, and the structure would be more scannable as separated usage, behavior, and output expectations.

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 the tool's complexity and the absence of an output schema, the description is remarkably complete: it covers return fields, citation resolvability, gap handling, contradictions, depth semantics, auth requirements, latency, and limitations. An agent has enough context to decide when to call it, how to set depth, what to expect back, and how to handle failure gaps.

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?

The input schema already covers both parameters at 100% coverage, including a detailed explanation of the depth enum. The description repeats much of this and adds some operational context like 'thorough needs a paid plan' and timing expectations, but it does not add substantially new meaning for the question or depth parameters themselves. Baseline 3 is appropriate because the schema carries the parameter documentation burden.

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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1500 STRUCTURED data sources' in ONE call, and explicitly distinguishes itself with 'this is NOT open-web search.' It also names concrete use cases like 'compare X and Y's regulatory + financial exposure,' which clarifies what the tool is for and how it differs from alternatives.

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 explicit when-not-to-use guidance with a named alternative ('For a single lookup use ask_pipeworx instead'). It even handles the auth edge case: 'If you are not signed in, use ask_pipeworx instead — it works on every tier.' This is model best-practice routing.

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