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

Beyond the readOnly/openWorld/idempotent annotations, the description discloses account and paid-plan requirements, parallel decomposition across thousands of tools, explicit gap[] behavior with never-invented findings, fetchable citation URIs, semantic excerpting, contradictions[] on certain depths, and expected latency. These are behavioral traits not visible in the annotations or schema. There is 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 front-loads the most decision-critical facts: account required, fallback alternative, structured-data scope, and best-use case. It is dense and information-rich, though the long parenthetical-heavy sentences and some repetition of depth behavior make it heavier than necessary. Every sentence contributes operational value, but it is not a model of concision.

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 two-parameter read-only research tool with no output schema, the description fully covers return shape, citation resolvability, gaps, latency, auth prerequisites, plan limits, and alternative tool routing. An agent can determine whether to invoke this tool and what to expect without needing additional context. The absence of an output schema is fully compensated by the description.

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 meaningful elaboration: concrete example questions for the question parameter and explanation of how depth levels translate into hops, gap recovery, contradiction scans, and paid gating. The schema already describes depth fairly well, so the description mostly reinforces rather than rescues unclear params.

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?

States a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call. It explicitly distinguishes itself from open-web search and from ask_pipeworx, which is for single lookups. The scope and output of the tool are immediately clear.

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?

Gives explicit when-to-use and when-not-to-use guidance: best for broad/multi-part questions over structured data, while a single lookup should use ask_pipeworx. It also provides a fallback condition: if not signed in, use ask_pipeworx because it works on every tier. This is strong, decision-relevant routing guidance.

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