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

Annotations already signal readOnly/idempotent/non-destructive, but the description goes far beyond: it discloses account requirements, paid-plan implications, parallel decomposition, findings packet contents, gap reporting, the 'never invented' guarantee, contradiction detection, semantic excerpting, and expected latency. This is a model of behavioral transparency and does not contradict the 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 it front-loads the critical account requirement and core differentiator. Some redundancy exists around citations and hops (mentioned twice in different forms), but every sentence contributes meaningful operational detail, which justifies the length 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?

Despite having no output schema, the description thoroughly describes the return envelope: findings with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop fields. It also covers latency, depth variants, and fallback tools, making the tool fully actionable for an agent without external documentation.

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%: both 'question' and 'depth' are fully described in the input schema. The description adds behavioral nuance about depth levels (e.g., gap recovery, contradiction scan), but the core parameter semantics are already carried by the schema, so the baseline of 3 applies.

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.' It also explicitly differentiates itself from ask_pipeworx for single lookups and breaking-news scenarios, so an agent can distinguish it from siblings without opening their schemas.

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, with concrete examples. It names ask_pipeworx as the alternative for single lookups and current-news topics, and even provides an account-level rule ('If you are not signed in, use ask_pipeworx instead'). This is exemplary routing guidance.

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