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

Annotations already declare readOnlyHint and idempotentHint, so the bar for behavioral disclosure is lower, yet the description adds substantial context: account/paid-plan requirements, parallel decomposition into facets, routing to 5,798 tools, gaps[] for unanswered facets, contradictions[] for disagreeing findings, semantic excerpting (not head-truncated), citation fetchability guarantees, and expected latency. No contradictions 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 is long, but every sentence adds a specific fact about behavior, constraints, or return format. It front-loads the account requirement and the key differentiator (structured data, not open-web). It is dense rather than padded, though a slightly tighter structure could improve scannability.

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 fully discloses return values (findings packet with evidence, confidence, source, fetched_at, pipeworx:// citation), gap handling, contradiction detection, excerpting behavior, auth prerequisites, and runtime. Everything an agent needs to call it correctly and interpret results is present.

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?

Input schema covers 100% of parameters, so baseline is 3. The description enriches this by explaining depth semantics beyond the enum descriptions (quick=3 single hop, standard=3 adds gap recovery + contradictions, thorough=6 paid with iterative chase) and adds latency expectations per depth. It also clarifies that the question parameter is meant to be broad/multi-part, which reinforces the schema but adds practical context.

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 ('research') and resource ('Pipeworx's 1517 STRUCTURED data sources'), and explicitly contrasts with open-web search: 'this is NOT open-web search.' It also differentiates from sibling ask_pipeworx by noting this is for broad/multi-part questions over structured data, making selection unambiguous.

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?

Provides explicit when-to-use and when-not-to-use guidance: 'If you are not signed in, use ask_pipeworx instead,' and 'For a single lookup use ask_pipeworx.' It also describes the best use case ('broad/multi-part questions over structured data') and depth-tier tradeoffs, so an agent knows exactly when to invoke this tool vs alternatives.

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