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

Annotations already declare read-only, idempotent, open-world, and non-destructive behavior. The description adds substantial context beyond that: the account/paywall requirement, latency envelope (15-60s, up to ~90s for thorough), parallel decomposition across 5,738 tools, explicit gaps[] with a 'never invented' guarantee, contradictions[] for standard/thorough depths, semantic excerpting rather than head-truncation, and the promise that citation_uri values are always fetchable. Rich behavioral disclosure with no contradiction to 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?

Front-loaded with the most decision-critical information: account requirement, fallback tool, and the NOT open-web-search disclaimer all appear before the mechanism details. Every clause carries information and little is wasted, but the first paragraph is a dense run-on and the 'use ask_pipeworx instead' routing guidance appears twice, making it slightly longer than necessary.

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 carries the full return-format burden and delivers: findings packet fields (verbatim evidence, confidence, source, fetched_at, pipeworx:// citation), gaps[], contradictions[], hop/citation_uri semantics, and the large-record excerpting behavior. For a high-complexity orchestration tool with auth tiers, paid depth levels, and variable latency, nothing an agent needs to call it correctly is missing.

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 schema already documents both question and depth well. The description adds meaningful value on top: it explains that 'thorough' requires a paid plan, gives latency expectations per depth, and provides worked example questions ('compare X and Y's regulatory + financial exposure') that demonstrate the expected natural-language breadth. Slightly above the baseline because the depth-related extra context is decision-relevant.

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 1499 STRUCTURED data sources' in ONE call. Explicitly distinguishes itself from both open-web search ('this is NOT open-web search') and the sibling tools it routes around ('For a single lookup use ask_pipeworx instead'). An agent can immediately identify this as the broad multi-facet research tool.

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 and when-not guidance with named alternatives: 'If you are not signed in, use ask_pipeworx instead — it works on every tier' and 'For a single lookup use ask_pipeworx instead.' Also states the positive condition: 'Best for broad/multi-part questions over structured data' with concrete example queries. Nothing is left to inference.

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