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

Annotations already declare readOnlyHint=true and idempotentHint=true, and the description adds substantial behavioral context: account/auth requirements, parallel decomposition, findings packet shape, gaps[] never invented, contradictions[] for standard/thorough, semantic excerpting, citation_uri only when resolvable, and expected latency 15-60s (up to ~90s). Nothing contradicts 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?

Long but information-dense; every sentence adds value. It front-loads the account requirement and the ask_pipeworx fallback before the core purpose, and later assigns behavioral details to depth levels. Some redundancy with the schema's depth descriptions exists, but the structure is logical and scannable. Minor trimming could improve it, but it earns its length.

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 complex research tool with no output schema, the description covers auth, alternatives, data scope, parallel decomposition, return packet fields, gap reporting, contradiction scanning, citation resolvability, excerpting behavior, and latency. An agent has everything needed to select and invoke the tool correctly, including edge cases like account tier and time expectations.

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 baseline is 3. The description adds meaningful depth-parameter context — paid plan for 'thorough', hop behavior, latency expectations — that goes beyond the schema's enum descriptions. It doesn't fully restate the quick/standard/thorough definitions, but it enhances rather than merely repeats.

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' and explicitly contrasts with open-web search. It clearly distinguishes from ask_pipeworx for single lookups and names the 'best for broad/multi-part questions' use case, so an agent can tell it apart from siblings.

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?

Explicitly gives when-to-use guidance: 'Best for broad/multi-part questions over structured data' and 'For a single lookup use ask_pipeworx instead.' It also names the alternative in the account-required case: 'use ask_pipeworx instead — it works on every tier.' This is clear routing with exclusions.

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