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

Annotations already cover the read-only, open-world, idempotent, non-destructive profile, and the description adds substantial context beyond that: facet decomposition, parallel routing to 5,798 tools, findings packet structure, explicit gaps[] with never-invented evidence, hop fields, fetchable citation_uri, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. 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 is long and dense, but nearly every sentence carries unique information — account requirements, alternative routing, return format, gap behavior, depth semantics, citation fetchability, excerpting, and latency. It earns its length given the tool's complexity, though it would benefit from clearer visual structure (e.g., paragraph breaks) to 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?

Despite there being no output schema, the description fully specifies what the agent will receive: findings with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop fields. It also covers prerequisites (account), runtime expectations, depth variations, and when to pick a sibling tool. Nothing necessary for correct invocation or expectation-setting 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 baseline is 3. The description adds meaningful depth semantics beyond the schema: it clarifies the question parameter accepts broad/multi-part queries and that decomposition is the point, and it elaborates on how 'standard' and 'thorough' differ in hop behavior and contradiction scanning. This is enough to push above baseline, though the schema already documents the enum values well.

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 precise verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in one call, and explicitly distinguishes itself from open-web search. It also names the specific alternative (ask_pipeworx) for cases where it shouldn't be used, making sibling differentiation 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?

Provides explicit when-to-use and when-not-to-use guidance with named alternatives: use ask_pipeworx if not signed in, for a single lookup, or for breaking/current news; deep_research is best for broad/multi-part structured-data questions. This is the strongest possible usage guidance, leaving no inference to the agent.

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