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

Annotations convey readOnly/openWorld/idempotent/non-destructive, but the description adds substantial behavioral context beyond those flags: account and paid-plan requirements, structured-data-only grounding with gaps[] never invented, parallel routing across 5,724 tools, hop/recovery behavior by depth, contradictions[] for standard/thorough, citation_uri fetchability guarantees, semantic excerpting, and latency expectations. No contradiction with the annotations exists.

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 carries decision-relevant information, and the most actionable constraints (account required, alternative tool, not open-web) are front-loaded. It is somewhat dense and stream-of-consciousness in places, but it earns its length given the tool's complexity and the lack of an output schema.

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 fully documents the return semantics: findings packet fields, gaps[], contradictions[], hop field, citation_uri guarantees, and latency. It also covers auth prerequisites, tier differences, and sibling routing. For a complex research tool this is as complete as an agent needs to select and invoke it correctly.

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, but the description adds meaningful depth semantics: it explains what depth:'standard' does (gap recovery), what depth:'thorough' additionally does (chases leads + contradictions scan), and that depth:'thorough' is paid. It also clarifies that 'question' can be broad/multi-part because decomposition is the point. This goes beyond the schema's enum descriptions.

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 1497 STRUCTURED data sources' that 'decomposes your question into focused facets' and routes them in parallel. It explicitly differentiates itself from open-web search and from ask_pipeworx, and clarifies it is for broad/multi-part structured-data questions. This is far beyond a tautology and gives an agent a precise mental model.

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 and when-not-to-use guidance: 'Best for broad/multi-part questions over structured data', 'For a single lookup use ask_pipeworx', and 'For BREAKING or colloquial CURRENT-NEWS... prefer ask_pipeworx'. It also names the exact alternative tool (ask_pipeworx) and adds an account/tier precondition with a fallback if not signed in. This leaves little to inference.

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