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

The annotations already mark it read-only and non-destructive, and the description adds substantial behavioral context: required sign-in, paid threshold for thorough, parallel decomposition, output packet contents, explicit gaps[] for unanswered facets, the never-invented guarantee, contradiction scanning for standard/thorough, semantic excerpting, and latency expectations. No statement conflicts with the 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 front-loaded with the most critical facts (account requirement, fallback tool) and each paragraph covers a distinct concern: eligibility, source grounding, use cases, depth behavior, citation fetchability, and latency. A few sentences are somewhat repetitive or over-detailed, but the structure keeps it navigable.

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 tool with no output schema, the description covers all essential operational context: authentication prerequisites, depth semantics, return format with per-finding fields, citation resolvability, gap handling, and runtime expectations. An agent has enough information to select it, authenticate, and understand what a successful invocation will produce.

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?

The schema already covers both parameters at 100% with meaningful descriptions, so the baseline is 3. The description adds value by explaining the behavioral meaning of depth levels (gap-recovery and lead-chasing hops) and by illustrating what kinds of natural-language questions the question parameter should receive.

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 names a precise verb and resource: it performs grounded multi-source research across Pipeworx's 1,517 structured data sources, decomposes questions, routes to tools, and returns a findings packet. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups, so an agent can tell it apart from siblings without inspecting schemas.

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?

It states exactly when to use the tool ('Best for broad/multi-part questions over structured data'), when not to use it ('For a single lookup use ask_pipeworx instead'), and what to do if signed out ('use ask_pipeworx instead — it works on every tier'). It also maps depth levels to appropriate effort, giving clear selection context.

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