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

Annotations already mark this as read-only, open-world, idempotent, and non-destructive, and the description adds substantial behavioral detail: account requirements, parallel execution, findings-packet structure, gaps[] never being invented, hop/citation_uri fetchability semantics, contradictions[] on standard/thorough, semantic excerpting of large records, and expected latency. 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 opening is front-loaded and useful, but the description becomes seriously bloated by a long repeated 'Wait, the text after...' fragment and redundant restatement of material already in the schema. The mid-section is noisy and poorly structured, which hurts an agent's ability to quickly parse the most important guidance.

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 is very complete: it explains the data universe, decomposition and parallel routing, the contents of the findings packet, gap reporting, citation_uri fetchability, contradictions, excerpting behavior, auth requirements, and latency. An agent has enough to invoke it correctly and interpret results.

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 100%, so the baseline is 3. The description adds useful operational context beyond the schema, such as the purpose of the question parameter (broad/multi-part is fine), the timing expectations, and the behavioral differences between depth tiers. This exceeds the baseline but doesn't add dramatically new parameter-level detail.

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 clearly identifies the tool as a grounded multi-source research tool over Pipeworx's structured data sources, explaining that it decomposes questions and routes facets to tools in parallel. It explicitly distinguishes itself from open-web search and from ask_pipeworx for single lookups, making the purpose unambiguous.

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 gives explicit when-to-use guidance: best for broad/multi-part structured-data questions, and when-not-to-use: single lookups should go to ask_pipeworx, and unsigned-in users should use ask_pipeworx. It also notes that thorough depth requires a paid plan. This is model-level usage guidance.

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