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

Annotations already indicate readOnly/openWorld/idempotent, and the description adds substantial behavior beyond them: parallel decomposition across 5,767 tools, gap[] reporting with no invented answers, stable fetchable citation URIs, semantic excerpting rather than head-truncation, contradiction scanning, latency expectations, and hop depth differences. This gives an agent a realistic model of what happens during the call and what the result contains.

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 it packs a high density of operationally relevant detail and leads with the most critical gate (account requirement and alternative route). Some clauses are dense and could be split for readability, but no sentence is filler and the structure moves from access constraints to core behavior to return-shape expectations.

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 authorization, pricing, tool selection, internal mechanics, result anatomy, edge cases (unanswerable facets), citation fetchability, and performance expectations. An agent has enough context to decide when to invoke deep_research and what to expect back.

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 description coverage is 100%, so the baseline is 3; the description adds extra value by binding depth levels to practical outcomes (standard vs thorough gap recovery, contradiction scan, paid requirement, approximate duration). It also clarifies that 'question' accepts broad/multi-part natural language, reinforcing the schema without simply repeating it.

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 specific action ('grounded multi-source research') and a specific resource (Pipeworx's 1506 structured data sources), and explicitly contrasts itself with open-web search. It also identifies the intended use case (broad, multi-part structured-data questions) and names sibling tools for adjacent cases, so an agent can distinguish deep_research from ask_pipeworx without opening 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?

The description gives clear selection criteria: use when the question is broad/multi-part over structured data; use ask_pipeworx for single lookups, breaking/current-news topics, or when not signed in. It also notes the account requirement and paid-plan constraint for 'thorough', providing explicit when-to-use and when-not-to-use guidance with named alternatives.

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