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

Annotations already cover read-only and idempotent behavior, but the description adds substantial beyond-schema context: account/auth requirements, expected latency (15-90s), the gap[] behavior for unanswered facets, the guarantee never to invent findings, semantically excerpting rather than head-truncating large records, contradiction detection for certain depths, and the 'always fetchable' citation property. 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, but it front-loads the most critical operational constraint (account required; fallback to ask_pipeworx) before the core functionality. Every section adds meaningful information for an agent deciding whether and how to invoke the tool, though the run-on phrasing and density prevent it from being exemplary.

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 is remarkably complete: it covers input style, research behavior, output packet structure (evidence, confidence, source, fetched_at, citation URI), failure behavior (gaps, contradictions), auth requirements, depth semantics, and expected timing. An agent has everything needed to select and call 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; the description adds real semantic value by explaining what each depth level actually does (quick=single hop, standard=gap recovery + contradictions, thorough=paid iterative pass with lead chasing), and by clarifying that the question parameter is meant for natural-language, broad/multi-part questions. 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 names a specific verb ('research'), a specific resource (Pipeworx's 1517 structured data sources), and a mechanism (decomposes the question, routes facets to tools in parallel, returns a citations-backed findings packet). It also explicitly contrasts itself with open-web search and distinguishably positions itself against 'ask_pipeworx', making its identity clear to an agent.

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 conditions: use it when signed in and when dealing with broad/multi-part structured-data questions; if not signed in, use ask_pipeworx instead; for a single lookup, use ask_pipeworx. It also names the paid-tier requirement for 'thorough', so an agent knows exactly when to route elsewhere.

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