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

The description goes far beyond the annotations, disclosing account requirements, paid depth tiers, parallel routing across 5,798 tools, output contents (verbatim evidence, confidence, source, fetched_at, citations), the gaps[] behavior, contradiction scanning for standard/thorough, semantic excerpting rather than head-truncation, and latency expectations. The annotations only say read-only/open-world/idempotent; the description adds rich behavioral context without contradicting them.

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 contains all required information but is overly long and somewhat rambling, with awkward phrasing like 'Decomposes your question into focused facets, routes each to the right one of 5,798 tools IN PARALLEL, and returns a findings packet.' The account requirement is front-loaded before the core purpose, and the parentheticals make it harder to parse. It earns a middle score: valuable content, poor brevity.

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 thoroughly covers the full call lifecycle: prerequisites, auth tiers, input shape, internal behavior, output format, edge cases (gaps[] and contradictions[]), citation fetchability, and latency. There is no significant missing context an agent would need to invoke the tool 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 description coverage is 100%, but the description still adds meaningful value by explaining that 'question' can be broad and multi-part, and by detailing the depth parameter's behavior (quick=3 single hop, standard=3 with gap recovery and contradiction scan, thorough=6 paid with iterative lead-chasing). This adds semantics 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 clearly states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in ONE call, returning a findings packet. It also explicitly distinguishes itself from open-web search and names the sibling ask_pipeworx as the alternative for single lookups, making its 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 questions over structured data' and 'For a single lookup use ask_pipeworx instead.' It also provides an auth-based routing rule: 'If you are not signed in, use ask_pipeworx instead — it works on every tier.' This is strong, actionable routing information.

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