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

Annotations already declare the tool read-only, open-world, idempotent, and non-destructive. The description adds substantial behavioral context beyond that: authentication requirements, paid tier for 'thorough', latency expectations, citation_uri only present when fetchable, explicit gaps[] for unanswered facets, contradictions[] for disagreeing findings, and semantic excerpting of large records. No contradiction with annotations is present.

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 dense but well-organized: critical account and alternative guidance is front-loaded, and technical detail is grouped by depth tiers and return format. It is longer than ideal and contains a few fragmented/overlapping sections (e.g., the 'in ONE call' and 'Second-hop iteration' phrasing), but every sentence carries useful information and no fluff.

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?

With no output schema, the description fully covers return values: findings packet fields, citations, gaps[], contradictions[], excerpting behavior, and expected timing. It also covers prerequisites, depth semantics, and failure behavior. For a tool of this complexity, nothing an agent needs to select or invoke it correctly is missing.

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?

Input schema coverage is 100%, so the baseline is 3. The description goes beyond the schema by explaining how depth values map to concrete behavior (quick=single hop, standard=gap recovery + contradictions, thorough=lead-chasing + recoveries + paid) and by clarifying that 'question' is expected to be broad/multi-part. This adds meaning the schema alone does not convey.

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 states a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' that decomposes questions into facets and returns a findings packet with verbatim evidence, confidence, source, and citations. It explicitly distinguishes itself from open-web search and from single-lookup tools like ask_pipeworx, so an agent can tell exactly what this tool is for.

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?

Provides explicit when-to-use guidance: 'Best for broad/multi-part questions over structured data' with concrete example queries, and 'For a single lookup use ask_pipeworx instead.' It also warns about account/tier prerequisites and directs unsigned-in users to ask_pipeworx, making the selection decision clear.

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