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

Annotations already cover readOnly/openWorld/idempotent, and the description adds substantial context on top: auth prerequisites (free GitHub sign-in, thorough requires a paid plan), latency expectations (15-60s, thorough up to ~90s), the return packet structure (verbatim evidence + confidence + source + fetched_at + citation), the 'never invented' gap[] guarantee, and the fetchability guarantee for citation URIs. No contradiction with annotations — the read-only, idempotent research behavior is consistent with the described design.

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 the tool is genuinely complex (3 depth modes, auth tiers, citation semantics, gap recovery) and every sentence carries distinct information — auth gate first, then alternative routing, then capability, then return format, then timing. It is front-loaded with the most decision-critical facts. It loses a point for being a single dense run-on paragraph with heavy parentheticals, which taxes an agent's parsing.

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 carries the full burden of explaining return values — and it does: findings packet fields, gaps[], contradictions[], hop field, citation_uri behavior, and semantic excerpting. Also covered are auth prerequisites, failure mode when the topic isn't in the catalog, latency, and alternative tools. For a tool of this complexity with zero output schema, nothing an agent needs to call it correctly or interpret its results 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?

Schema coverage is 100%, so the baseline is 3, but the description adds meaning beyond the schema: it ties depth modes to their behavioral consequences (which modes return contradictions[], hop behavior, timing, paid requirement for thorough) and clarifies the question parameter's intent ('Broad/multi-part is fine — decomposition is the point'). This goes beyond restating the schema.

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?

States a specific verb+resource — 'Grounded multi-source research across Pipeworx's 1506 STRUCTURED data sources' — and sharply distinguishes itself from open-web search ('this is NOT open-web search'). It also names what it is not relative to siblings by steering single lookups to ask_pipeworx. The purpose is unmistakable even before reading the schema.

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?

Gives explicit when-to-use ('Best for broad/multi-part questions over structured data') with concrete examples, and three explicit when-not-to-use routes: not signed in → ask_pipeworx; single lookup → ask_pipeworx; breaking/colloquial news → ask_pipeworx. The alternative-selection logic is fully spelled out, leaving nothing to inference.

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