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

Annotations already declare readOnlyHint=true and idempotentHint=true, but the description adds substantial non-obvious behavioral context: parallel routing to 5,908 tools, decomposition into facets, explicit gaps[] for unanswered questions, contradictions[] on higher depths, semantic excerpting, latency expectations (15-60s, up to ~90s), and the account/tier requirement. This goes far beyond what annotations alone convey.

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?

Although the description is long, it is information-dense and front-loaded: account caveat and fallback tool appear first, then scope, mechanism, output format, and latency. Every sentence contributes distinct operational guidance; minor wording repetition does not dilute its efficiency.

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 research tool with no output schema, the description is remarkably complete: it covers authentication, fallback alternatives, scope, return packet composition, gap/contradiction handling, excerpting semantics, and expected runtime. The only missing piece is an exact JSON response shape, but the detailed findings-packet description makes this acceptable.

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% and the schema already provides per-parameter descriptions, including depth's enum meanings. The description adds meaningful detail on top: multi-part natural language questions are expected ('decomposition is the point'), depth maps to hop counts and a paid tier for 'thorough', and 'standard' is the default. This is more than simple schema restating.

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 and resource: 'Grounded multi-source research across Pipeworx's 1540 STRUCTURED data sources' with a defined deliverable ('verbatim evidence + confidence + source + fetched_at + a stable pipeworx:// citation'). It explicitly contrasts itself with open-web search and with ask_pipeworx for single lookups, clearly differentiating it from siblings like compare_entities and fact_search.

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 when-to-use and when-not-to-use guidance: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx'. It also specifies best-fit scenarios ('broad/multi-part questions over structured data') and depth-tier constraints, so an agent can select this tool versus alternatives with minimal ambiguity.

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