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

Annotations already declare read-only, idempotent, open-world, non-destructive behavior, and the description adds substantial depth: account/tier requirements, facet decomposition, parallel tool routing, gaps[] for unanswered questions, citations that are always fetchable, contradictions[] for standard/thorough, semantic excerpting, and latency expectations. This meaningfully complements the structured 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 longer than average, but it is dense and front-loaded: auth, fallback, core purpose, and top use case appear first, with behavioral and latency details after. Some phrasing is slightly awkward, but nearly every sentence contributes non-redundant, decision-relevant information.

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 two-parameter tool with no output schema, the description is remarkably complete: it explains what the tool processes, how it decomposes questions, what the returned findings packet contains, how missing data is handled, what citations mean, and how long calls take. No essential selection or invocation context 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 description coverage is 100%, so the baseline is already solid. The description goes further by explaining what each depth tier costs and does, how 'thorough' differs from 'standard,' and what kind of question is appropriate for the question parameter, including concrete examples. That adds real value beyond 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?

The description states a precise action ('Grounded multi-source research') and names the exact resource domain: Pipeworx's 1517 structured data sources across SEC, FRED/BLS, FDA, USPTO, and more. It explicitly distinguishes the tool from open-web search and from sibling ask_pipeworx, so an agent can reliably tell when deep_research is the right tool.

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.' It also gives a clear exclusion: 'For a single lookup use ask_pipeworx instead,' and handles the auth prerequisite with 'If you are not signed in, use ask_pipeworx.' This leaves almost no ambiguity about routing.

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