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

The description goes far beyond the annotations: it discloses account/auth requirements, paid plan constraints, parallel decomposition, return packet structure (evidence, confidence, source, fetched_at, citation_uri), explicit gaps[], contradictions[], semantic excerpting, and expected latency. Nothing contradicts the readOnly, openWorld, idempotent, and non-destructive hints.

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 dense and almost every sentence carries substantive information. It front-loads the critical auth caveat and alternative tool, then moves through purpose, usage, depth behavior, output details, and latency. There is slight redundancy around pipeworx:// citations, but no filler or irrelevant content.

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 correctly takes on the burden of explaining the return format in detail. It covers auth, prerequisites, alternatives, depth semantics, latency, output fields, constraints, and failure behavior. For a complex research tool with two parameters, this is sufficiently complete for an agent to invoke it 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 coverage is 100%, so the baseline is 3. The description adds useful behavioral meaning beyond the schema: it clarifies what each depth level actually does in practice, explains latency differences, and emphasizes that the question parameter is suited to broad/multi-part inputs. This is a meaningful upgrade over the schema alone.

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 1,500 structured data sources in one call. It explicitly distinguishes itself from open-web search and says what it is NOT, which helps an agent differentiate it from siblings. It also gives concrete example questions ('compare X and Y's regulatory + financial exposure').

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?

Usage guidance is explicit: use ask_pipeworx when not signed in, use ask_pipeworx for single lookups, and use deep_research for broad/multi-part questions over structured data. It even explains depth-level behavior and the paid requirement for 'thorough', so an agent has clear selection criteria.

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