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

The description goes far beyond the annotations: it explains that findings never invented (gaps[] disclosed), that citations are present only when resolvable, that records are semantically excerpted, that contradictions[] is returned for standard/thorough, and provides latency expectations (15-60s, up to ~90s). All of this is behavior not declared in the annotations, and it equips the agent to set user expectations correctly.

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 every sentence carries distinct operational information: auth and tier restriction, sibling routing, source count, decomposition behavior, return packet fields, gap handling, depth semantics, citation resolvability, excerpting, and latency. It front-loads the critical decision point (auth → ask_pipeworx fallback) before background detail, so an agent can act immediately.

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 burden of return-value disclosure, and it delivers: a findings packet with verbatim evidence, confidence, source, fetched_at, citation_uri, gaps[], contradictions[], and hop field. It also covers prerequisites (account, paid plan for thorough) and performance expectations. The only minor omission is a full structural example of a finding, but the field list is sufficient for an agent to reason about the result.

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?

The schema already documents both parameters at 100% coverage, so the baseline is 3. The description adds value by expanding the depth enum with concrete semantics (quick=3 facets single hop, standard=3 with gap recovery + contradictions scan, thorough=6 paid) and by confirming question can be broad/multi-part in natural language. These are clarifications that strengthen the schema rather than repeat it.

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?

Clearly identifies a specific verb and resource: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources ... in ONE call.' It immediately distinguishes itself from open-web search and names the sibling ask_pipeworx as the single-lookup alternative, so an agent understands exactly what the tool does and what it doesn't do.

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?

Explicitly states when to use this vs alternatives: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx instead.' Also gives representative multi-part question examples for positive use cases. This is textbook when/when-not/alternatives guidance.

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