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

Annotations already declare readOnlyHint=true, openWorldHint=true, idempotentHint=true, and destructiveHint=false. The description goes well beyond these by disclosing account/payment requirements, latency expectations (15-60s, thorough up to ~90s), the gap-recovery and contradiction scans, the guarantee that gaps are 'never invented', and the detail that citations are always fetchable. No contradiction with 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 dense but well-organized: account requirement, primary capability, when to use, output format, and depth-specific behavior. It front-loads the most critical gating information (account required, alternative if not signed in). It is long, but most sentences carry distinct value; a slight trim of repeated 'citation' details would improve it.

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?

There is no output schema, yet the description fully specifies the return value: findings packet with verbatim evidence, confidence, source, fetched_at, pipeworx:// citation, gaps[], contradictions[], hop, and citation_uri. It also covers latency, account tiers, and alternatives. For a complex tool with only two parameters, this is complete enough for an agent to select and 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 description coverage is 100%, so the baseline is 3. The description adds meaningful context beyond the schema: depth:'thorough' requires a paid plan, latency by depth, and what each hop does in practical terms. The question parameter is also contextualized as 'broad/multi-part is fine'. This pushes it above baseline.

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 uses a specific verb ('research') and resource ('Pipeworx's 1517 STRUCTURED data sources') and clearly distinguishes this tool from open-web search ('this is NOT open-web search') and from ask_pipeworx. It also explains the mechanism (decomposition, parallel routing to tools) and the deliverable (findings packet with citations and gaps).

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 tool: 'Best for broad/multi-part questions over structured data'. It also names the alternative for single lookups ('For a single lookup use ask_pipeworx instead') and includes an account-based routing rule ('If you are not signed in, use ask_pipeworx instead — it works on every tier'). This is strong, actionable guidance.

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