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

Annotations already declare the tool safe (readOnly, idempotent, non-destructive), so the description adds significant beyond-annotation context: auth/paid-tier requirements, structured-source limitation, decomposition and parallel routing behavior, return packet contents (evidence, confidence, citation_uri, hop), explicit gaps[] rather than invented answers, contradictions[] for standard/thorough, semantic excerpting, and latency. No contradiction with annotations exists.

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 front-loaded with the most decision-critical facts (account requirement, alternative, core purpose). There is some redundancy with the schema's depth enum text and a few run-on parentheticals, but every sentence carries actionable information for an agent.

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 fully explains the return structure: findings packet with verbatim evidence, confidence, source, fetched_at, stable pipeworx:// citation, explicit gaps[], contradictions[], and hop field. It also covers error behavior (never invents answers) and performance expectations (15-90s). An agent has everything needed to select and invoke the tool 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 extra meaning by explaining what depth values concretely do (single hop, gap-recovery hop, iterative chase for thorough) and confirms the question parameter is intended for broad/multi-part natural language. This is a meaningful semantic 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 states a specific resource and action: 'Grounded multi-source research across Pipeworx's 1517 STRUCTURED data sources' in a single call, with an explicit negative scope ('this is NOT open-web search'). It also distinguishes itself from ask_pipeworx ('For a single lookup use ask_pipeworx'), so the agent can tell the tools apart at a glance.

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?

Explicit when-to-use guidance is present: 'Best for broad/multi-part questions over structured data', plus clear alternatives with conditions: 'If you are not signed in, use ask_pipeworx instead' and 'For a single lookup use ask_pipeworx'. This gives both positive and negative selection criteria.

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