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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive. The description goes far beyond this by disclosing account/paid-tier requirements, expected latency (15-60s, up to ~90s for thorough), the gaps[] behavior (never invented), contradictions[] scan, semantic excerpting, hop field, and citation_uri resolvability. This gives the agent a realistic model of execution cost and output guarantees.

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 earns its place, and the most critical information is front-loaded: account requirement and fallback tool first, then the core capability, then usage guidance, depth semantics, output structure, and latency. It is dense with no filler or repetition of schema contents.

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?

Even without an output schema, the description fully covers what an agent needs: prerequisites, exact output shape (findings packet with evidence, confidence, source, fetched_at, citation, gaps[], contradictions[], hop), fallback behavior for unsupported topics, and performance expectations. Nothing essential for correct invocation or interpretation 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 coverage is 100%, so the baseline is 3. The description adds meaningful parameter semantics: it explains that depth:"thorough" incurs a paid plan, and it maps depth values to behavioral outcomes ('standard re-angles unanswered gaps', 'thorough additionally chases the best leads'), which directly helps an agent choose the right enum value. It also clarifies that the question parameter welcomes broad/multi-part natural language since decomposition is the point.

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?

States a specific verb ('research'), a precise resource ('Pipeworx's 1497 STRUCTURED data sources'), and explicitly disambiguates from open-web search. It also names what it is not (NOT open-web search) and contrasts with sibling tools like ask_pipeworx. An agent can clearly identify when this tool applies.

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?

Gives explicit when-to-use: best for broad/multi-part questions over structured data. Provides explicit when-not-to-use and alternatives: single lookups go to ask_pipeworx, breaking/current-news topics prefer ask_pipeworx because deep_research returns empty gaps[]. Also states a hard prerequisite: requires a signed-in account, otherwise use ask_pipeworx. This is model-guidance beyond what any schema could convey.

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