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

Even though annotations already declare read-only, idempotent, and open-world hints, the description adds substantial behavioral context: account/paywall requirements, decomposition and parallel routing, findings packet fields, gaps[] for unanswered facets, contradictions[] for certain depths, citation fetchability, large-record excerpting, and latency expectations. This goes well beyond what annotations provide.

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 severely overlong and repetitive: the sentence 'For a single lookup use ask_pipeworx instead — it works on every tier.' appears dozens of times, burying the genuinely useful content. This is not concise or well-structured; the unique content could be delivered in a fraction of the space.

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, so the description carries the full burden of explaining return values and constraints. It covers the findings packet, citation URIs, gaps[], contradictions[], account requirements, latency, and large-record handling. An agent has enough information 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 coverage is 100%, so the baseline is 3. The description adds meaningful extra meaning beyond the schema: 'thorough' requires a paid plan, depth values map to research hops and gap-recovery behavior, and the question parameter is explicitly suited to broad or multi-part natural language. This is useful enrichment, but not a full tier above because the schema already describes the parameters well.

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 clearly states what the tool does: grounded multi-source research over structured data sources, decomposed into facets, routed in parallel, returning a findings packet. It also explicitly distinguishes itself from open-web search and from ask_pipeworx, so an agent can tell it apart from siblings.

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?

The description gives explicit when-to-use guidance: best for broad/multi-part questions over structured data, and it directs users to ask_pipeworx for single lookups or when not signed in. It also notes a paid-plan requirement for 'thorough' depth, which is a practical selection criterion.

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