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

Annotations already declare readOnly, openWorld, idempotent, and non-destructive behavior, so the safety profile is covered. The description adds useful behavioral context: it returns results 'drawn from the live catalog of thousands of tools' (implying dynamic content) and provides the exact tool + argument shape for each suggestion, which is a key behavioral trait.

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 longer than typical, but it front-loads the purpose with a list of natural-language queries and packs every sentence with useful content (output structure, live catalog, usage, and relationship to meta-tools). It is well-structured with a list of categories and explicit guidance, earning a 4 despite its length.

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?

Given there is no output schema, the description fully explains what the tool returns (category-bucketed example questions with exact tool + argument shape), how to call it (no args or topic), and when to use it (first contact and learning meta-tools). It covers the tool's role, parameters, and return behavior comprehensively, making it complete for a discovery tool.

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 the 'topic' parameter completely (enum-like list of valid values and the behavior when omitted). The description adds minimal extra meaning beyond the schema, only giving a few examples ('finance', 'pharma', 'betting') and saying it focuses the results. This aligns with the baseline 3 for full schema coverage.

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 identifies the tool as the onboarding entry point for agents wanting to know what Pipeworx can do, explicitly listing example queries and the output (category-bucketed example questions with exact tool and argument shape). It distinguishes from siblings by positioning it as the 'FIRST' step before using meta-tools like ask_pipeworx, entity_profile, and compare_entities.

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 an explicit directive: 'Use this FIRST when you do not yet know what Pipeworx can do for you, or to learn how to call the meta-tools.' It also specifies argument usage ('Call with no arguments for the full spread, or pass topic') and names the meta-tools it teaches, clarifying when not to use it (i.e., when you already know what to ask or need direct data).

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