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

Annotations already provide read-only, open-world, idempotent, and non-destructive hints, so the bar is lower. The description adds meaningful behavioral context beyond annotations: it explains the return structure (category-bucketed examples with tool+argument shapes), that results are drawn from a live catalog, and that the tool is an onboarding entry point. This is more substantial than typical read-only descriptions, though it doesn't cover edge cases like invalid topics or response format limits.

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?

Though longer than minimal examples, every clause earns its place: it front-loads with user-style queries, explains the return value, gives parameter guidance, and includes usage context. The structure is a compact paragraph that packs all essential information without redundancy.

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 a single optional parameter, rich annotations, and no output schema, the description fully covers the tool's purpose, return content, parameter semantics, and when to use it. It also contextualizes the tool within the broader catalog by referencing meta-tools. No critical gaps remain.

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 mentions topic with examples ('finance', 'pharma', 'betting') and says omitting it gives a cross-category spread, but the schema itself already describes the same focus areas and the omit behavior. Thus the description adds little meaning beyond what the schema provides.

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: it is the onboarding entry point for an agent that wants to know what to ask Pipeworx, returning category-bucketed example questions with exact tool and argument shapes. It also lists natural-language triggers (e.g., 'what can I ask Pipeworx?') and distinguishes its role from other tools by mentioning how to learn to call meta-tools.

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 explicitly says to use this FIRST when the agent doesn't know what Pipeworx can do or needs to learn meta-tool usage. It provides clear call guidance (no args for full spread, pass topic to focus) and names alternative/related tools (ask_pipeworx, entity_profile, compare_entities), giving strong contextual direction for when this tool is appropriate.

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