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

Annotations already convey read-only, idempotent, and non-destructive behavior. The description adds meaningful context beyond annotations: it reveals the tool returns 'category-bucketed example questions' and that data is 'drawn from the live catalog of thousands of tools', which implies dynamic content. This is useful behavioral detail without contradicting annotations.

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 detailed but well-structured, with the key purpose stated up front and usage rules following. The opening list of example queries (e.g., 'what can you do?') is slightly verbose but serves as useful NLP cues. Every sentence contributes, though the list could be trimmed for tighter conciseness.

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?

No output schema exists, but the description compensates by stating what is returned ('category-bucketed example questions') and their content ('exact tool + argument shape'). It covers purpose, usage, parameter behavior, and even alternatives, making it fully complete for an onboarding 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 describes `topic` with 100% coverage, so the baseline is 3. The description enriches this by providing concrete example values ('finance', 'pharma', 'betting') and explaining the effect ('focus' vs. 'full spread'), adding value beyond the schema.

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 the tool's purpose: an onboarding entry point that returns category-bucketed example questions with tool mappings. It uses a specific verb ('returns') and resource, and distinguishes itself from siblings by explicitly saying 'Use this FIRST' and naming meta-tools it teaches (ask_pipeworx, entity_profile, 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 clear usage context: '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 explains the parameter choice (no arguments vs. topic). It lacks an explicit when-not-to-use statement, but the 'FIRST' priority implies it's not for advanced users, so it's strong but not perfect.

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