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

The description fully discloses the tool's behavior: it returns example questions (not actual data), drawn from a live catalog, and can be focused by topic. It complements the annotations by detailing the output format and scope, making the tool's behavior clear beyond what readOnlyHint and idempotentHint convey.

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 dense and well-organized, front-loading the tool's purpose and then detailing usage. Although the leading list of example questions is slightly verbose, each sentence serves a purpose and the structure is logical.

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?

For a simple tool with one optional parameter and no output schema, the description is complete: it explains what is returned, how to invoke it, and when to use it. It goes beyond the schema and annotations to give a full picture, so the agent can use it without ambiguity.

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 single param `topic` with a description of valid values. The description adds examples ('finance', 'pharma', 'betting') but does not introduce new semantics beyond the schema's coverage, so a baseline 3 is appropriate.

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 is an onboarding entry point that returns category-bucketed example questions with exact tool and argument shapes. It distinguishes from siblings by positioning itself as the first stop for discovering what Pipeworx can do, unlike ask_pipeworx or discover_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?

Explicitly says 'Use this FIRST when you do not yet know what Pipeworx can do for you' and provides direction to learn about specific meta-tools. It also explains when to call with no arguments versus passing a topic, and implies it's for initial discovery rather than direct querying.

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