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

Annotations already declare readOnly/openWorld/idempotent, and the description adds behavioral detail: it returns category-bucketed examples drawn from the live catalog, supports optional topic filtering, and serves as a learning aid for meta-tools. No contradictions.

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 average, but it is front-loaded with example queries and each section (return format, topic parameter, usage context) earns its place. The list of categories is somewhat verbose but informative.

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 tool with one optional parameter and no output schema, the description is remarkably complete: it explains the return structure (category-bucketed examples with tool+argument shapes), the source (live catalog), and when to invoke it. There's no ambiguity about invocation or expected output.

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 covers the single optional 'topic' parameter 100% with allowed values. Description adds practical nuance: omitting topic yields a cross-category spread, while passing a topic focuses results, with concrete examples like 'finance' and 'betting'.

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 the onboarding entry point that returns category-bucketed example questions with the exact tool and argument shape. It uses a specific verb ('suggest_questions') and distinguishes from siblings by saying 'Use this FIRST' and referencing meta-tools like ask_pipeworx 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?

Explicit usage guidance is provided: '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 no-arg vs topic parameter choice and gives example queries.

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