list_discovery_schedules

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

Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds behavioral context by explaining the return structure (fields) and parameter details. No contradictions, and the description complements the annotations well.

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 concise, uses a clear structure with an initial summary followed by Args and Returns blocks. Every sentence adds value; no redundant or vague statements.

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 that an output schema exists (context signals), the description still summarizes the return fields sufficiently. It covers all necessary aspects for a list tool: what it lists, filtering, pagination, and return format.

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 input schema has 0% description coverage (only titles), but the description's Args block explains all three parameters: active_only (default True, filters active), limit (range 1-1000, default 25), offset (pagination). This adds full semantic meaning 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 that the tool lists discovery schedules, specifies the underlying table (discovery_schedule), and gives examples of returned fields (name, status, frequency, IP ranges, CI groups). This clearly distinguishes it from sibling tools like get_discovery_status or get_discovery_errors, which focus on other aspects.

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 includes an explicit usage suggestion: 'Use this to understand what automated discovery is running and when.' It does not explicitly mention when not to use or alternative tools, but the context is clear enough for an agent to decide.

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