list_metrics

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

Without annotations, the description carries full responsibility for transparency. It discloses that the API rejects unrecognized names (case-sensitive), and that the output includes names and labels only unless includeDetails is set. This gives agents a clear picture of behavior and error conditions.

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, using four sentences that lead with the main purpose, then provide usage context and filter details. There is no redundant or extraneous information, and every sentence adds value.

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 the tool's simplicity (listing metrics) and the presence of detailed schema parameter descriptions, the tool description is complete. It explains the output behavior (names/labels vs details) and the discovery use case, covering all essential aspects for an agent to use it correctly.

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 description adds context beyond the schema by explaining how filters work (free-text search, category, dimension, granularity) and mentions case-sensitivity for search. It also clarifies that includeDetails controls output richness, which is not fully explained in the schema descriptions alone.

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 available metrics from the Roblox Analytics Query API, with specific mention of 168 metrics across 16 categories. It distinguishes itself as a discovery tool for when the exact metric name is unknown, which differentiates it from siblings like query_metric or describe_metric.

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 instructs to 'Start here when you do not already know the exact metric name', providing clear when-to-use guidance. It also explains filtering capabilities, which helps agents decide between this and other tools that operate on known metrics.

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