Check which metric names exist in Prometheus, with an optional substring filter, to confirm metrics before querying and avoid misleading empty results.
List metric names known to this Prometheus, optionally filtered by a substring. Use this to verify a metric actually exists before concluding anything from an empty query result.
| Name | Required | Description | Default |
|---|---|---|---|
| contains | No | Optional case-insensitive substring filter |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It discloses the read-only nature implicitly ('List') and the substring filter behavior, but doesn't state whether the list is exhaustive, paginated, or limited in any way. For a simple read-only listing tool, this is adequate but not rich.
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?
Two sentences, zero waste. The core action and filter are front-loaded, and the usage guidance is a single actionable sentence. Every word earns its place.
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 single-parameter listing tool with no output schema, the description covers the action, the filter, and the use case. It doesn't describe the return format, but for a list of metric names that's a minor gap given the tool's simplicity.
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 description coverage is 100%, so the schema already documents the 'contains' parameter as an optional case-insensitive substring filter. The description adds the purpose context (verifying metric existence) but no additional parameter-level detail 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 states a specific verb ('List') and resource ('metric names known to this Prometheus'), and adds the optional substring filter. It clearly distinguishes itself from siblings like instant_query and range_query, which execute queries rather than enumerate metric names.
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 tells the agent when to use this tool: to verify a metric actually exists before concluding anything from an empty query result. This is clear usage guidance that also implies when not to use it (when you need actual values, use instant_query or range_query instead).
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.