Retrieve all available metrics with point counts and originating services, plus the four-field key required to query a specific time-series.
Every instrument currently in the store, with point count and originating service. The four-field key (resourceHash, scopeName, name, kind) identifies a single time-series for query_metric_points.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The readOnlyHint and idempotentHint annotations already establish the safety profile. The description adds useful return-value context by naming the point count, originating service, and key fields, but does not cover pagination, ordering, or scale considerations for enumerating all instruments.
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 tightly written sentences front-load the tool's purpose and then explain the key linkage to query_metric_points. Every sentence carries useful information without repetition.
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 zero-parameter listing tool without an output schema, the description does a good job explaining the returned fields and the key structure. It could be slightly more complete by mentioning pagination or whether the full store is always returned, but the core context is present.
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?
This tool has zero input parameters, so there is no parameter semantics to explain. The baseline score for a parameterless tool applies.
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 this tool returns every instrument in the store along with point count and originating service, giving a specific resource and return content. It connects to query_metric_points but does not explicitly distinguish itself from list_metric_services, so sibling differentiation is incomplete.
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 second sentence implies that the returned four-field key is used by query_metric_points, which gives some usage context. However, it does not explicitly say when to use this tool versus list_metric_services or query_metric_points, leaving the guidance implied rather than stated.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.