Skip to main content
Glama
Sora-bluesky

building-standards-act-mcp

by Sora-bluesky

使用量メトリクス

get_metrics
Read-onlyIdempotent

Returns server usage metrics—tool call counts, API requests, cache hit rate, and uptime—so you can monitor activity and performance.

Instructions

サーバーの使用量メトリクスを返す(ツール呼び出し回数・APIリクエスト数・キャッシュヒット率・稼働時間)

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault
apiYes
cacheYes
toolsYes
uptime_secondsYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changedv1.11.2
    • removedInput schema / $schema
      Removed value: -"http://json-schema.org/draft-07/schema#"
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
      +  "$schema": "http://json-schema.org/draft-07/schema#",
      +  "additionalProperties": false,
      +  "properties": {
      +    "api": {
      +      "additionalProperties": {
      +        "additionalProperties": false,
      +        "properties": {
      +          "avg_ms": {
      +            "type": "number"
      +          },
      +          "error_rate": {
      +            "type": "string"
      +          },
      +          "errors": {
      +            "type": "number"
      +          },
      +          "requests": {
      +            "type": "number"
      +          }
      +        },
      +        "required": [
      +          "requests",
      +          "avg_ms",
      +          "errors",
      +          "error_rate"
      +        ],
      +        "type": "object"
      +      },
      +      "propertyNames": {
      +        "type": "string"
      +      },
      +      "type": "object"
      +    },
      +    "cache": {
      +      "additionalProperties": {
      +        "additionalProperties": false,
      +        "properties": {
      +          "hit_rate": {
      +            "type": "string"
      +          },
      +          "hits": {
      +            "type": "number"
      +          },
      +          "misses": {
      +            "type": "number"
      +          }
      +        },
      +        "required": [
      +          "hits",
      +          "misses",
      +          "hit_rate"
      +        ],
      +        "type": "object"
      +      },
      +      "propertyNames": {
      +        "type": "string"
      +      },
      +      "type": "object"
      +    },
      +    "tools": {
      +      "additionalProperties": {
      +        "additionalProperties": false,
      +        "properties": {
      +          "avg_ms": {
      +            "type": "number"
      +          },
      +          "calls": {
      +            "type": "number"
      +          },
      +          "errors": {
      +            "type": "number"
      +          }
      +        },
      +        "required": [
      +          "calls",
      +          "avg_ms",
      +          "errors"
      +        ],
      +        "type": "object"
      +      },
      +      "propertyNames": {
      +        "type": "string"
      +      },
      +      "type": "object"
      +    },
      +    "uptime_seconds": {
      +      "type": "number"
      +    }
      +  },
      +  "required": [
      +    "uptime_seconds",
      +    "tools",
      +    "api",
      +    "cache"
      +  ],
      +  "type": "object"
      +}
  2. First observedv1.7.0

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description adds value by naming the metric categories returned, but says nothing about freshness, aggregation window, or scoping. With annotations carrying the safety burden, a 3 is appropriate.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

A single front-loaded sentence with the resource first and the payload enumerated in parentheses. Every element earns its place and nothing is padded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

An output schema exists, so return-value structure need not be described, and the description still previews the metric categories. Annotations cover the safety profile. The only gap is the absence of any usage context, which is minor for a zero-parameter read tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The tool takes zero parameters, which is the baseline-4 case. The description correctly implies no input is needed, and there is nothing further to document.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

Specific verb (返す) plus resource (使用量メトリクス), with an explicit enumeration of what is returned (tool call counts, API requests, cache hit rate, uptime). It is unmistakably distinct from the law-lookup siblings, though it never names or contrasts them, so it falls short of a 5.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no when-to-use guidance, no mention of prerequisites or timing (e.g. for monitoring or diagnostics), and no reference to any alternative tool. The agent must infer the use case entirely from the name.

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