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dsh-mcp-worker

dsh-mcp-worker

by tongsh6
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

查看运行指标

dsh_metrics
Read-only

Inspect task counts, latency percentiles, DSH token totals, concurrency limits, queue status, and backend/OTel health to guide leader adaptation and verify token savings and speed.

Instructions

返回任务计数、耗时分位（avg/p50/p95/max）、DSH 侧 token 汇总、并发限制、排队情况、后端与 OTel 状态；用于 leader 自适应与「省 token/提速」核对。

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "dsh_metrics"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.7.2

TDQS

A3.5/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true and openWorldHint=false, so the safety profile is covered. The description adds that the payload is a read-only snapshot spanning multiple subsystems, but it does not disclose cost of the call, freshness/caching of metrics, or whether percentiles cover a fixed window.

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

Conciseness4/5

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

A single dense sentence front-loads the main purpose and then lists returned sections; nothing is redundant. It is terse to the point of being a run-on list, but every clause maps to real output content.

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

Completeness3/5

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

With no output schema, the description does carry the burden of explaining returns, and it does list the metric families. Still, it omits field naming, time-window semantics, and how to interpret the OTel/backend status values, leaving gaps for an agent that must consume the result.

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, so per the rubric the baseline is 4; there is no parameter ambiguity to resolve. The description's enumeration concerns return content rather than inputs, which is appropriate given the empty schema.

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?

The description names a specific verb+resource (返回运行指标) and enumerates concrete contents: task counts, latency percentiles (avg/p50/p95/max), DSH-side token totals, concurrency limits, queue state, backend and OTel status. This distinguishes it from siblings like dsh_status or dsh_tasks, though it does not explicitly say how it differs from dsh_status.

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

Usage Guidelines3/5

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

It gives an implied usage context — leader self-adaptation and verifying token savings/speedups — which hints at when to call it. However, there is no explicit when-not guidance and no named alternative such as dsh_status for narrower state checks, so routing relies on inference.

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

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