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local-llm-mcp

local-llm-mcp

by Qinlupfu
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

Server Status

server_status

Check whether a local llama-server is running, healthy, which GGUF models are loaded, and how long it has been idle to inform auto-unload or restart actions.

Instructions

查询本地模型服务状态（进程/健康/加载的模型/闲置计时）。

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": "server_status"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.0

TDQS

A3.7/5.0
Behavior3/5

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

With no annotations, the description carries the burden, and it does disclose what status facets are reported (process/health/models/idle timer), which implies a safe read. However, it says nothing about cost, whether it starts or touches the server, or latency/blocking behavior, so the disclosure is only partial for a zero-annotation tool.

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 verb and resource first and the returned facets compactly parenthesized. No redundant or filler text.

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?

For a zero-parameter status query with an output schema that documents the return shape, the description is nearly sufficient: it tells the agent what the call reports. Only the boundary with ping/runtime_info/list_models is left implicit.

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 baseline there is no parameter semantics to convey. The description correctly adds no parameter claims and simply describes the query.

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?

States a specific verb (query/查询) and resource (local model service status) and enumerates the facets returned: process, health, loaded models, idle timer. This distinguishes it from siblings like ping and runtime_info, though the mention of 'loaded models' overlaps slightly with list_models without clarifying the boundary.

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?

Usage is implied — an agent would call this to check whether the local model server is up and what it holds — but the description names no conditions, prerequisites, or alternatives among ping/runtime_info/list_models. Adequate but with a clear gap in routing guidance.

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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