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digital-twin-mcp

digital-twin-mcp

by xici001
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

get_scene_snapshot

Get a snapshot of the digital twin scene, including coordinates, real-time metrics, and graded alerts for all devices in a single call.

Instructions

数字孪生场景快照：一次调用返回全部设备的坐标、实时指标与分级告警（专供可视化 / 监控轮询）。

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": "get_scene_snapshot"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.5/5.0
Behavior4/5

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

With no annotations, the description carries the full behavioral disclosure burden. It discloses a key behavioral trait—a single call returns all devices' data—and specifies the content categories (coordinates, real-time metrics, graded alerts). It does not detail potential payload size or data freshness, but for a zero-parameter read-only snapshot it provides meaningful transparency.

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?

The description is a single, well-structured sentence that front-loads the core purpose and includes the key differentiator ('one call returns all devices'). Every word contributes value; there is no redundancy or filler.

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

Completeness5/5

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

For a zero-parameter tool with an output schema and no destructive or mutating behavior, this description is complete. It states the return contents, scope, and recommended usage context, making it sufficient for an agent to select and invoke the tool correctly.

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 has zero parameters and an empty input schema, so the baseline is 4. The description adds no parameter-specific details because none are needed; all behavior is encapsulated in the fixed snapshot semantics.

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

Purpose5/5

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

The description uses a specific verb-plus-resource pattern: 'returns all devices' coordinates, real-time metrics, and graded alerts in one call.' It clearly distinguishes this aggregate snapshot tool from sibling tools like get_device_metrics and check_alerts by emphasizing the scene-wide scope.

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

Usage Guidelines4/5

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

The description explicitly states the intended use case: 'exclusively for visualization / monitoring polling.' This gives clear context for when to choose this tool, though it does not explicitly name sibling alternatives or state when not to use it.

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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