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upscale-mcp

upscale-mcp

by sidney
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

device_info

Reports the PyTorch version, active compute device, and model root folders so you can verify local upscaling environment setup before running jobs.

Instructions

Report the torch version, the compute device in use, the roots and the models folders.

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": "device_info"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.3.0

TDQS

A3.5/5.0
Behavior3/5

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

No annotations are provided, so the description carries the full behavioral burden. "Report" implies a read-only, non-destructive introspection call, and the enumerated fields effectively describe what is returned, but it does not explicitly state that nothing is mutated or reveal any side effects or cost.

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 efficient sentence, front-loaded with the verb and listing the useful outputs. Every clause earns its place with no filler.

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 simple parameterless diagnostic tool with no output schema, naming the four reported values in the description adequately substitutes for a return-value spec. Minor gap: no indication of format or whether results are environment-dependent.

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 no parameter semantics are required; the empty schema is self-explanatory. Baseline for a parameterless tool applies.

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 states a specific verb ("Report") and enumerates the exact resources reported: torch version, compute device, roots, and models folders. It is clearly distinguishable from siblings like list_models or upscale as a diagnostic/introspection tool, though it does not explicitly name or contrast with those siblings.

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 guidance on when to call this tool versus alternatives, no prerequisites, and no exclusions. Usage is only implied by the diagnostic nature of the reported fields, leaving the agent to infer context.

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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