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gpu-mcp

gpu-mcp

by MYaelMendez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

run_kernel

Execute a compiled CUDA kernel on the local GPU to leverage hardware acceleration.

Instructions

Run a previously compiled CUDA kernel on the local GPU.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

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

Input Schema

NameRequiredDescriptionDefault
nameYeskernel namematmul

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior2/5

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

No annotations are provided, so the description carries full burden. It states the basic operation but lacks disclosure of important behavioral traits such as error handling, resource impact, or whether it is destructive.

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 sentence that is concise and front-loaded with the action. Every word serves a purpose with no unnecessary information.

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

Completeness2/5

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

Given the tool is a simple execution operation, the description lacks details about output, error states, and prerequisites beyond 'previously compiled'. It is incomplete for practical use without additional context.

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

Parameters3/5

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

Schema description coverage is 100% with a single parameter 'name' described as 'kernel name'. The description does not add extra meaning beyond what the schema already provides, so baseline score of 3 is appropriate.

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 'Run' and clearly identifies the resource 'previously compiled CUDA kernel' with context 'on the local GPU'. It effectively distinguishes from siblings like compile_kernel.

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?

The description implies the tool is for running kernels that have already been compiled, contrasting with compile_kernel. However, it does not explicitly state when to use or not use this tool, nor mention alternatives.

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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