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gpu-mcp

gpu-mcp

by MYaelMendez
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

compile_kernel

Compile a CUDA matmul kernel using nvcc and MSVC vcvars for GPU execution.

Instructions

Compile a CUDA matmul kernel with nvcc (via MSVC vcvars).

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": "compile_kernel"
  }
}

Input Schema

NameRequiredDescriptionDefault
nameYeskernel namematmul

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.2/5.0
Behavior2/5

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

The description only states the action and toolchain but does not explain side effects (e.g., files created), required environment (e.g., CUDA toolkit installed), or possible errors. With no annotations, this is insufficient for an agent to understand behavioral impact.

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 with no unnecessary words, clearly stating the action and toolchain.

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?

The description lacks context about outputs, prerequisites, and workflow with sibling tools. It is a bare minimum but not complete for a tool that is likely part of a build-and-run pipeline.

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?

The schema already describes the parameter name and default. The tool description does not add additional semantics beyond what is in the schema, so it meets baseline but does not improve.

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 specific verb 'compile' and resource 'CUDA matmul kernel', and explains the toolchain (nvcc via MSVC vcvars). It distinguishes from sibling tools like run_kernel and probe_gpu.

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?

No guidance on when to use this tool versus siblings like run_kernel. No context on expected workflow (compile then run).

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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