RespCode

Provides multi-architecture code execution via Claude Desktop through RespCode MCP using a local runtime and an API key.

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5 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "respcodeai-respcode-mcp": {
      "command": "respcode-mcp",
      "args": [],
      "env": {
        "RESPCODE_API_KEY": "your-api-key-here"
      }
    }
  }
}

You can run RespCode MCP from your local environment to enable multi-architecture code execution for Claude Desktop. This MCP server lets Claude generate, run, and compare code across architectures by delegating tasks to the RespCode MCP runtime, activated through a simple, secure API key integration.

How to use

Set up the RespCode MCP server in your Claude Desktop workflow to enable generation, execution, and evaluation of code across architectures. You will install the RespCode MCP package, provide your API key, and configure Claude Desktop to communicate with the local RespCode MCP runner. Once configured, you can use Claude Desktop tools to generate code, execute it on different architectures, and compare results across models or configurations.

How to install

Prerequisites: ensure you have Python and pip available on your system.

pip install respcode-mcp

Additional sections

Configure the RespCode MCP server in Claude Desktop by adding the following MCP server entry to your Claude Desktop configuration file. This enables Claude Desktop to communicate with the local RespCode MCP runtime using your API key.

{
  "mcpServers": {
    "respcode": {
      "command": "respcode-mcp",
      "env": {
        "RESPCODE_API_KEY": "your-api-key-here"
      }
    }
  }
}

Notes and security

Keep your API key secure. Store it in a safe location and do not share it. Restart Claude Desktop after saving the MCP server configuration to ensure the runtime is loaded with the new settings.

Available tools

generate

AI generates and executes code across architectures

execute

Run your own code on the chosen architecture

compete

Compare 4 AI models on the same task

collaborate

Models refine each other to improve results

consensus

Best-of-4 selection to choose the top result

history

View past prompts and results

credits

Check MCP balance or credits