mcpField
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cerebras-code-mcp

cerebras-code-mcp

Local
github.com/kevint-cerebrasAI & Agents
Repository

About cerebras-code-mcp

Model Context Protocol (MCP) server for Cerebras to make coding faster in AI-first IDEs

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
  "mcpServers": {
    "kevint-cerebras-cerebras-code": {
      "command": "npx",
      "args": [
        "-y",
        "cerebras-code-mcp"
      ],
      "env": {
        "CEREBRAS_API_KEY": "<CEREBRAS_API_KEY>",
        "OPENROUTER_API_KEY": "<OPENROUTER_API_KEY>"
      }
    }
  }
}
Run commandnpx -y cerebras-code-mcp
Environment variables to set
CEREBRAS_API_KEYSecretYour Cerebras API key from cloud.cerebras.ai
OPENROUTER_API_KEYSecretOptional OpenRouter API key for fallback when Cerebras rate limits are hit

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npmcerebras-code-mcp1.3.1

Tools

1

Saved tool list · updated 2w ago

ParameterTypeRequiredDescription
promptstringYesREQUIRED: A comprehensive plan dump that MUST include: 1) EXACT method signatures and parameters, 2) SPECIFIC database queries/SQL if needed, 3) DETAILED error handling requirements, 4) PRECISE integration points with context files, 5) EXACT constructor parameters and data flow, 6) SPECIFIC return types and data structures. Be extremely detailed - this is your blueprint for implementation.
file_pathstringYesREQUIRED: Absolute path to the file (e.g., '/Users/username/project/file.py'). This tool will create or modify the file at this location.
context_filesarrayNoOPTIONAL: Array of file paths to include as context for the model. These files will be read and their content included to help understand the codebase structure and patterns.

Details

The maintainer has not added details yet. Check the README in the repository.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.