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Cerebras Code MCP Server v1.2.0

This MCP server is designed for planning with Claude Code and making changes with Cerebras to maximize speed and intelligence while avoiding API limits. Use your preferred AI for planning and strategy, then leverage Cerebras for high-quality code generation.

It will use the Qwen 3 Coder model, and can be embedded in IDEs like Claude Code, with beta support for Cursor.

✨ New in v1.2

  • Project Restructure: Organized project into smaller, more manageable components for DX purposes

  • Stronger Instruction: Improved write usage count among models

  • Claude Code - Enhanced Visual Diffs: Displays changes/edits in a pretty format

  • Hide User API Key: For security, doesn't display entered API keys in the terminal

  • Update Config Wizard for Messy Configs: Ensure user setup always works despite previous installs

Related MCP server: mcp-gemini-assistant

1. Install the NPM Package

npm install -g cerebras-code-mcp

2. Get Cerebras API key

Visit cloud.cerebras.ai and create an API key

[OPTIONAL] Add OpenRouter as a backup in case you hit your Cerebras rate limits Visit OpenRouter and get a key to use as a fallback provider.

You can set this key in your MCP settings under OPENROUTER_API_KEY, and it will trigger automatically if anything goes wrong with calling Cerebras.

3. Run the Setup Wizard for Claude Code / Cursor

cerebras-mcp --config

Use the setup wizard to configure the tool on your machine.

If you're using Cursor, it will ask you to copy and paste a prompt into your Cursor User Rules.

4. Usage

The MCP tool will appear as write in your tool list. It supports:

  • Natural language prompts: Just describe what you want in plain English

  • Context files: Include multiple files as context for better code understanding

  • Visual diffs: See changes with Git-style diffs

Example usage:

Create a REST API with Express.js that handles user authentication

Available Tools

1 tool
writeA

🚨 MANDATORY CODE TOOL - USE THIS FOR ALL CODE OPERATIONS! 🚨

This is your ONLY interface for code generation, file creation, and modifications. Never edit files directly!

✨ FEATURES:

  • Creates new files automatically

  • Modifies existing files with smart diffs

  • Shows visually enhanced git-style diffs with emoji indicators (āœ… additions, āŒ removals, šŸ” changes)

  • Supports context_files for better code understanding

  • Handles all programming languages

  • Provides comprehensive error handling

šŸŽÆ USE CASES:

  • Writing new code: Use with file_path + detailed prompt

  • Editing code: Use with file_path + modification prompt

  • Code generation: Use with file_path + generation prompt + optional context_files

āš ļø REMEMBER: This tool is MANDATORY for ALL code operations!

ParametersJSON Schema
NameRequiredDescriptionDefault
promptYesREQUIRED: 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_pathYesREQUIRED: Absolute path to the file (e.g., '/Users/username/project/file.py'). This tool will create or modify the file at this location.
context_filesNoOPTIONAL: 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.

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description must carry full behavioral transparency. It discloses that it creates new files automatically, modifies with smart diffs, shows emoji-indicated diffs, supports context_files, and handles all languages with error handling. This is substantial, though it omits details like overwrite behavior or output format, so it's not a perfect 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness2/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is overly verbose and repetitive. It uses urgent all-caps ('MANDATORY', 'ONLY interface'), multiple emoji sections, and repeats the same ideas (e.g., 'code generation' appears in features and use cases). Several sentences add little value, such as 'Handles all programming languages' and the final reminder. This could be cut to a few concise sentences.

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

Completeness3/5

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

The tool has 3 parameters, no output schema, and no annotations. The description covers features, use cases, and error handling, so it is fairly complete for understanding what the tool does. However, it never explains what the tool returns or what the result of a call looks like (e.g., success message, applied diff), which is a notable gap given the absence of an output schema.

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%, so baseline is 3. The description adds marginal value by mentioning that context_files are for 'better code understanding' and that file_path + prompt should be used together, but it mostly reiterates what's already in the schema. No significant new semantic information beyond the schema is provided.

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 clearly states that this tool is for code generation, file creation, and modifications, with a specific verb and resource. It also explicitly positions itself as the only interface for code operations, leaving no ambiguity about its purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit when-to-use guidance: 'USE THIS FOR ALL CODE OPERATIONS' and lists concrete use cases (writing new code, editing, code generation). It also provides a when-not-to-use instruction: 'Never edit files directly!' This is strong usage direction even though no sibling tools exist.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

TDQS

A3.9/5.0
Disambiguation5/5

With only one tool, there is no possibility of confusion between tools. The tool's purpose is clearly defined as the sole interface for code operations.

Naming Consistency5/5

The single tool named 'write' follows a simple verb convention. With only one tool, there are no inconsistencies to assess.

Tool Count2/5

A server dedicated to code operations with only one tool is too few. The scope of the server suggests the need for additional tools like reading, searching, or deleting code, making this count insufficient.

Completeness2/5

The tool only handles writing and modifying files. It lacks essential operations like reading, deleting, or listing code, resulting in significant gaps for a code-focused server.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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