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Cerebras

Cerebras Code MCP Server

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Cerebras Code MCP Server v1.3.3

This MCP server is designed for planning with Claude Code, Cline, or Cursor 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 and Cline, with beta support for Cursor.

āœØ New in v1.3.3

  • 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

  • VS Code Support: Added install/remove support for VS Code

  • Update Config Wizard for Messy Configs: Added a removal wizard that helps uninstall

Related MCP server: mcp-agent-review

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 / Cline / VS Code (Copilot)

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. Removal/Cleanup (Optional)

cerebras-mcp --remove

Use the removal wizard to clean up configurations for any IDE or perform a complete cleanup.

5. 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

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

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

āœØ 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
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.
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.
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

A4.4/5.0
Behavior4/5

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

Despite no annotations, the description explains features like creating files, modifying with smart diffs, and supporting context files. It lacks details on destructiveness or error handling specifics, but covers key behaviors.

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

Conciseness3/5

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

The description is long and uses excessive formatting (emojis, all caps). While structured clearly, it contains redundant emphasis and could be more concise.

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 description covers many aspects but misses details about output/return values. Given no output schema, this is a gap. It also lacks information on failure modes.

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

Parameters5/5

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

Schema coverage is 100%, and the description adds valuable context, especially for the 'prompt' parameter with detailed instructions on what to include. This goes beyond the schema.

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 it is for code operations, including creation, modification, and generation. It emphasizes being the only interface for such tasks, distinguishing itself effectively.

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 provides explicit use cases and scenarios, such as writing new code or editing existing files. It also includes a warning not to edit files directly, offering clear guidance on when to use this tool.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 1 tool updatev1.3.3
    • First observedwrite
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TDQS

A4.2/5.0
Disambiguation5/5

Only one tool exists, so there is no possibility of confusion between tools. The single tool's purpose is clearly defined.

Naming Consistency5/5

With only one tool, naming consistency is trivially upheld. The name 'write' is a clear verb indicating action.

Tool Count3/5

A single tool for code writing is borderline acceptable, but it feels thin for a server that likely requires additional operations like reading or listing files.

Completeness2/5

The server lacks any read, list, or search tools, making it impossible to inspect existing code before modification. This gap will cause agent failures in typical workflows.

Maintenance

ActivityInactive
ResponsivenessSyncing

Resources

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