Cerebras Multi-Model MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| CEREBRAS_API_KEY | Yes | Your Cerebras API key from https://cloud.cerebras.ai/ | |
| OPENROUTER_API_KEY | No | Optional OpenRouter API key for fallback |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| cerebras_quickA | Fast code generation using Cerebras gpt-oss-120b (120B params). Fast model for simple edits, boilerplate, single-function generation. Use for trivial tasks where speed matters most. |
| cerebras_complexB | Heavy-duty code generation using Cerebras gpt-oss-120b (120B params). Large model for multi-file features, CRUD APIs, complex components, and demanding code generation tasks. |
| cerebras_reasoningA | Advanced reasoning code generation using Cerebras zai-glm-4.7 (355B params, reasoning_format:hidden). Most powerful model for algorithms, architecture decisions, advanced logic, and tasks requiring deep reasoning. |
| cerebras_instructB | Instruction-following code generation using Cerebras zai-glm-4.7 (355B params, reasoning_format:hidden). Instruction-tuned model for precise instruction following, documentation-heavy code, typed interfaces, and detailed specs. |
| cerebras_autoA | Auto-selects the best Cerebras model based on prompt complexity. Simple tasks use 8B, complex features use 120B, reasoning tasks use 357B, documentation tasks use 235B. Use this when unsure which model fits. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 5 tools
Each tool targets a distinct use case: auto-selection, heavy code, instruction-following, fast generation, or deep reasoning. Descriptions clearly separate model sizes and tasks, eliminating ambiguity.
All tool names follow a consistent 'cerebras_<adjective>' pattern using snake_case. The descriptors (auto, complex, instruct, quick, reasoning) clearly indicate the tool's purpose.
With 5 tools, the server covers the main model variants without being excessive. The count is well-scoped for a model selection server, providing essential choices without redundancy.
The tool surface covers auto-selection, heavy tasks, instruction-tuned, fast, and reasoning use cases. One minor gap: no explicit 'chat' or 'vision' tool, but the domain appears focused on code generation, so the set is largely complete.