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khansabassem

Cerebras Multi-Model MCP Server

by khansabassem

cerebras_quick

Generate code quickly for simple tasks like edits, boilerplate, or single functions. Uses a fast model to prioritize speed for trivial code generation.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYesREQUIRED: Detailed code generation instructions. Include method signatures, data structures, error handling requirements, and integration details.
file_pathYesREQUIRED: Absolute path to the file to create or modify.
max_tokensNoOPTIONAL: Maximum tokens in the response.
temperatureNoOPTIONAL: Sampling temperature (default 0.1).
context_filesNoOPTIONAL: Array of file paths to read as context for the generation.
Behavior2/5

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

No annotations are provided, so the description must disclose behavioral traits. It mentions speed and model size but does not reveal that the tool likely writes to files (based on file_path parameter) or any side effects, permissions, or rate limits. Key behavioral information is missing.

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

Conciseness5/5

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

The description is extremely concise with three short sentences, front-loading the purpose and usage. No unnecessary words or repetition.

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?

Given the lack of output schema and 5 parameters, the description is brief. It does not explain the tool's action (e.g., whether it writes files directly or returns code) or what the output looks like. Basic contextual details are missing.

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 no significant meaning beyond the schema; it merely restates the tool's speed. The schema already adequately describes the parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool is for fast code generation using a specific model, targeting simple edits, boilerplate, and single-function generation. It distinguishes from siblings by emphasizing trivial tasks where speed matters, though it does not explicitly contrast with other cerebras tools.

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

Usage Guidelines4/5

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

The description provides explicit guidance to use this tool for trivial tasks where speed is most important. It implies alternatives exist for more complex tasks but does not name them or specify when not to use it.

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

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