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sejongjeong

Run:AI MCP Server

by sejongjeong

Execute Run:AI CLI

execute_runai_cli

Execute local Run:AI CLI commands for operations that require direct CLI access: port-forward, exec, attach, logs, and diagnostics.

Instructions

Run:AI CLI 전용 로컬 세션 작업 실행. API 대체 불가능한 작업만: port-forward (로컬 포트 → 워크로드 pod 터널링), exec (컨테이너 내 명령 실행: nvidia-smi, bash 등), attach (실행 중 프로세스에 TTY 연결), logs (로그 조회/스트리밍 --follow), diagnostics (클러스터 진단). 워크로드 생성/삭제/조회 등은 search_runai_api_spec + call_runai_api를 사용하세요.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
command_argsYesrunai CLI args (without 'runai' prefix)
timeout_secondsNoTimeout in seconds. Use 0 for port-forward.
Behavior3/5

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

With no annotations, the description carries the full burden for behavioral disclosure. It adds useful context like local session, port-forward tunneling, streaming logs, and --follow, but omits safety considerations, permissions, return value format, and error behavior for an execution tool that can run arbitrary commands inside containers.

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?

Two focused sentences: purpose comes first, followed by supported subcommands and an explicit pointer to sibling tools for other operations. No filler 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?

The description is strong on purpose and usage, but the tool is a CLI execution tool with no output schema, and the description does not mention return output, error handling, or exit behavior. That gap matters for invocation correctness. However, the supported-command scoping and API alternative guidance make it minimally viable.

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

Parameters4/5

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

Schema covers 100% of parameters, so baseline is 3. The description adds value by narrowing command_args to the allowed subcommands and giving concrete examples (nvidia-smi, bash, --follow). timeout_seconds is clarified in the schema with 'Use 0 for port-forward', but the description itself doesn't add timeout guidance 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?

Description clearly states it executes Run:AI CLI local session operations and enumerates specific subcommands (port-forward, exec, attach, logs, diagnostics). It also explicitly distinguishes from siblings like search_runai_api_spec and call_runai_api by limiting scope to API-irreplaceable tasks.

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?

Provides explicit guidance: 'API 대체 불가능한 작업만' (only tasks that cannot be replaced by API) and lists exactly when to use each subcommand. It also says to use search_runai_api_spec + call_runai_api for workload create/delete/query, naming alternatives clearly.

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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