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romeo_pip_install

Installs Python packages into a virtual environment on the correct architecture node, preferring precompiled wheels from build_wheel to avoid native recompilation and x86_64 login-node builds.

Instructions

Installe des paquets Python dans un environnement virtuel, sur un noeud de la bonne architecture, en privilegiant les roues deja compilees par build_wheel. Evite de recompiler les extensions natives et n'utilise jamais le noeud de login, dont les roues seraient en x86_64.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
archNoarmgpu
confirmNo
minutesNo
env_pathYes
packagesYes
extra_flagsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.4.0

TDQS

A3.7/5.0
Behavior3/5

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

Annotations already declare this is a write operation (readOnlyHint=false) that is not destructive. The description usefully adds that execution is routed to a compute node of the matching architecture and never the login node, which is real operational context. However it says nothing about what happens on failure, whether the environment is mutated in place, or how confirmation/timeouts behave.

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

Conciseness4/5

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

A single dense sentence that front-loads the action and the architecture constraint, with the login-node caveat at the end. Efficient and free of filler, though the multiple subordinate clauses make it slightly harder to scan than a two-sentence split would be.

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?

An output schema exists, so return values need not be described. Still, for a mutating installer with 6 parameters at zero schema coverage, the description leaves the agent without semantics for confirm, minutes or extra_flags, which matters for calling it correctly.

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

Parameters2/5

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

Schema description coverage is 0% across 6 parameters, so the description must carry the burden and does not. It only indirectly conveys the packages and arch concepts ('noeud de la bonne architecture'); env_path, confirm, minutes and extra_flags are entirely undocumented in both schema and description.

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?

States a specific verb and resource (installs Python packages), the target scope (a virtual environment, on a node of the correct architecture), and names the sibling tool build_wheel as the source of preferred pre-built wheels. An agent can distinguish this from build_wheel, build_on_node and run_login_command without opening a schema.

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?

Gives clear operational guidance: prefer wheels already compiled by build_wheel, avoid recompiling native extensions, and never use the login node because its wheels are x86_64. It names an alternative (build_wheel) but does not spell out the inverse condition (when to skip pip install and call build_wheel directly), so it stops short of explicit when/when-not routing.

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