Decisify

execute_pyscipopt_sandbox

Run PySCIPOpt Python scripts in an isolated subprocess to test optimization models safely, returning exit code, stdout, stderr, and execution time.

Instructions

Direct sandbox execution tool for running Python / PySCIPOpt scripts in an isolated subprocess. Returns success flag, exit_code, stdout, stderr, and execution_time_seconds.

Input Schema

TableJSON Schema

Name	Required	Description	Default
`code`	Yes
`timeout_seconds`	No

Output Schema

TableJSON Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Sep 30, 2026

TDQS

B3/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully states that execution happens in an isolated subprocess and lists return fields, but omits sandbox boundaries, timeout behavior, resource limits, permissions, and potential side effects.

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?

Two efficient sentences, front-loaded with the core action and followed by return values. The return list is somewhat redundant given the output schema, but still 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 the core purpose and output shape for a code-execution tool, and an output schema exists. However, for a sandbox execution tool with no annotations, it lacks important context about timeout handling, sandbox restrictions, and when to choose it over similar sibling tools.

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%. The description implies the 'code' parameter is a Python/PySCIPOpt script, but says nothing about the 'timeout_seconds' parameter, its default, or behavior when exceeded.

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?

States a specific verb ('execution') and resource ('Python / PySCIPOpt scripts') with scope ('isolated subprocess'). It is clear what the tool does, though it does not explicitly differentiate itself from the sibling execute_model_code.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives such as execute_model_code. It implies use for PySCIPOpt scripts, but provides no conditions, prerequisites, or exclusions.

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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