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Sandbox Agent

Sandbox Agent

by eduresser
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

execute_terminal

Executes shell commands in an isolated sandbox for file management and dependency installation, returning stdout, stderr, and exit code.

Instructions

Runs a shell command inside the sandbox.

Useful for listing files, installing Linux dependencies, etc.

Args:
    session_id: ID returned by create_session.
    command: Shell command to execute (e.g. "ls -la", "apt-get install -y curl").

Returns:
    JSON with stdout, stderr, and exit_code.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "session_id": "<session_id>",
      "command": "<command>"
    },
    "name": "execute_terminal"
  }
}

Input Schema

NameRequiredDescriptionDefault
session_idYes
commandYes

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv3.0.0

TDQS

A4.6/5.0
Behavior4/5

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

With no annotations, the description carries full burden. It discloses that commands run inside a sandbox, requires a session ID from create_session, and returns stdout, stderr, and exit_code. It hints at mutability (installing dependencies) but could explicitly mention that the sandbox state persists across commands.

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 concise, with a clear purpose statement, usage context, parameter explanations, and return format. Every sentence adds value and is well-structured without fluff.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the two simple parameters and presence of an output schema, the description fully covers required context: it explains the sandbox environment, session prerequisite, typical use cases, and return structure. No gaps remain.

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

Parameters5/5

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

The schema has 0% description coverage, so the description fully compensates by explaining that session_id is the ID from create_session and command is a shell command with examples. This adds significant meaning beyond the bare 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?

The description clearly states the tool runs a shell command inside a sandbox, with examples like listing files and installing dependencies. It distinguishes itself from sibling tools like execute_code by focusing on shell commands, making its purpose unambiguous.

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 specific use cases (listing files, installing dependencies) and implies when to use it. However, it does not explicitly state when not to use it or mention alternative tools like execute_code for non-shell scripts, leaving some room for ambiguity.

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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