agentic-compute-mcp

execute_code_securely

Run Python code securely in a remote Azure sandbox with automatic x402 micropayments. Evaluate algorithms, process data, and execute custom scripts without local dependencies.

Instructions

Executes Python code in a remote, isolated Azure sandbox environment with automatic x402 payment handling.

Use this tool to safely evaluate Python algorithms, process data structures, perform math calculations, or run custom scripts.

Usage Guidelines:

Code must be standard Python 3.
Script execution is subject to a 30-second timeout limit; avoid infinite loops or blocking operations.
Ensure all required imports are included within the snippet.
x402 micropayments (USDC on Base) are automatically verified per execution call.

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": {
      "code": "<code>"
    },
    "name": "execute_code_securely"
  }
}

Input Schema

Name	Required	Description	Default
`code`	Yes	A complete, self-contained Python 3 code string to execute within the remote sandbox environment.

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv1.1.4Aug 8, 2026

addedInput schema / properties / code / description

Added value: +"A complete, self-contained Python 3 code string to execute within the remote sandbox environment."

First observedv1.1.1Aug 6, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations, the description carries the full burden. It discloses the isolated sandbox environment, timeout limit, and automatic payment handling, which are key behavioral traits. It could optionally mention error handling or return format, but it already provides significant transparency for a code execution tool.

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 well-structured with a concise opening sentence, a short use-case paragraph, and a bulleted guideline list. Every sentence earns its place; there is no redundancy or fluff.

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

Completeness4/5

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

For a code execution tool with a single parameter and output schema present, the description covers purpose, environment, timeouts, imports, and payment behavior. It omits return-value details but the presence of an output schema reasonably handles that. It is complete enough for an agent to select and invoke correctly.

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?

The schema description covers 100% of the single parameter, so baseline is 3. The description reinforces the need for self-contained code with imports included, but this is more of an instruction than new parameter semantics. It does not add syntax, format, or edge-case detail 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?

The description clearly states the action ('Executes Python code'), the resource ('remote, isolated Azure sandbox environment'), and distinguishes it from sibling tools that target specific use cases like CSV sanitization or plot generation. It also lists concrete use cases, leaving no ambiguity about what the tool does.

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?

A dedicated 'Usage Guidelines' section provides explicit context for when to use the tool (evaluate algorithms, process data, math, custom scripts) and important constraints (standard Python 3, 30-second timeout, imports included, x402 payments). It does not name alternatives or exclusions, but the guidance is clear and actionable.

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

Deploy Server