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agentic_actions_mcp

agentic_actions_mcp

by codewithyasho
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

python_code

Execute Python code and retrieve its output. Use it to run scripts and test code snippets.

Instructions

This function will execute a python code and return the output.

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": "python_code"
  }
}

Input Schema

NameRequiredDescriptionDefault
codeYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.1/5.0
Behavior2/5

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 only mentions execution and returning output, omitting details about side effects, execution environment, error handling, or whether the output is stdout, stderr, or an exit code.

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 a single, front-loaded sentence with no unnecessary words. It efficiently states the core action and result, making it appropriately concise.

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

Completeness2/5

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

Given the lack of annotations, output schema, and the inherent complexity of a code execution tool, the description is insufficient. The agent is not informed about the output shape, potential side effects, or safety considerations, leaving significant gaps.

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?

The input schema has one 'code' parameter with no description, and the description clarifies that it represents the Python code to execute. This adds meaning beyond the schema, but does not provide constraints like size or format specifics.

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?

The description states the tool executes Python code and returns output, using a specific verb and resource. However, it does not differentiate from sibling tools like python_file or bash, so it falls short of a perfect score.

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?

The description provides no guidance on when to use this tool versus alternatives such as bash or python_file. It lacks any context about preferred use cases 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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