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Glama
gradion-ai

ipybox

by gradion-ai
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault

No arguments

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Server capabilities have not been inspected yet.

Tools

Functions exposed to the LLM to take actions

NameDescription
execute_ipython_cellA

Execute Python code in a stateful IPython kernel within a Docker container.

    The kernel maintains state across executions - variables, imports, and definitions
    persist between calls. Each execution builds on the previous one, allowing you to
    build complex workflows step by step. Use '!pip install package_name' to install
    packages as needed.

    The kernel has an active asyncio event loop, so use 'await' directly for async
    code. DO NOT use asyncio.run() or create new event loops.

    Executions are sequential (not concurrent) as they share kernel state. Use the
    reset() tool to clear the kernel state and start fresh.

    Returns:
        str: Output text from execution, or empty string if no output.
upload_fileA

Upload a file from the host filesystem to the container's /app directory.

    Makes a file from the host available inside the container for code execution.
    The uploaded file can then be accessed in execute_ipython_cell using the
    path '/app/{relpath}'.
download_fileA

Download a file from the container's /app directory to the host filesystem.

Retrieves files created or modified during code execution from the container. The file at '/app/{relpath}' in the container will be saved to the specified location on the host.

Parent directories are created automatically if they don't exist.

resetA

Reset the IPython kernel to a clean state.

Creates a new kernel instance, clearing all variables, imports, and definitions from memory. Installed packages and files in the container filesystem are preserved. Useful for starting fresh experiments or clearing memory after processing large datasets.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.4/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose with no overlap: download_file retrieves files from container to host, execute_ipython_cell runs Python code in a persistent kernel, reset clears kernel state, and upload_file sends files from host to container. The boundaries are unambiguous, preventing agent misselection.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (download_file, execute_ipython_cell, reset, upload_file) with clear, descriptive actions. Reset is a single verb but fits naturally as it describes a clear action without needing a noun, maintaining overall consistency in style and readability.

Tool Count5/5

With 4 tools, the server is well-scoped for its purpose of interactive Python execution in a container. Each tool earns its place by covering essential operations: file transfer (upload/download), code execution, and state management (reset). This count avoids bloat while providing complete workflow coverage.

Completeness5/5

The tool set provides complete coverage for the domain of container-based IPython execution. It supports the full lifecycle: uploading files, executing code with state persistence, resetting the environment, and downloading results. No obvious gaps exist; agents can perform end-to-end workflows without dead ends.

Maintenance

ActivityInactive
ResponsivenessUnresponsive

Resources