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Glama
philipp-eisen

Modal MCP Toolbox

by philipp-eisen
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
run_python_code_in_sandboxB
Runs python code in a safe environment and returns the output.

Usage:
   run_python_code_in_sandbox("print('Hello, world!')")
   run_python_code_in_sandbox("import requests

print(requests.get('https://icanhazip.com').text)", requirements=["requests"])

generate_flux_imageC

Let's you generate an image using the Flux model.

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

B3.1/5.0

Scored across 2 tools

Disambiguation5/5

The two tools have completely distinct purposes: one generates images using a specific AI model, while the other executes Python code in a sandboxed environment. There is no overlap in functionality, and an agent would never confuse these tools.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with snake_case naming: generate_flux_image and run_python_code_in_sandbox. The naming is clear, descriptive, and follows the same convention throughout.

Tool Count2/5

With only 2 tools, this server feels extremely thin for a 'Toolbox' name that suggests broader utility. The tools are unrelated (image generation vs. code execution), making the server feel like two separate utilities bundled together rather than a coherent toolbox.

Completeness2/5

As a 'Toolbox,' there are significant gaps in coverage. The server lacks tools for common utility tasks like file operations, data processing, or other AI models. Even within the narrow domains represented, there are no complementary operations (e.g., no image manipulation tools to accompany generation, no code analysis tools to accompany execution).

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources