Skip to main content
Glama
nicknochnack

MCP Server for ML Model Integration

by nicknochnack
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

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

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
PredictChurnC

This tool predicts whether an employee will churn or not, pass through the input as a list of samples. Args: data: employee attributes which are used for inference. Example payload

    [{
    'YearsAtCompany':10,
    'EmployeeSatisfaction':0.99,
    'Position':'Non-Manager',
    'Salary:5.0
    }]

Returns:
    str: 1=churn or 0 = no churn

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

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools, as there are no other tools to compare it to. The tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

With only one tool, naming consistency is inherently perfect, as there are no other tool names to be inconsistent with. The tool name 'PredictChurn' follows a clear verb_noun pattern.

Tool Count2/5

A single tool is too few for a server described as 'ML Model Integration', which suggests a broader scope covering multiple models or operations. This feels thin and incomplete for the apparent domain.

Completeness2/5

The server is severely incomplete for ML model integration, as it only offers a churn prediction tool. There are significant gaps, such as no tools for training models, listing available models, updating models, or handling other common ML tasks.

Maintenance

ActivityInactive
ResponsivenessNo issues

Resources