ml-lifecycle-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
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
| Capability | Details |
|---|---|
| tools | {
"listChanged": false
} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| forecast_timeseriesA | Forecast future values of a univariate time series. Args: values: Historical observations in chronological order (minimum 4 points). horizon: Number of future steps to forecast. seasonal_periods: Length of one seasonal cycle (e.g. 12 for monthly data with yearly seasonality), if the series is seasonal. Omit if unknown or the series is too short to estimate seasonality reliably. Returns: Point forecast plus an approximate 80% confidence interval, and the method actually used (seasonal models silently fall back to a trend-only model if there isn't enough data for the requested period). |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 1 tool
With only one tool, there is no possibility of confusion or overlap between tools. The single tool's purpose is clearly described.
There is only one tool, so naming consistency is trivially maintained. The name 'forecast_timeseries' follows a clear verb_noun pattern.
The server name implies a broad ML lifecycle scope, but it exposes only one forecasting tool. This is far too few tools to cover the intended domain, making the count inappropriate.
The server claims to handle an ML lifecycle but provides only a time series forecasting tool. Essential operations like data preprocessing, model training, evaluation, and deployment are missing, resulting in a severely incomplete surface.