ML Trainer MCP
An MCP server that trains and evaluates machine learning models through MCP tools.
Quickstart
uv pip install -e .
uv run ml-trainer-mcp
Or test interactively with the MCP Inspector:
mcp dev src/ml_trainer_mcp/server.py
Tools
| Tool | Description |
|---|---|
load_csv(path) |
Load a CSV file as the active dataset |
dataset_info() |
Shape, dtypes, and summary stats |
list_columns() |
List column names |
missing_values() |
Report missing values per column |
train_linear_regression(target) |
Train a linear regression model |
train_random_forest(target, ...) |
Train a random forest regressor |
evaluate_model(test_size) |
Evaluate the trained model on a held-out split |
predict(input_data) |
Predict on a single new row |
feature_importance() |
Feature importances / coefficients |
1 maintainer
Metadata
1 maintainer
Release files for ml-trainer-mcp 0.2.0
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | Provenance | Actions |
|---|---|---|---|---|
| ml_trainer_mcp-0.2.0.tar.gz | 8.9 kB | With provenance | Details for ml_trainer_mcp-0.2.0.tar.gz |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Actions Reset |
|---|---|---|---|---|
| ml_trainer_mcp-0.2.0-py3-none-any.whl | Python 3 | none | any | Details for ml_trainer_mcp-0.2.0-py3-none-any.whl |
Total release size: 21.4 kB
Release files / ml_trainer_mcp-0.2.0.tar.gz
| Download URL | ml_trainer_mcp-0.2.0.tar.gz |
|---|---|
| Size | 8.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
9cf9dd9bf22db732aa94a7940fb65ce3a47b1d163fa0831da04565a79b5581ef
|
|
BLAKE2b-256 checksum How to use checksums |
3c7b92d11df8cf0e5aa4f0859b55a486982e12a44d4dd9e34132390ddc1c6800
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 30, 2026.
Transparency logRelease files / ml_trainer_mcp-0.2.0-py3-none-any.whl
| Download URL | ml_trainer_mcp-0.2.0-py3-none-any.whl |
|---|---|
| Size | 12.5 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
6db2eef166cb7f6c229c3e9fae032aaa3772827900a021cb128804b74bbc5a87
|
|
BLAKE2b-256 checksum How to use checksums |
a3a8cb4dd0eeea0d3d4c26253295248d7981e6a24d394e5180c2a5730984d99a
|
| Upload date | |
|
Uploaded using Trusted Publishing? What is trusted publishing? |
Yes |
| Uploaded via |
twine/6.1.0 CPython/3.13.12
|
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 30, 2026.
Transparency logSecurity for ml-trainer-mcp 0.2.0
Provenance
Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.
Full provenance
Both files in this release have been uploaded with provenance metadata.
Attestation for ml_trainer_mcp-0.2.0.tar.gz
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Jun 30, 2026.
Transparency logA note on package security
PyPI is a repository for distributing packages, not a service for auditing them. While third-party security researchers and our staff do their best to detect and remove malicious packages, PyPI cannot guarantee the quality, security or suitability of any given package.
To learn more about protecting yourself from malicious actors, visit our package security documentation.
