glin

glin

☕ If glin is useful to you and you want to help maintain it, you can buy me a coffee.

glin lets your AI agent train a classifier on your data and then use it. You install glin into an agent such as Claude Code, Claude Desktop or Cursor. Then you talk to the agent in plain language. The agent trains the model, makes predictions and explains them. You do not write code.

glin trains an Explainable Boosting Machine (EBM) on a CSV or Excel file. Each prediction comes with an exact breakdown of the features that caused it. The breakdown comes from the additive structure of the model. It is not a SHAP or LIME approximation.

glin is an MCP server. It runs on your machine and talks to the agent over stdio. Your data and your models stay on your machine.

Changelog: CHANGELOG.md lists the changes in each release.

Case study: The segment of 100,000 sales leads. Claude with glin put 71% of new leads in the correct segment. Claude alone got 33%. With 1,000 labeled leads in the prompt, Claude got 60% and used 6.7 times more tokens.

How glin works

1. You give the agent a data file and the column to predict.
2. The agent calls glin. glin checks the data, trains a model and saves it in ~/.glin/models/ /.
3. glin tests the model on rows that it did not train on. The agent tells you how good the model is.
4. You ask about new cases. The agent sends each case to the model and gets a prediction with the exact reasons.
5. You ask what drives the outcome. The agent reads it from the model.

A saved model stays available. You can use it again in a new chat.

What you can ask the agent

• "Train a classifier on ~/data/leads.csv that predicts converted. Call it leads_v1."
• "Which of these 3 deals should I prioritize?" The agent sends each deal to the model and gives the reasons.
• "What drives churn in this model?"
• "Tune the model, and tell me how good it really is."
• "Which models do I have?"
• "Delete the model leads_v1."

Contents

• How glin works
• What you can ask the agent
• Install
• Use glin from your agent
• Train a model
• Get predictions
• Explain a model
• Tune a model
• Manage models
• Your data
• Files that glin can read
• Excel files
• Data checks
• How glin reads columns
• Dates and timestamps
• Columns that glin does not use
• Target leakage check
• Reference
• MCP tools
• MCP prompts
• Server flags
• Case studies
• Development
• Releasing (maintainers)

Install

Your MCP client starts glin with uvx. uvx downloads the PyPI package glin-ml and keeps it in a cache. You do not install glin with pip. (The PyPI name glin belongs to an unrelated project.)

Do step 1 for all clients. Then do step 2 for your client.

Step 1: Install uv and download glin

1. Install uv. Use one of these commands:

   # macOS and Linux

curl -LsSf https://astral.sh/uv/install.sh | sh

macOS with Homebrew

brew install uv

Windows (PowerShell)

powershell -ExecutionPolicy ByPass -c "irm https://astral.sh/uv/install.ps1 | iex"

1. Open a new terminal.
2. Run this command:

   uvx --from 'glin-ml[excel]>=0.1.5' glin --version

The command shows glin-ml 0.1.5 or a later version. The first run downloads glin and its libraries into the cache. This can take approximately one minute.

Do not skip step 1.3. Some clients stop a server that does not start in 30 seconds. Claude Code is one of them. When the cache is full, glin starts in a few seconds.

Step 2: Add glin to your client

Do the steps for one of these clients:

• Claude Code, with the plugin (recommended)
• Claude Code, without the plugin
• Claude Desktop
• Cursor and other MCP clients
• A client with a registry search

Claude Code, with the plugin (recommended)

The plugin adds the glin MCP server and a skill. The skill tells the agent when and how to build, tune, predict with and explain a model. The plugin includes Excel support.

1. Start Claude Code.
2. Add the glin marketplace:

   /plugin marketplace add AkashChatterjee/glin

1. Install the plugin:

   /plugin install glin@glin-marketplace

1. If Claude Code tells you to restart it, restart it.
2. Run /mcp. Make sure that the list shows the glin server as connected.

The plugin starts uvx --from 'glin-ml[excel]>=0.1.5' glin serve. The plugin files are in .claude-plugin/marketplace.json and plugins/glin/.

Claude Code, without the plugin

Use this option to add only the MCP server, without the skill.

1. In a terminal, run this command:

   claude mcp add --scope user glin -- uvx --from 'glin-ml[excel]>=0.1.5' glin serve

--scope user makes glin available in all your projects. To add glin to the current project only, remove --scope user.

1. Start Claude Code.
2. Run /mcp. Make sure that the list shows the glin server as connected.

Claude Desktop

1. Open Claude Desktop.
2. Open Settings > Developer and click Edit Config. This opens the file claude_desktop_config.json:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
• Windows: %APPDATA%\Claude\claude_desktop_config.json

1. Add the glin entry to mcpServers. If the file has other servers, keep them:

   {

"mcpServers": { "glin": { "command": "uvx", "args": ["--from", "glin-ml[excel]>=0.1.5", "glin", "serve"] } } }

1. Save the file.
2. Quit Claude Desktop fully, then start it again. On macOS, closing the window is not sufficient.
3. Make sure that the glin tools show in the tools menu of a new chat.

If Claude Desktop cannot find uvx (for example spawn uvx ENOENT), use the full path. Run which uvx (macOS and Linux) or where uvx (Windows), and put the result in "command".

Cursor and other MCP clients

1. Open the MCP config file of your client:

• Cursor: ~/.cursor/mcp.json for all projects, or .cursor/mcp.json in one project.
• Other clients: see the documentation of the client.

1. Add the same glin entry as for Claude Desktop:

   {

"mcpServers": { "glin": { "command": "uvx", "args": ["--from", "glin-ml[excel]>=0.1.5", "glin", "serve"] } } }

1. Save the file and restart the client.
2. Make sure that the client shows glin as an enabled MCP server.

If the client cannot find uvx, use the full path to uvx in "command", as for Claude Desktop.

A client with a registry search

glin is in the official MCP Registry as io.github.AkashChatterjee/glin. The registry is a catalog of metadata. It is not an installer. MCP marketplaces and some clients read it and show glin in their server lists. For example, VS Code installs servers from the GitHub MCP Registry.

1. In your client, search the server list for glin.
2. Install the entry io.github.AkashChatterjee/glin.
3. Make sure that the client shows glin as an enabled MCP server.

The registry entry starts uvx glin-ml serve, without the excel extra. The registry format has no field for extras. To read Excel files, use the steps in Cursor and other MCP clients instead. If your client has no registry search, use those steps too.

Install options

• CSV only: If you do not use Excel files, you can remove [excel] from the arguments.
• Server flags: Put flags such as --allow-dir after serve in the arguments. See Server flags.
• Update glin: Run uvx --refresh --from 'glin-ml[excel]>=0.1.5' glin --version. Then restart your client.
• An installed copy: To use a copy from uv tool install 'glin-ml[excel]' or pipx install 'glin-ml[excel]', set "command" to glin and "args" to ["serve"]. You must update this copy yourself, for example with uv tool upgrade glin-ml.

Use glin from your agent

When glin is installed, talk to the agent in plain language. The agent selects the glin tools. You do not need to know the tool names. This section shows what you can ask and what you get. For the exact tool arguments, see MCP tools.

Train a model

Give the agent a data file and the column to predict:

"Train a classifier on ~/data/churn.csv that predicts churn. Call it churn_v1."

The agent calls train_model. glin checks the data, prepares the columns and trains the model. Then glin returns:

• the features that it kept and the features that it dropped, each with a reason;
• warnings, for example a column that possibly leaks the target;
• test results on a holdout: 20% of the rows that the model did not train on.

The test results are log_loss, baseline_log_loss (a guess from the class shares only), train_log_loss, accuracy, balanced_accuracy and roc_auc. A useful model has a log_loss that is clearly lower than baseline_log_loss.

For a careful build, ask the agent to use the build_classifier prompt. The prompt tells the agent to clean the data, remove leakage, tune the model and report the honest performance. See MCP prompts.

If a model with the same name exists, training fails. To replace a model, for example with new data, ask the agent to retrain it with overwrite=True.

Get predictions

Ask about one case or many cases. Give the values that you know:

"I've got 3 deals to prioritize before quarter close: Northwind Systems ($42k, Opportunity stage, came from a referral, VP contact, owned by Carla Nguyen, 8 touches logged), Summit Retail Group ($8k, still a Lead, paid social source, owned by Brian Kessler), and Anchor Nonprofit ($3k, still a Lead, owned by Frank Suarez, no activity logged yet). Run them through deal_predictor_v1 and tell me which to prioritize."

The agent calls predict once for each deal. It gives a ranked answer with the reasons:

Each prediction has an audit. The audit gives the score of each feature, and the scores add up exactly to the probability. glin checks this sum in each prediction (additivity_verified).

Some important points:

• A value that you do not give is missing for the model. It is not an average value.
• Use the categories that are in the training data, for example Lead, Opportunity and Customer. Othe