# MOSTLY AI MCP Server
[](https://github.com/mostly-ai/mostlyai-mcp-server/stargazers)
A [Model Context Protocol (MCP)](https://modelcontextprotocol.io) server with OAuth 2.1 support for LLM agents to interact with the [MOSTLY AI Platform](https://app.mostly.ai).
## Available Tools
| Tool | Description |
|------|-------------|
| `get_platform_info` | retrieve information about the platform |
| `get_user_info` | retrieve information about the current user |
| `get_models` | retrieve a list of available models of a specific type |
| `get_computes` | retrieve a list of available compute resources for tasks |
| `list_connectors` | list all available connectors |
| `get_connector` | get details of a specific connector |
| `create_connector` | create a new connector and optionally test the connection |
| `get_connector_locations` | list available locations (schemas, tables, buckets, etc.) for a connector |
| `get_connector_schema` | get the schema of a table or file at a connector location |
| `read_connector_data` | read data from a connector location |
| `write_connector_data` | write data to a connector location (currently disabled) |
| `query_connector` | execute a read-only SQL query against a connector's data source |
| `list_generators` | list all available generators |
| `get_generator` | get details of a specific generator |
| `poll_generator_progress` | poll progress of a generator training job |
| `train_generator` | train a new generator with provided data or configuration |
| `clone_generator` | clone an existing generator |
| `get_synthetic_dataset` | get details of a specific synthetic dataset |
| `poll_synthetic_dataset_progress` | poll progress of a synthetic dataset generation job |
| `generate_synthetic_dataset` | generate a synthetic dataset using a generator |
| `list_synthetic_datasets` | list all available synthetic datasets |
| `probe_generator` | probe a generator for a new synthetic dataset (quick sample) |
## Usage of Remote Server
### Prerequisites
An MCP host that supports the latest MCP specification and remote servers, such as [Claude](https://claude.ai/), [VS Code](https://code.visualstudio.com/) and [Cursor](https://www.cursor.com/).
### Installation
<details>
**<summary>Claude Desktop or Web UI</summary>**
`Search and tools` -> `Add integrations` -> `Add integration` -> Set Integration name to the name of your choice (e.g., `mostlyai`) and Integration URL to `https://mcp.mostly.ai/sse`.
</details>
<details>
**<summary>VS Code (>= v1.101)</summary>**
[](https://insiders.vscode.dev/redirect/mcp/install?name=mostlyai&config=%7B%22url%22%3A%20%22https%3A%2F%2Fmcp.mostly.ai%2Fsse%22%7D)
For quick installation, please use the one-click button above.
Alternatively, one can add the following JSON to `.vscode/mcp.json` (for the workspace) or the global `settings.json` (for the user).
```json
{
"servers": {
"mostlyai": {
"url": "https://mcp.mostly.ai/sse"
}
}
}
```
</details>
<details>
**<summary>Cursor</summary>**
[](https://cursor.com/install-mcp?name=mostlyai&config=eyJ1cmwiOiJodHRwczovL21jcC5tb3N0bHkuYWkvc3NlIn0%3D)
For quick installation, please use the one-click button above.
Alternatively, one can add the following JSON to `~/.cursor/mcp.json`.
```json
{
"mcpServers": {
"mostlyai": {
"url": "https://mcp.mostly.ai/sse"
}
}
}
```
</details>
## Development
### Setup virtual environment
`uv sync --frozen`
### Start the server locally
1. Set the necessary environment variables (see `.env.example`)
2. `uv run mostlyai-mcp-server --port 8000`
3. Connect to the server with the configuration described in the above section, but change the url to `http://localhost:8000/sse`.
### Debug
We recommend to use the Anthropic's [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) for testing and debugging by running `npx @modelcontextprotocol/inspector`.
