Prolific Mcp Server
About
A Model Context Protocol server bridging LLM agents to the Prolific public API. Built with FastMCP.
Explore
- Bridges LLM agents to the Prolific public API.
- Exposes tools such as get_filters, create_filter_set, publish_study, and more.
- Supports stdio and streamable HTTP transports.
- Configurable via environment variables PROLIFIC_TOKEN and PROLIFIC_URL.
- Published as a .mcpb bundle for easy installation in Claude Desktop.
- No sandbox mode – publish_study spends real money on participants.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Prolific Mcp ServerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
- Python 3.11+
- A Prolific API token (create one at <https://app.prolific.com>)
| Variable | Required | Default | Description |
|---|---|---|---|
| PROLIFIC_TOKEN | yes | — | Prolific API token, sent as Authorization: Token … |
| PROLIFIC_URL | no | https://api.prolific.com | Base URL of the Prolific API |
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"prolific mcp server": {
"prolific": {
"command": "uvx",
"args": [
"prolific-mcp"
],
"env": {
"PROLIFIC_TOKEN": "your_token_here"
}
}
}
}
}McpServers
{
"prolific": {
"command": "uvx",
"args": [
"prolific-mcp"
],
"env": {
"PROLIFIC_TOKEN": "your_token_here"
}
}
}
A Model Context Protocol server bridging LLM agents to the Prolific public API. Built with FastMCP.
Listed on the official MCP Registry — discoverable from Claude Desktop, Cursor, Claude Code, and other MCP-aware clients.
> [!WARNING]
> publish_study spends real money. Once a study is published on Prolific, participants can start it immediately and you are charged for their work. Treat any LLM-driven invocation as a real spend: review the study draft and reward before approving the tool call. There is no sandbox mode — the same endpoint is used in production.
What it does
Exposes a small set of tools so an LLM can help a researcher design and launch a study on Prolific:
| Tool | Prolific endpoint |
|---|---|
| get_filters | GET /api/v1/filters/ |
| get_filter_sets | GET /api/v1/filter-sets/ |
| create_filter_set | POST /api/v1/filter-sets/ |
| get_eligibility_count | POST /api/v1/eligibility-count/ |
| list_workspaces | GET /api/v1/workspaces/ |
| list_projects | GET /api/v1/workspaces/{workspace_id}/projects/ |
| list_studies | GET /api/v1/studies/ |
| view_study | GET /api/v1/studies/{id}/ |
| create_study | POST /api/v1/studies/ |
| publish_study | POST /api/v1/studies/{id}/transition/ |
Requirements
- Python 3.11+
- A Prolific API token (create one at <https://app.prolific.com>)
Quickstart
Install and run from PyPI with uv:
export PROLIFIC_TOKEN=your_token_here
uvx prolific-mcp
Or from a checkout:
uv sync
export PROLIFIC_TOKEN=your_token_here
uv run prolific-mcp
The server defaults to stdio and is intended to be launched by an MCP client (Claude Desktop, Cursor, etc.).
Local development (HTTP mode)
Stdio gives the client ownership of the process, which hides server logs. For local iteration, run over streamable HTTP instead so logs stream to your terminal:
```bash
PROLIFIC_TOKEN=your_token_here uv run prolific-mcp --http
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