# Integration
[← Back to README](../README.md)
## OpenAI
Run the keyphrases-mcp server locally and expose it to the internet via `ngrok`:
> [uv Installation Guide](https://docs.astral.sh/uv/getting-started/installation/)
> [ngrok Installation Guide](https://ngrok.com/docs/getting-started)
```sh
uvx --from git+https://github.com/IvanRublev/keyphrases-mcp.git keyphrases-mcp-server --allowed-dir <path_to_documents> --http
ngrok http 8000
```
Note the public URL (e.g., https://your-server.ngrok.io) for the next steps.
Add to ChatGPT with the following:
1. Enable Developer Mode
Open ChatGPT and go to Settings → Connectors
Under Advanced, toggle Developer Mode to enabled
2. Create Connector
In Settings → Connectors, click Create
Enter:
Name: Keyphrases-MCP
Server URL: https://your-server.ngrok.io/mcp/
Check I trust this provider
Click Create
Use in Chat
1. Start a new chat
2. Click the + button → More → Developer Mode
Enable your MCP server connector (required - the connector must be explicitly added to each chat)
Now you can use the tool.
## With Docker
You can use a dockerized deployment of this server to provide access via Streamable HTTP transport to MCP clients as follows:
Build the image, it will take ~10 GB of the disk space.
```sh
docker build -f Dockerfile -t keyphrases-mcp .
```
Run the container exposing ports and documents directory (replace path_to_documents with appropriate path on your system)
to validate it's starting properly.
```sh
docker run --rm --name keyphrases-mcp-server -i -v <path_to_documents>:/app/documents -p 8000:8000 keyphrases-mcp:latest
```
To provide CUDA GPU acceleration to the app, add the `--gpus all` flag to the command above.
Add the following configuration to your MCP client settings with validated paths:
```json
{
"mcpServers": {
"docker-keyphrases-mcp-server": {
"command": "docker",
"args": [
"run",
"--rm",
"--name", "keyphrases-mcp-server",
"-i",
"-v", "<path_to_documents>:/app/documents",
"-p", "8000:8000",
"keyphrases-mcp:latest"
]
}
}
}
```
## OpenAI Agents SDK
Integrate this MCP Server with the OpenAI Agents SDK. Read the [documents](https://openai.github.io/openai-agents-python/mcp/) to learn more about the integration of the SDK with MCP.
Install the Python SDK.
```sh
pip install openai-agents
```
Configure the OpenAI token:
```sh
export OPENAI_API_KEY="<openai_token>"
```
And run the [application](./openai_agents_sdk/keyphrases_assistant.py).
```sh
cd openai_agents_sdk && python keyphrases_assistant.py --allowed-dir <path_to_documents>
```
You can troubleshoot your agent workflows using the [OpenAI dashboard](https://platform.openai.com/traces/).
## Claude Desktop
Run the following command once to download embeddings and spaCy models.
> [uv Installation Guide](https://docs.astral.sh/uv/getting-started/installation/)
```sh
<path_to_uvx>/bin/uvx --from git+https://github.com/IvanRublev/keyphrases-mcp.git keyphrases-mcp-server --download-models
```
Update the Claude configuration file on macOS: `~/Library/Application Support/Claude/claude_desktop_config.json` on windows: `%APPDATA%\Claude\claude_desktop_config.json`
Add the kyphrases-mcp server configuration to run it from pypi org with `uvx`:
```json
{
"mcpServers": {
"keyphrases-mcp-server": {
"type": "stdio",
"command": "<path_to_uvx>/bin/uvx",
"args": [
"--from", "keyphrases-mcp",
"keyphrases-mcp-server",
"--allowed-dir", "<path_to_documents>"
]
}
}
}
```
Start the application. It will take some time do download ~1 GB of dependencies on the first launch.
Alternatively, you can clone the source code from the GitHub repository and start the server using `uv`. This is usually desired for development.
```json
{
"mcpServers": {
"keyphrases-mcp-server": {
"type": "stdio",
"command": "<path_to_uv>/bin/uv",
"args": [
"run",
"--directory", "<path_to_keyphrases-mcp>/src",
"-m", "main",
"--allowed-dir", "<path_to_documents>"
]
}
}
}
```
