by port-labs
Integrate with Port.io API for AI workflow automation, enabling smarter task management and seamless workflow orchestrat
Connects to Port.io's developer portal API to let you query service catalogs, manage scorecards, and automate developer workflows through natural language. Note: This repository is deprecated - use Port's Remote MCP Server instead.
Port is a community-built MCP server published by port-labs that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate with Port.io API for AI workflow automation, enabling smarter task management and seamless workflow orchestrat It is categorized under developer tools.
You can install Port in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Port is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
According to our notes, Port benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Useful MCP listing: Port is the kind of server we cite when onboarding engineers to host + tool permissions.
Port has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
We evaluated Port against two servers with overlapping tools; this profile had the clearer scope statement.
Port is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Port is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Port against two servers with overlapping tools; this profile had the clearer scope statement.
Port reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
I recommend Port for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: Port surfaces stars and publisher context so we could sanity-check maintenance before adopting.
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[!WARNING] This repository has been deprecated and is no longer actively maintained or supported. No further updates, bug fixes, or security patches will be provided.
We strongly encourage all users to migrate to Port's Remote MCP Server, which is the actively maintained and supported solution.
The Remote MCP Server offers full feature parity and ongoing improvements backed by the Port team.
The Port IO MCP server is a Model Context Protocol (MCP) server, enabling advanced automations and natual language interactions for developers and AI applications.
We're continuously expanding Port MCP's capabilities. Have a suggestion? We'd love to hear your feedback on our roadmap!
Before you begin, you'll need:
Create a Port Account (if you don't have one):
Obtain Port Credentials:
Installation Requirements:
[!NOTE] You will also need to provide your Port region, which is either EU or US. If not provided, the default is EU.
Port MCP Server can be installed using two methods:
Use our official Port MCP server package with uvx for easy installation and management.
Create a Python Virtual Environment (Recommended)
python -m venv venv
Activate the Virtual Environment
# On Linux/macOS:
source venv/bin/activate
# On Windows:
venv\Scripts\activate
Install the UV Package Manager
# Using Homebrew (macOS/Linux):
brew install uv
# Or using pip:
pip install uv
Verify UV Installation
which uv
Set Required Environment Variables
export PORT_CLIENT_ID="your_port_client_id"
export PORT_CLIENT_SECRET="your_port_client_secret"
export PORT_REGION="EU" # or "US"
Set Python Path (if using virtual environment)
export PYTHONPATH="/path/to/your/venv/bin/python"
Run the MCP Server
uvx mcp-server-port --client-id your_port_client_id --client-secret your_port_client_secret --region EU --log-level DEBUG
Verify Server is Running You should start seeing logs from the server. You can also check the log file:
cat /tmp/port-mcp.log
Use our official Docker image:
docker pull ghcr.io/port-labs/port-mcp-server:latest
See below for detailed instructions on each MCP client.
You can pass these additional arguments for more advanced configuration:
| Configuration Parameter | UVX Flag | Docker Environment Variable | Description | Default Value |
|---|---|---|---|---|
| Log Level | log-level | PORT_LOG_LEVEL | Controls the level of log output | ERROR |
| API Validation | api-validation-enabled | PORT_API_VALIDATION_ENABLED | Controls if API schema should be validated and fail if it's not valid | False |
claude_desktop_config.json file and add the below configuration based on the installation method.
[!TIP] Consider using the full path to Docker (e.g.,
/usr/local/bin/docker) instead of justdocker. You can find this path by runningwhich dockerin your terminal. Using the full path helps avoid PATH resolution issues and ensures consistent behavior across different shell environments.
{
"mcpServers": {
"port": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"PORT_CLIENT_ID",
"-e",
"PORT_CLIENT_SECRET",
"-e",
"PORT_REGION",
"-e",
"PORT_LOG_LEVEL",
"ghcr.io/port-labs/port-mcp-server:latest"
],
"env": {
"PORT_CLIENT_ID": "<PORT_CLIENT_ID>",
"PORT_CLIENT_SECRET": "<PORT_CLIENT_SECRET>",
"PORT_REGION": "<PORT_REGION>",
"PORT_LOG_LEVEL": "<PORT_LOG_LEVEL>"
}
}
}
}
[!NOTE] If you want to run the command from a virtual Python environment, add a
PYTHONPATHvariable to theenvobject with its path, e.g.,/path/to/your/venv/bin/python.
{
"mcpServers": {
"Port": {
"command": "uvx",
"args": [
"[email protected]",
"--client-id",
"<PORT_CLIENT_ID>",
"--client-secret",
"<PORT_CLIENT_SECRET>",
"--region",
"<PORT_REGION>"
],
"env": {
"PORT_CLIENT_ID": "<PORT_CLIENT_ID>",
"PORT_CLIENT_SECRET": "<PORT_CLIENT_SECRET>",
"PORT_REGION": "<PORT_REGION>",
"PYTHONPATH": "/Users/matangrady/.venv-port-mcp/bin/python"
}
}
}
}
mcp.json file and add the below configuration based on the installation method.
[!TIP] Consider using the full path to Docker (e.g.,
/usr/local/bin/docker) instead of justdocker. You can find this path by runningwhich dockerin your terminal. Using the full path helps avoid PATH resolution issues and ensures consistent behavior across different shell environments.
{
"mcpServers": {
"port": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"PORT_CLIENT_ID",
"-e",
"PORT_CLIENT_SECRET",
"-e",
"PORT_REGION",
"-e",
"PORT_LOG_LEVEL",
"ghcr.io/port-labs/port-mcp-server:latest"
],
"env": {
"PORT_CLIENT_ID": "<PORT_CLIENT_ID>",
"PORT_CLIENT_SECRET": "<PORT_CLIENT_SECRET>",
"PORT_REGION": "<PORT_REGION>",
"PORT_LOG_LEVEL": "<PORT_LOG_LEVEL>"
}
}
}
}
[!NOTE] If you want to run the command from a virtual Python environment, add a
PYTHONPATHvariable to theenvobject with its path, e.g.,/path/to/your/venv/bin/python.
{
"mcpServers": {
"Port": {
"command": "uvx",
"args": [
"[email protected]",
"--client-id",
"<PORT_CLIENT_ID>",
"--client-secret",
"<PORT_CLIENT_SECRET>",
"--region",
"<PORT_REGION>"
],
"env": {
"PORT_CLIENT_ID": "<PORT_CLIENT_ID>",
"PORT_CLIENT_SECRET": "<PORT_CLIENT_SECRET>",
"PORT_REGION": "<PORT_REGION>",
"PYTHONPATH": "/Users/matangrady/.venv-port-mcp/bin/python"
}
}
}
}
[!TIP] VS Code can automatically discover MCP servers already installed in Cursor and Claude.
[!NOTE] For quick installation, use the one-click install buttons and select where to add the MCP configuration. Make sure to replace the placeholders with your Port credentials.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.