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Xplainable MCP Server

Official
by xplainable
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Python
Remote

Xplainable MCP Server

A Model Context Protocol (MCP) server that provides secure access to Xplainable AI platform capabilities through standardized tools and resources.

Features

  • Secure Authentication: Token-based authentication with environment variable management

  • Read Operations: Access models, deployments, preprocessors, and collections

  • Write Operations: Deploy models, manage deployments, generate reports (with proper authorization)

  • Type Safety: Full Pydantic model validation for all inputs/outputs

  • Rate Limiting: Built-in rate limiting and request validation

  • Audit Logging: Comprehensive logging of all operations

Related MCP server: Plausible MCP Server

Installation

pip install xplainable-mcp-server

CLI Commands

The server includes a CLI for management and documentation:

# List all available tools xplainable-mcp-cli list-tools xplainable-mcp-cli list-tools --format json xplainable-mcp-cli list-tools --format markdown # Validate configuration xplainable-mcp-cli validate-config xplainable-mcp-cli validate-config --env-file /path/to/.env # Test API connection xplainable-mcp-cli test-connection # Generate tool documentation xplainable-mcp-cli generate-docs xplainable-mcp-cli generate-docs --output TOOLS.md

Quick Start

For Production Users

If you just want to use this MCP server with Claude Code:

  1. Get your Xplainable API key from https://platform.xplainable.io

  2. Add the MCP configuration (see Claude Code Configuration above)

  3. That's it! Claude Code will handle installation automatically

For Developers

1. Set up environment variables

Create a .env file with your Xplainable credentials:

XPLAINABLE_API_KEY=your-api-key-here XPLAINABLE_HOST=https://platform.xplainable.io XPLAINABLE_ORG_ID=your-org-id # Optional XPLAINABLE_TEAM_ID=your-team-id # Optional

2. Run the server

# For development (localhost only) xplainable-mcp # For production (with TLS/proxy) xplainable-mcp --host 0.0.0.0 --port 8000

3. Connect with an MCP client

Claude Code Configuration

Option 1: Install from GitHub (Recommended)

{ "mcpServers": { "xplainable": { "command": "uvx", "args": ["--from", "git+https://github.com/yourusername/xplainable-mcp-server.git", "xplainable-mcp-server"], "env": { "XPLAINABLE_API_KEY": "your-api-key-here", "XPLAINABLE_HOST": "https://platform.xplainable.io" } } } }

Option 2: Clone and run from source

{ "mcpServers": { "xplainable": { "command": "python", "args": ["-m", "xplainable_mcp.server"], "cwd": "/path/to/cloned/xplainable-mcp-server", "env": { "XPLAINABLE_API_KEY": "your-api-key-here", "XPLAINABLE_HOST": "https://platform.xplainable.io" } } } }

Option 3: Development with local backend

{ "mcpServers": { "xplainable": { "command": "python", "args": ["-m", "xplainable_mcp.server"], "cwd": "/path/to/xplainable-mcp-server", "env": { "XPLAINABLE_API_KEY": "your-development-key", "XPLAINABLE_HOST": "http://localhost:8000", "ENABLE_WRITE_TOOLS": "true" } } } }

Claude Desktop Configuration

Add the configuration to your Claude Desktop MCP settings file:

File Locations:

  • macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

  • Windows: %APPDATA%\Claude\claude_desktop_config.json

  • Linux: ~/.config/Claude/claude_desktop_config.json

Option 1: Install from GitHub (Recommended)

{ "mcpServers": { "xplainable": { "command": "uvx", "args": ["--from", "git+https://github.com/yourusername/xplainable-mcp-server.git", "xplainable-mcp-server"], "env": { "XPLAINABLE_API_KEY": "your-api-key-here", "XPLAINABLE_HOST": "https://platform.xplainable.io" } } } }

Option 2: Development setup (from source)

{ "mcpServers": { "xplainable": { "command": "python", "args": ["-m", "xplainable_mcp.server"], "cwd": "/path/to/xplainable-mcp-server", "env": { "XPLAINABLE_API_KEY": "your-api-key", "XPLAINABLE_HOST": "https://platform.xplainable.io", "ENABLE_WRITE_TOOLS": "true" } } } }

Option 3: Using conda environment

{ "mcpServers": { "xplainable": { "command": "conda", "args": ["run", "-n", "xplainable-mcp", "python", "-m", "xplainable_mcp.server"], "cwd": "/path/to/xplainable-mcp-server", "env": { "XPLAINABLE_API_KEY": "your-api-key", "XPLAINABLE_HOST": "https://platform.xplainable.io", "ENABLE_WRITE_TOOLS": "true" } } } }

Development Setup

For Local Development with Claude Code

  1. Set up the environment:

# Create conda environment conda create -n xplainable-mcp python=3.9 conda activate xplainable-mcp # Install dependencies pip install -e . pip install -e /path/to/xplainable-client

  1. Configure environment variables:

# .env file for development XPLAINABLE_API_KEY=your-development-api-key XPLAINABLE_HOST=http://localhost:8000 ENABLE_WRITE_TOOLS=true RATE_LIMIT_ENABLED=false

  1. Test the setup:

# Test connection to local backend python -c " import sys sys.path.append('.') from xplainable_mcp.server import get_client client = get_client() print('Connection successful!') print(f'Connected to: {client.connection_info}') "

Example Deployment Workflow

Here's a complete example of deploying a model and testing inference:

# 1. List available models python -c " from xplainable_mcp.server import get_client client = get_client() models = client.models.list_team_models() for model in models[:3]: # Show first 3 print(f'Model: {model.display_name} (ID: {model.model_id})') print(f' Version: {model.active_version}') print(f' Deployed: {model.deployed}') " # 2. Deploy a model version (replace with actual version_id) python -c " from xplainable_mcp.server import get_client client = get_client() deployment = client.deployments.deploy('your-version-id-here') print(f'Deployment ID: {deployment.deployment_id}') " # 3. Generate deployment key python -c " from xplainable_mcp.server import get_client client = get_client() key = client.deployments.generate_deploy_key('deployment-id', 'Test Key') print(f'Deploy Key: {key}') " # 4. Test inference (requires active deployment) curl -X POST https://inference.xplainable.io/v1/predict \ -H 'Content-Type: application/json' \ -d '{ "deploy_key": "your-deploy-key", "data": {"feature1": "value1", "feature2": 123} }'

Available Tools

Discovery Tools

  • list_tools() - List all available MCP tools with descriptions and parameters

Read-Only Tools

  • get_connection_info() - Get connection and diagnostic information

  • list_team_models(team_id?) - List all models for a team

  • get_model(model_id) - Get detailed model information

  • list_model_versions(model_id) - List all versions of a model

  • list_deployments(team_id?) - List all deployments

  • list_preprocessors(team_id?) - List all preprocessors

  • get_preprocessor(preprocessor_id) - Get preprocessor details

  • get_collection_scenarios(collection_id) - List scenarios in a collection

  • get_active_team_deploy_keys_count(team_id?) - Get count of active deploy keys

  • misc_get_version_info() - Get version information

Write Tools (Restricted)

Note: Write tools require

  • activate_deployment(deployment_id) - Activate a deployment

  • deactivate_deployment(deployment_id) - Deactivate a deployment

  • generate_deploy_key(deployment_id, description?, days_until_expiry?) - Generate deployment key

  • get_deployment_payload(deployment_id) - Get sample payload data for deployment

  • gpt_generate_report(model_id, version_id, ...) - Generate GPT report

Security

Authentication

The server requires authentication via:

  • Bearer tokens for MCP client connections

  • API keys for Xplainable backend (from environment only)

Transport Security

  • Default binding to localhost only

  • TLS termination at reverse proxy recommended

  • Origin/Host header validation

Rate Limiting

Per-tool and per-principal rate limits are enforced to prevent abuse.

Synchronization with xplainable-client

When the xplainable-client library is updated, use these tools to keep the MCP server synchronized:

Quick Sync Check

# Check if sync is needed python scripts/sync_workflow.py # Generate detailed report python scripts/sync_workflow.py --markdown sync_report.md # Check current tool coverage xplainable-mcp-cli list-tools --format json

Comprehensive Sync Process

  1. Read the sync workflow guide: SYNC_WORKFLOW.md

  2. Review common scenarios: examples/sync_scenarios.md

  3. Run automated analysis: python scripts/sync_workflow.py

  4. Implement changes following the patterns in

  5. Test thoroughly and update documentation

Development

Setup

# Clone the repository git clone https://github.com/xplainable/xplainable-mcp-server cd xplainable-mcp-server # Install development dependencies pip install -e ".[dev]" # Run tests pytest # Type checking mypy xplainable_mcp # Linting ruff check . black --check .

Testing

# Run all tests pytest # Run with coverage pytest --cov=xplainable_mcp # Run specific tests pytest tests/test_tools.py

Deployment

Docker

# Build the image docker build -t xplainable-mcp-server . # Run with environment file docker run --env-file .env -p 8000:8000 xplainable-mcp-server

Compatibility Matrix

MCP Server Version

Xplainable Client

Backend API

0.1.x

>=1.0.0

v1

Contributing

See CONTRIBUTING.md for guidelines.

Security

For security issues, please see SECURITY.md.

License

MIT License - see LICENSE for details.

This server cannot be installed

-
security - not tested
A
license - permissive license
-
quality - not tested

How are these scores calculated?

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