Qlik Sense MCP

🚀 Qlik Sense MCP Server

The Qlik Sense MCP Server integrates with Qlik Sense Enterprise APIs via the Model Context Protocol (MCP), offering a unified interface for Repository API and Engine API operations.

🚀 Quick Start

The easiest way to start using the Qlik Sense MCP Server is by using uvx:

uvx qlik-sense-mcp-server

This command will automatically install and run the latest version without affecting your system Python environment.

✨ Features

Key Features

• Unified API: A single interface for all Qlik Sense APIs.
• Security: Certificate-based authentication support.
• Performance: Optimized queries and response handling.
• Flexibility: Multiple data export formats.
• Analytics: Advanced data analysis tools.

Repository API (Fully Working)

Engine API (Fully Working)

📦 Installation

Quick Start with uvx (Recommended)

uvx qlik-sense-mcp-server

Alternative Installation Methods

From PyPI

pip install qlik-sense-mcp-server

From Source (Development)

git clone https://github.com/bintocher/qlik-sense-mcp.git
cd qlik-sense-mcp
make dev

System Requirements

• Python 3.12+
• Qlik Sense Enterprise
• Valid certificates for authentication
• Network access to Qlik Sense server

Setup

1. Setup certificates

mkdir certs
# Copy your Qlik Sense certificates to certs/ directory

2. Create configuration

cp .env.example .env
# Edit .env with your settings

💻 Usage Examples

Start Server

# Using uvx (recommended)
uvx qlik-sense-mcp-server

# Using installed package
qlik-sense-mcp-server

# From source (development)
python -m qlik_sense_mcp_server.server

Example Operations

Get Applications List

# Via MCP client
result = mcp_client.call_tool("get_apps")
print(f"Found {len(result)} applications")

Create Data Analysis Hypercube

# Create hypercube for sales analysis
result = mcp_client.call_tool("engine_create_hypercube", {
    "app_id": "your-app-id",
    "dimensions": ["Region", "Product"],
    "measures": ["Sum(Sales)", "Count(Orders)"],
    "max_rows": 1000
})

Export Data

# Export data in CSV format
result = mcp_client.call_tool("engine_create_data_export", {
    "app_id": "your-app-id",
    "table_name": "Sales",
    "format_type": "csv",
    "max_rows": 10000
})

📚 Documentation

API Reference

Repository API Functions

get_apps

Retrieves list of all Qlik Sense applications.

Parameters:

• filter (optional): Filter query for application search

Returns: Array of application objects with metadata

get_app_details

Gets detailed information about specific application.

Parameters:

• app_id (required): Application identifier

Returns: Application object with complete metadata

get_app_metadata

Retrieves comprehensive application metadata including data model.

Parameters:

• app_id (required): Application identifier

Returns: Object containing app overview, data model summary, sheets information

get_users

Retrieves list of Qlik Sense users.

Parameters:

• filter (optional): Filter query for user search

Returns: Array of user objects

get_streams

Gets list of application streams.

Parameters: None

Returns: Array of stream objects

get_tasks

Retrieves list of tasks (reload, external program).

Parameters:

• task_type (optional): Type filter ("reload", "external", "all")

Returns: Array of task objects with execution history

start_task

Executes specified task.

Parameters:

• task_id (required): Task identifier

Returns: Execution result object

get_data_connections

Gets list of data connections.

Parameters:

• filter (optional): Filter query for connection search

Returns: Array of data connection objects

get_extensions

Retrieves list of Qlik Sense extensions.

Parameters: None

Returns: Array of extension objects

get_content_libraries

Gets list of content libraries.

Parameters: None

Returns: Array of content library objects

Engine API Functions

engine_get_doc_list

Lists available documents via Engine API.

Parameters: None

Returns: Array of document objects with metadata

engine_open_app

Opens application via Engine API for further operations.

Parameters:

• app_id (required): Application identifier

Returns: Application handle object for subsequent operations

engine_get_script

Retrieves load script from application.

Parameters:

• app_id (required): Application identifier

Returns: Object containing script text and metadata

engine_get_fields

Gets list of fields from application.

Parameters:

• app_id (required): Application identifier

Returns: Array of field objects with metadata and statistics

engine_get_sheets

Retrieves application sheets.

Parameters:

• app_id (required): Application identifier

Returns: Array of sheet objects with metadata

engine_get_table_data

Extracts data from application tables.

Parameters:

• app_id (required): Application identifier
• table_name (optional): Specific table name
• max_rows (optional): Maximum rows to return (default: 1000)

Returns: Table data with headers and row information

engine_get_field_values

Gets field values with frequency information.

Parameters:

• app_id (required): Application identifier
• field_name (required): Field name
• max_values (optional): Maximum values to return (default: 100)
• include_frequency (optional): Include frequency data (default: true)

Returns: Field values with frequency and metadata

engine_get_field_statistics

Retrieves comprehensive field statistics.

Parameters:

• app_id (required): Application identifier
• field_name (required): Field name

Returns: Statistical analysis including min, max, average, median, mode, standard deviation

engine_get_data_model

Gets complete data model with tables and associations.

Parameters:

• app_id (required): Application identifier

Returns: Data model structure with relationships

engine_create_hypercube

Creates hypercube for data analysis.

Parameters:

• app_id (required): Application identifier
• dimensions (required): Array of dimension fields
• measures (required): Array of measure expressions
• max_rows (optional): Maximum rows to return (default: 1000)

Returns: Hypercube data with dimensions and measures

engine_create_data_export

Exports data in various formats.

Parameters:

• app_id (required): Application identifier
• table_name (optional): Table name for export
• fields (optional): Specific fields to export
• format_type (optional): Export format ("json", "csv", "simple")
• max_rows (optional): Maximum rows to export (default: 10000)
• filters (optional): Field filters for data selection

Returns: Exported data in specified format

Architecture

Project Structure

qlik-sense-mcp/
├── qlik_sense_mcp_server/
│   ├── __init__.py
│   ├── server.py          # Main MCP server
│   ├── config.py          # Configuration management
│   ├── repository_api.py  # Repository API client
│   └── engine_api.py      # Engine API client (WebSocket)
├── certs/                 # Certificates (git ignored)
│   ├── client.pem
│   ├── client_key.pem
│   └── root.pem
├── .env.example          # Configuration template
├── .env                  # Your configuration
├── mcp.json.example      # MCP configuration template
├── pyproject.toml        # Project dependencies
└── README.md

System Components

• QlikSenseMCPServer: Main server class handling MCP protocol operations, tool registration, and request routing.
• QlikRepositoryAPI: HTTP client for Repository API operations including applications, users, tasks, and metadata management.
• QlikEngineAPI: WebSocket client for Engine API operations including data extraction, analytics, and hypercube creation.
• QlikSenseConfig: Configuration management class handling environment variables, certificate paths, and connection settings.

Development

Development Environment Setup

The project includes a Makefile with common development tasks:

# Setup development environment
make dev

# Show all available commands
make help

# Build package
make build

Version Management and Releases

Use Makefile commands for version management:

# Bump patch version and create PR
make version-patch

# Bump minor version and create PR
make version-minor

# Bump major version and create PR
make version-major

This will automatically:

1. Bump the version in pyproject.toml
2. Create a new branch
3. Commit changes
4. Push branch and create PR

Publishing Process

1. Merge PR with version bump
2. Create and push tag to trigger automatic PyPI publication:

   git tag v1.0.1
   git push origin v1.0.1
   

3. GitHub Actions will automatically build and publish to PyPI

Clean Git History

If you need to start with a clean git history:

make git-clean

Warning: This completely removes git history!

Adding New Functions

1. Add tool definition in server.py

# In handle_list_tools()
{"name": "new_tool", "description": "Tool description", "inputSchema": {...}}

2. Add handler in server.py

# In handle_call_tool()
elif name == "new_tool":
    result = await asyncio.to_thread(self.api_client.new_method, arguments)
    return [TextContent(type="text", text=json.dumps(result, indent=2))]

3. Implement method in API client

# In repository_api.py or engine_api.py
def new_method(self, param: str) -> Dict[str, Any]:
    """Method implementation."""
    # Implementation code
    return result

Troubleshooting

Common Issues

Certificate Errors

SSL: CERTIFICATE_VERIFY_FAILED

Solution:

• Verify certificate paths in .env
• Check certificate expiration
• Set QLIK_VERIFY_SSL=false for testing

Connection Errors

ConnectionError: Failed to connect to Engine API

Solution:

• Verify port 4747 accessibility
• Check server URL correctness
• Verify firewall settings

Authentication Errors

401 Unauthorized

Solution:

• Verify QLIK_USER_DIRECTORY and QLIK_USER_ID
• Check user exists in Qlik Sense
• Verify user permissions

Diagnostics

Test Repository API

python -c "
from qlik_sense_mcp_server.config import QlikSenseConfig
from qlik_sense_mcp_server.repository_api import QlikRepositoryAPI
config = QlikSenseConfig.from_env()
api = QlikRepositoryAPI(config)
print('Apps:', len(api.get_apps()))
"

Test Engine API

python -c "
from qlik_sense_mcp_server.config import QlikSenseConfig
from qlik_sense_mcp_server.engine_api import QlikEngineAPI
config = QlikSenseConfig.from_env()
api = QlikEngineAPI(config)
api.connect()
print('Docs:', len(api.get_doc_list()))
api.disconnect()
"

Performance

Optimization Recommendations

1. Use filters to limit data volume
2. Cache results for frequently used queries
3. Limit result size with max_rows parameter
4. Use Repository API for metadata (faster than Engine API)

Benchmarks

Security

Recommendations

1. Store certificates securely - exclude from git
2. Use environment variables for sensitive data
3. Limit user permissions in Qlik Sense
4. Update certificates regularly
5. Monitor API access

Access Control

Create user in QMC with minimal required permissions:

• Read applications
• Execute tasks (if needed)
• Access Engine API

📄 License

MIT License

Copyright (c) 2025 Stanislav Chernov

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Project Status: Production Ready | 21/21 Commands Working | v1.0.0

Installation: uvx qlik-sense-mcp-server