Which integrations are available for this server?

Provides a read-only semantic gateway to the DataForge Product API by Business Qlik, enabling AI agents to browse projects and versions, and retrieve normalized metadata including measures, dimensions, and full Relationship Metadata (RMD).

How do I use DataForge Semantic MCP Server?

1. Click on "Install Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@DataForge Semantic MCP Server list all measures and dimensions for project 392 version 948" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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DataForge Semantic MCP Server

by SGromych

Overview Schema Related Servers Score Discussions

Python

Remote

DataForge Semantic MCP Server

Read-only semantic gateway between AI agents and DataForge Product API. Fetches projects, versions, measures, dimensions, facts and full RMD (Reference Model Data) plus data model entities (data marts, connections, dimension groups, fact tables, relationships), normalizes and caches the data, and exposes it via MCP protocol or as a Python library.

What This Server Provides

DataForge stores semantic layer metadata -- the business definitions of measures, dimensions, and facts used in analytics projects. This MCP server gives AI agents structured access to that metadata:

Projects -- top-level containers for analytics models
Versions -- snapshots of a project's semantic layer (one version can be marked as "global"/production)
Measures -- business metrics (e.g. "Total Revenue", "Gross Margin") with formulas, data types, source mappings
Dimensions -- attributes for slicing data (e.g. "Customer Segment", "Region") with grouping and source mappings
Facts -- factual data elements (e.g. "Order Amount") with types, formulas, source mappings
RMD -- full Reference Model Data combining all measures, dimensions, and facts in one response

Additionally, the DF API surface exposes data model entities:

Data Marts -- logical data marts with selected measures, dimensions, facts
Connections -- database connections with types and status
Dimension Groups -- logical groupings of dimensions with primary keys
Fact Tables -- physical fact tables with measures, dimensions, facts
Relationships -- links between fact tables and dimension groups
Consolidated RMD -- full data model export via DF API

All responses are JSON. The server normalizes raw DataForge API fields into a clean, stable schema so AI agents get consistent data regardless of API changes.

Features

Library-first -- use directly from Python, no MCP server required
MCP adapter -- 22 tools for Claude Desktop, Cursor and other MCP clients
Caching -- file-based cache with TTL and last-known-good fallback
Normalization -- inconsistent API fields mapped to clean canonical models
Retry & error handling -- exponential backoff on 5xx, proper error codes for auth issues

Quick Start

Installation

pip install -e ".[dev]"

Configuration

Copy .env.example to .env and set your values:

DATAFORGE_BASE_URL=https://api.prod-df.businessqlik.com
DATAFORGE_API_KEY=your_api_key_here
DEFAULT_LANGUAGE=ru

As a Python Library

import asyncio
from dataforge_mcp import create_semantic_service

async def main():
    service = create_semantic_service()

    projects = await service.list_projects()
    print(projects)

    versions = await service.list_versions(project_id=392)
    print(versions)

    rmd = await service.get_rmd(project_id=392, version_id=948)
    print(f"Measures: {rmd['stats']['measure_count']}")
    print(f"Dimensions: {rmd['stats']['dimension_count']}")
    print(f"Facts: {rmd['stats']['fact_count']}")

    # DF API: data model entities
    data_marts = await service.list_data_marts(project_id=392, version_id=948)
    print(data_marts)

asyncio.run(main())

As an MCP Server (stdio)

python -m dataforge_mcp

Add to Claude Desktop config (claude_desktop_config.json):

{
  "mcpServers": {
    "dataforge": {
      "command": "python",
      "args": ["-m", "dataforge_mcp"],
      "env": {
        "DATAFORGE_BASE_URL": "https://api.prod-df.businessqlik.com",
        "DATAFORGE_API_KEY": "your_api_key_here"
      }
    }
  }
}

Docker (SSE mode)

cp .env.example .env
# edit .env with your API key
docker compose up

MCP Tools -- Detailed Reference

All tools return JSON via MCP TextContent. Every response has a stable schema described below.

RMD API Tools

`df_health`

Check server, API and cache status.

Input: none

Output:

{
  "server_status": "ok",
  "product_api_status": "ok",
  "base_url": "https://api.prod-df.businessqlik.com",
  "cache_status": "ok"
}

Field	Type	Description
`server_status`	`string`	Always `"ok"` if the server is running
`product_api_status`	`string`	`"ok"` or `"unavailable"`
`base_url`	`string`	Configured DataForge API base URL
`cache_status`	`string`	`"ok"` or `"unavailable"`

`df_list_projects`

List available DataForge projects accessible with the configured API key.

Input:

Parameter	Type	Default	Description
`page`	`integer`	`1`	Page number
`page_size`	`integer`	`100`	Items per page
`use_cache`	`boolean`	`true`	Use cached data if available

Output:

{
  "projects": [
    {
      "id": 392,
      "name": "Fashion Retail",
      "description": "Retail analytics project"
    }
  ],
  "pagination": {
    "total": 10,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Project fields:

Field	Type	Description
`id`	`integer`	Unique project identifier
`name`	`string`	Project name
`description`	`string \| null`	Project description

`df_list_versions`

List versions for a specific project.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "versions": [
    {
      "id": 948,
      "name": "Global Version",
      "is_global": true
    }
  ],
  "pagination": {
    "total": 5,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Version fields:

Field	Type	Description
`id`	`integer`	Unique version identifier
`name`	`string`	Version name
`is_global`	`boolean`	Whether this is the global (production) version

`df_get_measures`

Get all measures (business metrics) for a project version.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default (`ru`)	Language for localized names
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "measures": [
    {
      "row_number": "1",
      "group": "Sales",
      "block": "Revenue",
      "name": "Total Revenue",
      "description": "Total revenue from all sales",
      "data_type": "Numeric",
      "measure_type": "Sum",
      "formula": "[Revenue]",
      "restrictions": null,
      "connected_source": {
        "db": "Sales DB",
        "schema": null,
        "table": "sales_table",
        "column": null
      },
      "original_source_type": "Database",
      "original_source": "Sales DB",
      "original_object": "sales_table",
      "report_for_verification": null,
      "comment": "Primary revenue metric",
      "display_data_type": null,
      "status": "Active",
      "relevance": "High",
      "required": true,
      "visibility": "Public",
      "responsible_for_data": "Data Team",
      "variation": null,
      "raw": {}
    }
  ]
}

Measure fields:

Field	Type	Description
`row_number`	`string \| integer \| null`	Row number from RMD spreadsheet
`group`	`string \| null`	Business group (e.g. "Sales", "Finance")
`block`	`string \| null`	Block within a group (e.g. "Revenue", "Costs")
`name`	`string \| null`	Measure name (localized)
`description`	`string \| null`	Human-readable description
`data_type`	`string \| null`	Data type (e.g. "Numeric", "String")
`measure_type`	`string \| null`	Aggregation type (e.g. "Sum", "Count", "Average")
`formula`	`string \| null`	Calculation formula
`restrictions`	`string \| null`	Any restrictions or filters applied
`connected_source`	`object \| null`	Database source mapping (see Connected Source)
`original_source_type`	`string \| null`	Source type (e.g. "Database")
`original_source`	`string \| null`	Original data source name
`original_object`	`string \| null`	Original object (table/view) name
`report_for_verification`	`string \| null`	Report used to verify this measure
`comment`	`string \| null`	Additional notes
`display_data_type`	`string \| null`	Display format type
`status`	`string \| null`	Status (e.g. "Active", "Draft")
`relevance`	`string \| null`	Relevance level (e.g. "High", "Medium", "Low")
`required`	`boolean \| null`	Whether the measure is required
`visibility`	`string \| null`	Visibility level (e.g. "Public", "Internal")
`responsible_for_data`	`string \| null`	Team/person responsible for data quality
`variation`	`string \| null`	Measure variation/variant
`raw`	`object`	Original unmodified API response (empty by default)

`df_get_dimensions`

Get all dimensions (attributes for slicing/filtering data) for a project version.

Input: same as df_get_measures.

Output:

{
  "project_id": 392,
  "version_id": 948,
  "dimensions": [
    {
      "row_number": "1",
      "group": "Sales",
      "block": "Customer",
      "name": "Customer ID",
      "description": "Unique customer identifier",
      "dimension_group": "Customer Attributes",
      "data_type": "String",
      "connected_source": {
        "db": 161,
        "schema": null,
        "table": "customers",
        "column": "CompanyName"
      },
      "dimension_type": null,
      "original_source_type": "Database",
      "original_source": "Sales DB",
      "original_object": "customers_table",
      "comment": "Primary key for customers",
      "formula": null,
      "value_options": null,
      "display_data_type": null,
      "source_data_type": null,
      "status": "Active",
      "relevance": "High",
      "required": true,
      "visibility": "Public",
      "responsible_for_data": "Data Team",
      "raw": {}
    }
  ]
}

Dimension fields:

Field	Type	Description
`row_number`	`string \| integer \| null`	Row number from RMD spreadsheet
`group`	`string \| null`	Business group
`block`	`string \| null`	Block within a group
`name`	`string \| null`	Dimension name (localized)
`description`	`string \| null`	Human-readable description
`dimension_group`	`string \| null`	Logical grouping of dimensions (e.g. "Customer Attributes")
`data_type`	`string \| null`	Data type (e.g. "String", "Integer", "Date")
`connected_source`	`object \| null`	Database source mapping (see Connected Source)
`dimension_type`	`string \| null`	Dimension type classification
`original_source_type`	`string \| null`	Source type
`original_source`	`string \| null`	Original data source name
`original_object`	`string \| null`	Original object name
`comment`	`string \| null`	Additional notes
`formula`	`string \| null`	Calculation formula (if computed)
`value_options`	`string \| array \| null`	Allowed values or value list
`display_data_type`	`string \| null`	Display format type
`source_data_type`	`string \| null`	Data type in the source system
`status`	`string \| null`	Status (e.g. "Active", "Draft")
`relevance`	`string \| null`	Relevance level
`required`	`boolean \| null`	Whether the dimension is required
`visibility`	`string \| null`	Visibility level
`responsible_for_data`	`string \| null`	Team/person responsible for data quality
`raw`	`object`	Original unmodified API response (empty by default)

`df_get_facts`

Get all facts for a project version.

Input: same as df_get_measures.

Output:

{
  "project_id": 392,
  "version_id": 948,
  "facts": [
    {
      "row_number": "1",
      "group": "Sales",
      "block": "Orders",
      "name": "Order Amount",
      "description": "Total order amount",
      "original_source_type": "Database",
      "original_source": "Sales DB",
      "original_object": "orders_table",
      "source_data_type": "Decimal",
      "fact_type": "Additive",
      "formula": "[Amount]",
      "connected_source": {
        "db": "Sales DB",
        "schema": null,
        "table": "orders",
        "column": null
      },
      "report_for_verification": null,
      "comment": null,
      "status": "Active",
      "relevance": "High",
      "required": true,
      "visibility": "Public",
      "responsible_for_data": "Data Team",
      "raw": {}
    }
  ]
}

Fact fields:

Field	Type	Description
`row_number`	`string \| integer \| null`	Row number from RMD spreadsheet
`group`	`string \| null`	Business group
`block`	`string \| null`	Block within a group
`name`	`string \| null`	Fact name (localized, from `fact_name`)
`description`	`string \| null`	Description (from `fact_description`)
`original_source_type`	`string \| null`	Source type
`original_source`	`string \| null`	Original data source name
`original_object`	`string \| null`	Original object name
`source_data_type`	`string \| null`	Data type in source system
`fact_type`	`string \| null`	Fact type (e.g. "Additive")
`formula`	`string \| null`	Calculation formula
`connected_source`	`object \| null`	Database source mapping (see Connected Source)
`report_for_verification`	`string \| null`	Verification report
`comment`	`string \| null`	Additional notes
`status`	`string \| null`	Status (e.g. "Active", "Draft")
`relevance`	`string \| null`	Relevance level
`required`	`boolean \| null`	Whether the fact is required
`visibility`	`string \| null`	Visibility level
`responsible_for_data`	`string \| null`	Responsible team/person
`raw`	`object`	Original unmodified API response (empty by default)

`df_get_rmd`

Get full RMD -- all measures, dimensions, and facts for a project version in a single call. This is the most common starting point for AI agents.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language for localized names
`use_cache`	`boolean`	no	`true`	Use cached data
`include_raw`	`boolean`	no	`false`	Include raw API response in each entity

Output:

{
  "project": {
    "id": 392,
    "name": "",
    "description": null
  },
  "version": {
    "id": 948,
    "name": "",
    "is_global": false
  },
  "measures": [
    {
      "name": "Total Revenue",
      "description": "Total revenue from all sales",
      "measure_type": "Sum",
      "formula": "[Revenue]",
      "..."
    }
  ],
  "dimensions": [
    {
      "name": "Customer ID",
      "description": "Unique customer identifier",
      "dimension_group": "Customer Attributes",
      "..."
    }
  ],
  "facts": [
    {
      "name": "Order Amount",
      "description": "Total order amount",
      "fact_type": "Additive",
      "..."
    }
  ],
  "stats": {
    "measure_count": 120,
    "dimension_count": 75,
    "fact_count": 30
  }
}

When include_raw is false (default), the raw field is stripped from every measure, dimension, and fact to reduce response size. Each entity in measures, dimensions, facts has the same fields as described in df_get_measures, df_get_dimensions, df_get_facts respectively.

`df_refresh_cache`

Force refresh cached data for a project version. Invalidates measures, dimensions, facts, and RMD cache keys, then re-fetches RMD.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language

Output:

{
  "status": "refreshed",
  "cache_key": "rmd:392:948:ru",
  "fetched_at": "2026-03-29T12:00:00+00:00"
}

Field	Type	Description
`status`	`string`	Always `"refreshed"` on success
`cache_key`	`string`	The primary cache key that was refreshed
`fetched_at`	`string`	ISO 8601 timestamp of when data was fetched

DF API Tools (Data Model)

All DF API tools require project_id and version_id. They use the same base URL and API key as RMD API tools. These tools expose the physical data model: how measures, dimensions, and facts are organized into data marts, fact tables, dimension groups, and connected via relationships.

`df_list_data_marts`

List data marts for a project version. Data marts are logical groupings that combine selected measures, dimensions, and facts from fact tables.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`type`	`string`	no	--	Filter by data mart type
`merge_type`	`string`	no	--	Filter by merge type
`search`	`string`	no	--	Search by name
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "data_marts": [
    {
      "id": 1,
      "name": "Sales Mart",
      "type": "standard",
      "merge_type": "union",
      "description": "Main sales data mart"
    }
  ],
  "pagination": {
    "total": 3,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Field	Type	Description
`id`	`integer`	Unique data mart identifier
`name`	`string \| null`	Data mart name
`type`	`string \| null`	Data mart type
`merge_type`	`string \| null`	How multiple sources are merged
`description`	`string \| null`	Data mart description

`df_get_data_mart`

Get data mart details including selected measures, dimensions, facts, and source fact tables.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`data_mart_id`	`integer`	yes	--	Data mart ID
`language`	`string`	no	config default	Language
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "id": 1,
  "name": "Sales Mart",
  "type": "standard",
  "merge_type": "union",
  "description": "Main sales data mart",
  "measures": [
    { "id": 10, "name": "Revenue" }
  ],
  "dimensions": [
    { "id": 20, "name": "Date" }
  ],
  "facts": [],
  "source_fact_tables": [
    { "id": 30, "name": "orders" }
  ]
}

Field	Type	Description
`id`	`integer`	Data mart ID
`name`	`string \| null`	Data mart name
`type`	`string \| null`	Data mart type
`merge_type`	`string \| null`	Merge type
`description`	`string \| null`	Description
`measures`	`array`	Selected measures in this data mart
`dimensions`	`array`	Selected dimensions in this data mart
`facts`	`array`	Selected facts in this data mart
`source_fact_tables`	`array`	Fact tables that feed this data mart

`df_get_data_mart_view`

Get physical view metadata for a data mart.

Input:

Parameter	Type	Required	Description
`project_id`	`integer`	yes	Project ID
`version_id`	`integer`	yes	Version ID
`data_mart_id`	`integer`	yes	Data mart ID

Output: object with physical view metadata (structure depends on the data mart configuration).

`df_generate_sql`

Generate SQL script for a data mart. This is read-only -- the SQL is generated but never executed.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`data_mart_id`	`integer`	yes	--	Data mart ID
`limit`	`integer`	no	--	Row limit for generated SQL
`offset`	`integer`	no	--	Row offset for generated SQL

Output:

{
  "sql": "SELECT * FROM orders LIMIT 100"
}

Field	Type	Description
`sql`	`string \| null`	Generated SQL query

`df_list_connections`

List database connections for a project version.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`db_type`	`string`	no	--	Filter by database type (e.g. "postgresql", "mssql")
`status`	`string`	no	--	Filter by connection status
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "connections": [
    {
      "id": 1,
      "name": "Main DB",
      "db_type": "postgresql",
      "status": "active"
    }
  ],
  "pagination": {
    "total": 2,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Field	Type	Description
`id`	`integer`	Connection identifier
`name`	`string \| null`	Connection name
`db_type`	`string \| null`	Database type (e.g. "postgresql", "mssql", "clickhouse")
`status`	`string \| null`	Connection status

`df_get_connection`

Get connection details. Optionally include database schema.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`connection_id`	`integer`	yes	--	Connection ID
`language`	`string`	no	config default	Language
`include_db_schema`	`boolean`	no	`false`	Include full database schema in response
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "id": 1,
  "name": "Main DB",
  "db_type": "postgresql",
  "status": "active"
}

Response may contain additional fields depending on the connection type. When include_db_schema is true, the response includes the full database schema (tables, columns, types).

`df_get_connection_schema`

Get database schema snapshot for a connection -- tables, columns, and data types.

Input:

Parameter	Type	Required	Description
`project_id`	`integer`	yes	Project ID
`version_id`	`integer`	yes	Version ID
`connection_id`	`integer`	yes	Connection ID

Output: object with database schema structure (tables, columns, types). Structure depends on the database type.

`df_list_dimension_groups`

List dimension groups for a project version. Dimension groups are logical collections of related dimensions (e.g. "Customer Attributes", "Date Hierarchy") linked to fact tables.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "dimension_groups": [
    {
      "id": 1,
      "name": "Date Group",
      "primary_key": "date_id"
    }
  ],
  "pagination": {
    "total": 5,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Field	Type	Description
`id`	`integer`	Dimension group identifier
`name`	`string \| null`	Dimension group name
`primary_key`	`string \| null`	Primary key column name

`df_get_dimension_group`

Get dimension group details including contained dimensions and related fact tables.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`dimension_group_id`	`integer`	yes	--	Dimension group ID
`language`	`string`	no	config default	Language
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "id": 1,
  "name": "Date Group",
  "primary_key": "date_id",
  "dimensions": [
    { "id": 10, "name": "Year" },
    { "id": 11, "name": "Month" }
  ],
  "related_fact_tables": [
    { "id": 20, "name": "orders" }
  ]
}

Field	Type	Description
`id`	`integer`	Dimension group ID
`name`	`string \| null`	Dimension group name
`primary_key`	`string \| null`	Primary key column
`dimensions`	`array`	Dimensions belonging to this group
`related_fact_tables`	`array`	Fact tables linked to this dimension group

`df_list_fact_tables`

List fact tables for a project version. Fact tables are the physical tables that store measures, dimensions, and facts.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "fact_tables": [
    {
      "id": 1,
      "name": "orders"
    }
  ],
  "pagination": {
    "total": 4,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Field	Type	Description
`id`	`integer`	Fact table identifier
`name`	`string \| null`	Fact table name

`df_get_fact_table`

Get fact table details including measures, dimensions, facts, dimension groups, and verification filters.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`fact_table_id`	`integer`	yes	--	Fact table ID
`language`	`string`	no	config default	Language
`include_dependencies`	`boolean`	no	`false`	Include dependency information
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "id": 1,
  "name": "orders",
  "measures": [
    { "id": 10, "name": "Revenue" }
  ],
  "dimensions": [
    { "id": 20, "name": "Customer ID" }
  ],
  "facts": [
    { "id": 30, "name": "Order Amount" }
  ],
  "dimension_groups": [
    { "id": 1, "name": "Date Group" }
  ],
  "verification_filters": []
}

Field	Type	Description
`id`	`integer`	Fact table ID
`name`	`string \| null`	Fact table name
`measures`	`array`	Measures in this fact table
`dimensions`	`array`	Dimensions in this fact table
`facts`	`array`	Facts in this fact table
`dimension_groups`	`array`	Dimension groups linked to this fact table
`verification_filters`	`array`	Verification filters configured for this fact table

`df_list_relationships`

List relationships for a project version. Relationships define how fact tables connect to dimension groups (star schema links).

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`fact_table_id`	`integer`	no	--	Filter by fact table
`dimension_group_id`	`integer`	no	--	Filter by dimension group
`page`	`integer`	no	`1`	Page number
`page_size`	`integer`	no	`100`	Items per page
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project_id": 392,
  "version_id": 948,
  "relationships": [
    {
      "id": 1,
      "fact_table_id": 1,
      "dimension_group_id": 1
    }
  ],
  "pagination": {
    "total": 8,
    "page": 1,
    "page_size": 100,
    "total_pages": 1
  }
}

Field	Type	Description
`id`	`integer`	Relationship identifier
`fact_table_id`	`integer \| null`	Linked fact table ID
`dimension_group_id`	`integer \| null`	Linked dimension group ID

`df_get_relationship`

Get relationship details between a fact table and a dimension group.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`relationship_id`	`integer`	yes	--	Relationship ID
`language`	`string`	no	config default	Language
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "id": 1,
  "fact_table_id": 1,
  "dimension_group_id": 1
}

Response may contain additional fields depending on the relationship configuration.

`df_get_consolidated_rmd`

Get consolidated RMD export via DF API -- the entire data model in one response. Includes project metadata, version info, all measures, dimensions, facts, dimension groups, fact tables, and relationships.

Input:

Parameter	Type	Required	Default	Description
`project_id`	`integer`	yes	--	Project ID
`version_id`	`integer`	yes	--	Version ID
`language`	`string`	no	config default	Language
`use_cache`	`boolean`	no	`true`	Use cached data

Output:

{
  "project": { "id": 392, "name": "Fashion Retail" },
  "version": { "id": 948, "name": "Global Version" },
  "measures": [ ... ],
  "dimensions": [ ... ],
  "facts": [ ... ],
  "dimension_groups": [ ... ],
  "fact_tables": [ ... ],
  "relationships": [ ... ],
  "exported_at": "2026-05-18T12:00:00Z"
}

Field	Type	Description
`project`	`object`	Project metadata
`version`	`object`	Version metadata
`measures`	`array`	All measures
`dimensions`	`array`	All dimensions
`facts`	`array`	All facts
`dimension_groups`	`array`	All dimension groups
`fact_tables`	`array`	All fact tables
`relationships`	`array`	All relationships
`exported_at`	`string \| null`	ISO 8601 export timestamp

Shared Response Structures

Pagination Object

Paginated endpoints return a pagination object:

{
  "total": 120,
  "page": 1,
  "page_size": 100,
  "total_pages": 2
}

Field	Type	Description
`total`	`integer`	Total number of items across all pages
`page`	`integer`	Current page number
`page_size`	`integer`	Items per page
`total_pages`	`integer`	Total number of pages

Connected Source Object

Measures, dimensions, and facts can reference their physical database source:

{
  "db": "Sales DB",
  "schema": null,
  "table": "sales_table",
  "column": "revenue"
}

Field	Type	Description
`db`	`string \| integer \| null`	Database name or ID
`schema`	`string \| null`	Database schema
`table`	`string \| null`	Table or view name
`column`	`string \| null`	Column name

Error Format

All errors follow a consistent format:

{
  "error": {
    "code": "DATAFORGE_API_KEY_INVALID",
    "message": "API error: API_KEY.INVALID_KEY",
    "details": {
      "http_status": 401
    }
  }
}

Error codes:

Code	HTTP Status	Description
`DATAFORGE_API_KEY_MISSING`	401	`X-Api-Key` header not provided
`DATAFORGE_API_KEY_INVALID`	401	Invalid API key
`DATAFORGE_UNAUTHORIZED`	401	Unauthorized
`DATAFORGE_AUTH_FAILED`	401	Authentication failed
`DATAFORGE_ACCOUNT_LOCKED`	403	Account locked
`DATAFORGE_IP_BLOCKED`	403	IP address blocked
`DATAFORGE_LICENSE_INVALID`	403	No valid license
`DATAFORGE_INVALID_PARAMETER`	400	Invalid request parameter (DF API)
`DATAFORGE_PAGE_SIZE_EXCEEDED`	400	Page size too large (DF API)
`DATAFORGE_RESOURCE_NOT_FOUND`	404	Project, version, or entity not found
`DATAFORGE_SERVER_ERROR`	5xx	Server-side error (retried automatically)
`DATAFORGE_TIMEOUT`	--	Request timed out (retried automatically)
`DATAFORGE_CONNECTION_ERROR`	--	Network connection error (retried automatically)

Typical AI Agent Workflow

A typical workflow for an AI agent using this server:

1. df_health              -> verify connectivity
2. df_list_projects       -> discover available projects
3. df_list_versions       -> find the global (production) version
4. df_get_rmd             -> fetch all measures, dimensions, and facts at once
   (or df_get_measures / df_get_dimensions / df_get_facts separately)
5. Use the semantic metadata to understand the data model,
   generate queries, or answer business questions

For exploring the physical data model:

6. df_list_fact_tables       -> see available fact tables
7. df_get_fact_table         -> inspect a fact table's contents
8. df_list_dimension_groups  -> see dimension groups
9. df_list_relationships     -> understand how fact tables connect to dimension groups
10. df_list_data_marts       -> explore data marts
11. df_get_data_mart         -> get data mart details
12. df_generate_sql          -> generate SQL for a data mart
13. df_get_consolidated_rmd  -> full data model export in one call

Architecture

AI Agent / MCP Client
    |
    v
MCP Adapter (mcp/)           -- thin wrappers, no business logic
    |
    v
SemanticService (application/) -- cache-first orchestration (CORE)
    |
    +--> DataForgeClient (dataforge/) -- HTTP calls to RMD API + DF API with retry
    +--> Normalizer (semantic/)       -- raw API -> canonical models
    +--> FileCacheStore (cache/)      -- TTL + last-known-good fallback

SemanticService is the single entry point. MCP tools only delegate to it.

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Lint
ruff check src/ tests/

# Format
ruff format src/ tests/

Configuration Reference

Variable	Default	Description
`DATAFORGE_BASE_URL`	`https://api.prod-df.businessqlik.com`	DataForge API base URL
`DATAFORGE_API_KEY`	--	API key (required)
`DEFAULT_LANGUAGE`	`ru`	Default language for measures/dimensions/facts
`CACHE_DIR`	`./cache`	Cache directory path
`CACHE_TTL_SECONDS`	`3600`	Cache TTL in seconds
`MCP_TRANSPORT`	`stdio`	Transport: `stdio` or `sse`
`HOST`	`0.0.0.0`	Host for SSE mode
`PORT`	`8080`	Port for SSE mode
`LOG_LEVEL`	`INFO`	Log level

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How are these scores calculated?

Resources

GitHub Repository

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