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vpsathish05

Custom Fabric MCP

by vpsathish05
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Python

Custom Fabric MCP

An MCP (Model Context Protocol) server for AI-powered, governed access to Microsoft Fabric data assets. Enables AI agents to query Fabric Warehouses and Lakehouses with full governance, validation, and explainability.

Features

  • 16 MCP tools — query, schema discovery, knowledge retrieval, export, admin

  • Governance-first — RBAC/ABAC, PII masking, rate limiting, SQL deny patterns

  • Validated SQL — schema-aware validation, query optimization (NOLOCK, TOP), cost estimation

  • Grounded responses — citations, confidence scoring, natural-language explanations

  • Knowledge retrieval — business glossary, KPI dictionary, knowledge graph, query history

  • Caching — Redis-backed query and result cache with semantic hashing

  • Export services — charts (matplotlib), Excel (openpyxl), PDF/Word, email with confirmation gate

Quick Start

# Install dependencies
uv sync

# Install with dev tools (ruff, pytest, pyright)
uv sync --group dev

# Install with optional services (matplotlib, openpyxl)
uv sync --extra services

# Run in development mode (MCP Inspector)
uv run mcp dev src/server/main.py

# Run with MCP inspector
uv run mcp inspector src/server/main.py

# Run directly (stdio transport)
uv run python -m src.server.main

# Lint and format
uv run ruff check src/
uv run ruff format src/

# Run tests
uv run pytest tests/ -x --timeout=30

# Seed sample schema for local dev
uv run python scripts/seed-schema.py

Configuration

Copy .env.example to .env and fill in your values:

cp .env.example .env

Required Configuration

Variable

Description

FABRIC_SQL_ENDPOINT

Fabric workspace SQL endpoint URL

FABRIC_DATABASE_NAME

Database name in Fabric

AZURE_TENANT_ID

Azure AD tenant ID

AZURE_CLIENT_ID

App registration client ID

AZURE_CLIENT_SECRET

App registration client secret

Optional Configuration

Variable

Default

Description

SERVER_TRANSPORT

stdio

Transport: stdio or sse

SERVER_PORT

8080

Port for SSE transport

LOG_LEVEL

INFO

Log level

LOG_FORMAT

json

Log format: json or console

REDIS_URL

redis://localhost:6379

Redis for caching

REDIS_CACHE_TTL_SECONDS

3600

Cache TTL (1 hour)

GOVERNANCE_MAX_ROWS_DEFAULT

1000

Default row limit

GOVERNANCE_RATE_LIMIT_PER_MINUTE

20

Queries per minute

GOVERNANCE_TOKEN_BUDGET

4000

CU-second budget

See .env.example for all available options.

Architecture

User → LLM → AgentPlanner → [Retrieval → Governance → Validation → Execution → Masking → Response]
                                  ↓            ↓           ↓            ↓          ↓         ↓
                              Glossary     RBAC/PII    Optimizer     Fabric     PiiMasker  Assembler
                              KG/RAG      RateLimit   CostEstimate   pyodbc    (HMAC salt) Citations
                              History     DenyPatterns  Schema       Pool(5)    HideColumn  Confidence

Pipeline (9 steps in AgentPlanner)

  1. Retrieve context — glossary, KG, history, RAG, session memory

  2. Governance pre-check — RBAC, rate limits, deny patterns

  3. Schema discovery — INFORMATION_SCHEMA (cached with TTL)

  4. Validate + optimize — table/column existence, NOLOCK, TOP injection, cost estimate

  5. Execute — parameterized query against Fabric SQL Endpoint

  6. Post-query masking — PII redaction/partial mask/tokenize based on role × sensitivity

  7. Assemble response — citations, confidence (weighted), explanation, warnings

  8. Audit log — structured entry with user, tables, governance decisions, latency

  9. Store — query history + session memory for future reuse

Deployment

Docker

docker build -t custom-fabric-mcp .
docker run -p 8080:8080 --env-file .env custom-fabric-mcp

Azure Container Apps (Bicep)

# Deploy to dev environment
./scripts/deploy.sh dev latest

# Or use az CLI directly
az deployment group create \
  --resource-group rg-fabric-mcp-dev \
  --template-file infra/main.bicep \
  --parameters infra/parameters.dev.json

Infrastructure includes: VNET, Key Vault, Container Registry, Redis Cache, Container App with auto-scaling (1-3 replicas).

Project Structure

src/
├── common/       # Config, errors (6 types), logging (structlog)
├── server/       # MCP entry point (app factory pattern)
├── fabric/       # Auth, connection pool, query executor, schema
├── governance/   # RBAC, PII masking, policy engine, audit
├── validation/   # SQL validator, optimizer, cost estimator
├── retrieval/    # RAG, knowledge graph, glossary, history, memory
├── cache/        # Redis client, query cache, result cache
├── response/     # Citations, confidence, explainer, assembler
├── agents/       # Planner (pipeline), model router, workflow engine
├── tools/        # 16 MCP tools (the API surface)
└── services/     # Charts, Excel, documents, email, confirmation

MCP Tools

Tool

Description

health_check

Server health and configuration status

get_available_tools

Discover all available tools

query_data

Execute validated SQL against Fabric

query_revenue

Query revenue by year/region/grouping

query_kpi

Query specific KPI (total_revenue, customer_count, etc.)

get_schema

Get complete database schema

get_tables

List available tables

get_columns

Get column details for a table

search_glossary

Search business term definitions

get_kpi_definition

Get KPI calculation logic

search_docs

Search business documentation (RAG)

export_excel

Export results to .xlsx

generate_chart

Generate bar/line/pie chart (PNG)

get_query_history

Search past queries for reuse

check_access

Verify user access to tables

get_data_freshness

Check data refresh status

License

MIT

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