Dell Enterprise MCP Workflow Proxy
Provides integration with Dell enterprise hardware management APIs, enabling interaction with Dell OpenAPI endpoints and Redfish API for managing Dell hardware.
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Dell Enterprise MCP Workflow Proxyrun inventory workflow for all servers"
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.
Dell Enterprise MCP Workflow Proxy
An air-gapped, edge-native, deterministic translation layer. This system ingests 500+ raw Dell OpenAPI endpoints, clusters them into 20 high-level workflows using a local offline LLM, and executes them deterministically at runtime via FastMCP and HTTPX.
🏗️ Architecture Overview
The system design strictly separates ingest/clustering compile-time phases from the deterministic runtime translation execution:
flowchart TD
subgraph Compile-Time Phases
A[Raw OpenAPI Specs] -->|Phase 1: Ingestion| B[OpenAPI Parser]
B -->|OpenAPI schemas| C[AI Clustering Engine]
C -->|Local Ollama/Instructor| D[(Workflow Mappings JSON)]
end
subgraph Runtime Execution
E[MCP Clients] -->|MCP Protocol| F[FastMCP Server]
D -->|Deterministic Routes| F
F -->|Select Executor| G[BaseExecutor Interface]
G -->|Hot-Swapped| H[HTTPX Executor]
G -->|Hot-Swapped| I[Dell OM SDK / Redfish API]
endCore Architecture Principles
Deterministic Execution: All LLM clustering is done out-of-band during the compile/development phase. The runtime operates strictly deterministically using the pre-computed
workflow_mapping.json.Interface Segregation: Target-system communications are decoupled from the routing and schema translation layers, enabling clean testing and production switching.
Air-gapped & Offline First: Local parsing via
openapi-coreand offline AI clustering viainstructor+ollama.
Related MCP server: Agentic Control Framework (ACF)
📁 Project Directory Scaffolding
dell_mcp_proxy/
├── data/
│ ├── raw_specs/ # Downloaded raw Dell OpenAPI YAML/JSON specs
│ └── output/ # Generated workflow_mapping.json mapping file
├── src/
│ ├── __init__.py
│ ├── core/ # Shared Pydantic models, configurations, and exceptions
│ │ ├── __init__.py
│ │ ├── config.py
│ │ └── exceptions.py
│ ├── parser/ # Phase 1: OpenAPI spec parsing and schema extraction
│ │ ├── __init__.py
│ │ └── openapi_parser.py
│ ├── ai_clustering/ # Phase 2: Offline clustering via Instructor/Ollama LLM
│ │ ├── __init__.py
│ │ └── workflow_generator.py
│ └── proxy/ # Phase 3: FastMCP Server & Deterministic Routing
│ ├── __init__.py
│ ├── server.py # FastMCP initialization & tool registrations
│ └── executors/ # Target execution engines
│ ├── __init__.py
│ ├── base.py # BaseExecutor Abstract Base Class
│ └── httpx_executor.py # HTTPX executor implementation
├── tests/
│ ├── __init__.py
│ └── conftest.py
├── .env.example # Local environment template variables
├── .flake8 # Flake8 style config
├── pyproject.toml # Project dependencies, packaging, and tool overrides
└── README.md # System documentation🛠️ Technical Stack & Dependencies
Language: Python 3.10+
Dependency & Environment Manager:
uvCore Libraries:
pydantic (V2): For structured data validation and modeling.fastmcp: High-performance Python framework for Model Context Protocol servers.httpx: Async HTTP client for mock and target communications.openapi-core: Strict OpenAPI parsing and validation.instructor&ollama: Structured local offline LLM interactions.
Code Quality:
black: Strict formatting (88-char limit).flake8: Style guide enforcement.mypy: Strict type checking (strict = true).pytest: Runtime test runner.
🚀 Getting Started & Local Setup
1. Prerequisites
Ensure you have Python 3.10+ and uv installed. If you do not have uv, install it via:
curl -LsSf https://astral.sh/uv/install.sh | sh2. Environment Setup
Initialize the virtual environment and install all dependencies:
# Sync environment and lock dependencies
uv sync3. Environment Variables
Copy the template variables file:
cp .env.example .env4. Running & Listing Tools
Activate the virtual environment:
source .venv/bin/activateList the registered workflow tools on the MCP server:
fastmcp list src/proxy/server.pyRun the FastMCP development console to test tools in your browser:
fastmcp dev src/proxy/server.py🛡️ Code Quality & Verification Commands
All files conform to strict enterprise quality checks. Verify your changes using:
# Code Formatting (Black)
uv run black --check .
# Code Linting (Flake8)
uv run flake8 .
# Strict Type Checking (Mypy)
uv run mypy .
# Run Tests
uv run pytest🔌 The Executor Contract (BaseExecutor)
To ensure clean hot-swapping between testing frameworks and production environments, the system abstracts execution behind src/proxy/executors/base.py.
from abc import ABC, abstractmethod
from typing import Any, Dict
class BaseExecutor(ABC):
@abstractmethod
async def execute_workflow(
self, workflow_name: str, params: Dict[str, Any]
) -> Dict[str, Any]:
"""
Asynchronously execute a clustered workflow.
"""
passHTTPXExecutor: Used for mock API endpoints and REST verification during development.
OMSDKExecutor: Used to swap in official Dell omsdk capabilities for hardware testing.
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