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deadSwank001

Nexus-MCP

by deadSwank001
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Python
Local

#Nexus-MCP ( Foundry-Reverse )

Newer Foundry MCP reverse-engineered to work on Ollama

A fully local, open-source alternative to the Azure AI Foundry MCP Server, powered entirely by Ollama.
No Azure subscription, no API keys, no cloud required.

Features

Category

Tools

Health

health_check

Model Management

list_models, get_model_info, pull_model, delete_model, list_running_models, compare_models

Inference

generate, chat

Evaluation

evaluate_response, evaluate_agent

Knowledge / RAG

create_index, list_indexes, add_document, query_knowledge, delete_index

Plus MCP Resources (ollama://models, ollama://running, ollama://indexes) and reusable Prompts (summarize, rag_answer, code_review).

Requirements

  • Python 3.12+

  • Ollama running locally (ollama serve)

  • uv (recommended) or pip

Quick Start

# 1. Clone
git clone https://github.com/deadSwank001/Foundry-Reverse.git
cd Foundry-Reverse

# 2. Copy and edit configuration
cp .env.example .env

# 3. Run the MCP server (stdio transport)
uv run foundry-reverse

VS Code / Copilot Integration

Copy mcp.json to your VS Code workspace .vscode/mcp.json (or user-level MCP config), then restart VS Code.

{
  "mcpServers": {
    "foundry-reverse": {
      "command": "uv",
      "args": ["run", "foundry-reverse"],
      "env": {
        "OLLAMA_BASE_URL": "http://localhost:11434"
      }
    }
  }
}

Configuration

Variable

Default

Description

OLLAMA_BASE_URL

http://localhost:11434

Ollama API endpoint

OLLAMA_TIMEOUT

120

Request timeout in seconds

EMBED_MODEL

nomic-embed-text

Ollama model used for embeddings

JUDGE_MODEL

(first available)

Ollama model used as evaluator

KNOWLEDGE_STORE_PATH

.foundry_knowledge.json

Path for the local RAG store

Development

# Install deps (including dev)
uv sync --all-groups

# Run tests
uv run pytest

Architecture

src/foundry_reverse/
├── __init__.py          # Package version
├── server.py            # FastMCP server – all tools, resources, prompts
├── ollama_client.py     # Async Ollama REST API client
├── evaluation.py        # LLM-as-judge evaluation helpers
└── knowledge.py         # In-memory vector store (cosine similarity + Ollama embeddings)

Comparison with Azure AI Foundry MCP

Feature

Azure Foundry MCP

Foundry-Reverse

Model catalog

Azure AI model registry

Local Ollama models

Inference

Azure OpenAI / serverless

Ollama (/api/generate, /api/chat)

Embeddings

Azure OpenAI embeddings

Ollama (/api/embeddings)

Vector search

Azure AI Search

In-memory cosine similarity

Evaluation

Azure AI Evaluation SDK

Local LLM-as-judge

Auth

Azure Service Principal / keys

None (local only)

Cost

Pay-per-token

Free

Install Server
F
license - not found
B
quality
C
maintenance

How are these scores calculated?

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