Which integrations are available for this server?

Provides tools for managing local Ollama models (list, pull, delete), running inference (generate, chat), evaluating responses, and performing RAG with embeddings, all against a local Ollama instance.

How do I use Nexus-MCP?

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., "@Nexus-MCP chat with llama3.2" 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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Nexus-MCP

by deadSwank001

Overview Schema Related Servers Score Discussions

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).

Related MCP server: mcplex

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

license - not found

quality

maintenance

How are these scores calculated?

Maintenance

–Maintainers

–Response time

–Release cycle

–Releases (12mo)

Commit activity

Resources

GitHub Repository

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