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deadSwank001

Nexus-MCP

by deadSwank001
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Python
Local

Nexus-MCP ( Foundry-Reverse )

Neuerer Foundry-MCP, der für die Arbeit mit Ollama reverse-engineert wurde

Eine vollständig lokale Open-Source-Alternative zum Azure AI Foundry MCP Server, die vollständig von Ollama betrieben wird. Kein Azure-Abonnement, keine API-Schlüssel, keine Cloud erforderlich.

Funktionen

Kategorie

Tools

Status

health_check

Modellverwaltung

list_models, get_model_info, pull_model, delete_model, list_running_models, compare_models

Inferenz

generate, chat

Evaluierung

evaluate_response, evaluate_agent

Wissen / RAG

create_index, list_indexes, add_document, query_knowledge, delete_index

Zusätzlich MCP-Ressourcen (ollama://models, ollama://running, ollama://indexes) und wiederverwendbare Prompts (summarize, rag_answer, code_review).

Related MCP server: mcplex

Anforderungen

  • Python 3.12+

  • Ollama lokal ausgeführt (ollama serve)

  • uv (empfohlen) oder pip

Schnellstart

# 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

Kopieren Sie mcp.json in das .vscode/mcp.json Ihres VS Code-Arbeitsbereichs (oder in die MCP-Konfiguration auf Benutzerebene) und starten Sie VS Code neu.

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

Konfiguration

Variable

Standard

Beschreibung

OLLAMA_BASE_URL

http://localhost:11434

Ollama API-Endpunkt

OLLAMA_TIMEOUT

120

Anfrage-Timeout in Sekunden

EMBED_MODEL

nomic-embed-text

Ollama-Modell für Embeddings

JUDGE_MODEL

(erstes verfügbares)

Ollama-Modell als Evaluator

KNOWLEDGE_STORE_PATH

.foundry_knowledge.json

Pfad für den lokalen RAG-Speicher

Entwicklung

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

# Run tests
uv run pytest

Architektur

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)

Vergleich mit Azure AI Foundry MCP

Funktion

Azure Foundry MCP

Foundry-Reverse

Modellkatalog

Azure AI Modell-Registry

Lokale Ollama-Modelle

Inferenz

Azure OpenAI / serverless

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

Embeddings

Azure OpenAI Embeddings

Ollama (/api/embeddings)

Vektorsuche

Azure AI Search

In-Memory Kosinus-Ähnlichkeit

Evaluierung

Azure AI Evaluation SDK

Lokales LLM-als-Richter

Authentifizierung

Azure Service Principal / Schlüssel

Keine (nur lokal)

Kosten

Pay-per-Token

Kostenlos

Install Server
F
license - not found
B
quality
D
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

Resources

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