modelcontextstandard

Model Context Standard (MCS)

A lightweight alternative to MCP – removing the fluff.

MCS describes a simple, robust and protocol‑agnostic approach to connecting language models with external tools. Unlike MCP, which introduces a custom protocol stack with transport, initialization and messaging layers, MCS asks a simpler question: What is technically necessary to connect LLMs to external systems?

At the end of the day, function-calling LLMs need to connect to the environment, and MCS provides the absolute minimum required to make that happen. Without any further "decoration".

Core Idea

LLM ──► JSON call ──► Parser/Driver ──► Bridge ──► External API
                       ▲                          │
                       └──────── Result ◄─────────┘

1. Bridge – a transport layer (e.g. HTTP, CAN, AS2)
2. Spec – a structured description of available functions (e.g. OpenAPI)

That’s all. The model emits structured calls, a driver executes them via the bridge and returns the result.

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Why MCS?

• Uses existing standards instead of inventing new ones
• Avoids the security pitfalls of custom stacks (unlike MCP)
• Leverages familiar tooling such as Swagger, Postman, FastAPI, Express, Spring Boot
• Enables reuse of existing APIs without refactoring
• Supports optional autostart via containers (Docker)
• Allows domain‑specific drivers: REST, EDI/AS2, CAN‑Bus, OPC‑UA …

MCS acts like a device‑driver model: once a driver for a domain exists, any application (or LLM) can access that domain in a consistent way.

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What This Repo Provides

• Reference driver: REST over HTTP
• Working FastAPI backend <placeholder‑link>
• OpenAPI‑based service discovery
• Minimal parser to extract and execute model‑generated calls

Quick Start

# clone and run demo driver
$ git clone <repo-url>
$ cd mcs
$ docker compose up -d        # starts the FastAPI demo bridge

Point your LLM (e.g. ChatGPT with Browser) to http://localhost:8000/openapi.json and watch it call the Fibonacci tool.

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Proof of Concept – try MCS in 2 minutes

You can verify the MCS pattern with any LLM that has web access by spinning up the tiny FastAPI demo included in this repo.

# clone on a VPS / cloud VM with a public DNS or IP
$ git clone <repo-url>
$ cd modelcontextstandard
$ docker compose -f docker/quickstart/docker-compose.yml up -d  # exposes :8000 on your public host
# optional: use a tunnel such as ngrok or cloudflared if you do not have a static IP

🛠️ Tip: Platforms like Coolify or Render make one‑click deployment of Dockerised apps very easy.

No server handy? A public demo is temporarily available at:

https://mcs-quickstart.coolify.alsdienst.de

(as long as the endpoint is up).

The demo service is implemented in fastapi_server_mcs_quickstart.py and exposes two endpoints:

Path	Purpose
/openapi-html	serves the OpenAPI spec as HTML (LLM‑readable)
/tools/fibonacci?n=	returns 2 × Fibonacci(n) to detect hallucination

How to test with an LLM

1. Ensure the demo is reachable under a public domain (or use the hosted URL above).
2. Ask the LLM to fetch /openapi-html and construct the URL for the Fibonacci tool.
3. In a second prompt, ask the LLM to visit that URL (e.g. ...?n=8).
4. A correct call returns 42. If the model answers 21, it hallucinated.

Model	Result	Notes
ChatGPT (Browser)	✅	Requires two prompts → sample
Claude 3 (web access)	✅	Needs two‑step flow → sample
Gemini	❌	Refuses second request
Grok	❌	Mis‑parses OpenAPI and builds wrong URL
DeepSeek	❌	Hallucinates, cannot target URL

🔍 POST requests are not required for this smoke test. Simple GETs confirm that the pattern works. Extending the driver to support POST + JSON is straightforward.

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How to Contribute

We welcome contributions that

• refine the formal description of the MCS pattern
• provide new drivers for other transport layers or data formats

Feel free to fork, extend and build upon this codebase.

Proof‑of‑Work Notice – This repository is shared as is. Pull requests and issues are welcome, but there is no guarantee of review, merging or long‑term maintenance. Contributions will be evaluated based on alignment with the research goals and available time.

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Limitations

MCS is a pattern, not a full framework. For every new protocol or format you still need to supply a driver/bridge. This repo ships exactly one: REST over HTTP.

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License

<placeholder‑license>

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Contact

Open a Discussion or mention @<your‑username> in an Issue/PR.

If you build something on top of MCS, we would love to hear about it!