Unlock the Power of LLMs: Connect Any Model to the Real World with reusable Drivers - Simpler, Safer and Infinitely Scalable

Imagine transforming your LLM into a supercharged operating system. Effortlessly linking to APIs, databases, industrial systems, or even printers without reinventing the wheel.

MCS is the lightweight standard that makes it happen.

No bloated protocols, no security headaches. Just reusable drivers that work everywhere. If you're tired of MCP's overhead and vulnerabilities, MCS is your upgrade. Built on proven tech like OpenAPI and REST, it's the future of AI integration.

Ready to Build?
→ Start with the 2-minute quickstart
→ Build your first driver with the Python SDK
→ Or use ready-made drivers: REST-HTTP (Python SDK), Filesystem-localfs, and more coming
→ Still curious? You might want to Read The Friendly Manual

MCS is a lightweight standard focused on what's essential for connecting LLMs to real systems. It is function calling at its core. Instead of building custom wrappers or servers, you configure reusable drivers that use existing API with standard specs like OpenAPI.
Mix and match, extend or trim functions by URL, no redeploys, no prompt engineering headaches, and no code changes.

Build a driver once for REST-HTTP, EDI-AS2, Filesystem-localfs or even CAN-Bus or MCP-STDIO. Every LLM app can plug into it. No more wrappers, no more reimplementation or endless prompt engineering. Just configure and connect.

MCS avoids custom stacks and utilizes battle-tested standards first like OpenAPI, REST, OAuth. This ensures better compatibility, more security, easier auditing and seamless integration into existing toolchains.

If your API already exists, why wrap it again? MCS connects directly to it. Skip the wrapper server. Just point to the spec and you're ready to go.

MCS drivers include refined prompts tested across models. No manual prompt tuning on the client/app side needed. MCS handles model-specific quirks automatically. It's future-proof too with potential dynamic hubs using checksums to auto-load updated, high-performance versions for new models.

MCP autostart is not a feature, it is a design flaw, because STDIO transport forces it. MCS avoids that. Drivers connect directly to existing systems, avoiding unnecessary CPU usage and security risks. Use autostart only when needed and do it safely. MCS makes suggestions how to implement this more safely, but in 99% the cases you do not need autostart anymore. Say goodbye to malicious servers (up to 8% on Github) 1.

MCP pioneered standardization in this area, MCS makes it practical. MCS drivers can wrap existing MCP endpoints (e.g. mcs-driver-mcp-stdio) to enable smooth migration. You get modularity, reuse and zero lock-in.

From Web APIs to industrial IoT, MCS drivers abstract the complexity. Optimize prompts per model inside the driver or orchestrators. Every LLM app benefits without bothering about model-specific quirks. Your app stays focused.

specification – The core Protocol Spec. We're optimizing this together - Join the discussion!
docs – In-depth guides and examples (coming soon)
python-sdk – Ready to use Python implementation with reference drivers. Install via pip and start building.
typescript-sdk – TypeScript SDK (in progress – based on the Python implementation)
mcs-pkg – Centralized registry for discovering and publishing MCS drivers (planned – think apt for AI tools with autodiscovery and installation).
[example-drivers] - A sample driver repo for tutorials and testing. (coming soon)
mcs-tool-driver-filesystem-localfs – A reference driver for local file system access.

We recommend starting with the specification to understand the core ideas. Once familiar, dive into the Python SDK to see how drivers are built in practice. It also serves as a blueprint for implementing SDKs in other languages.

It is simple to verify how MCS works in principle.
Use any LLM with web access to connect to a simple OpenAPI-enabled tool. This demonstrates the core concept.

This project provides a tiny FastAPI demo that exposes a readable OpenAPI HTML spec and a test function (fibonacci) that returns 2 × Fibonacci(n), helping detect hallucinations.

ℹ️ Most LLMs can currently access external content only via GET requests and basic HTML parsing. But that’s enough for this test.

# clone on a VPS / cloud VM with a public DNS or IP
$ git clone https://github.com/modelcontextstandard/modelcontextstandard.git
$ 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.

Don’t have a server? Use the hosted demo (up while supplies last):

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

The service exposes two endpoints:

1. Ensure the demo is reachable via public URL. (or use the hosted URL above).
2. Ask your LLM to open /openapi-html, understand the interface.
3. Ask the LLM to get you the fibonacci result for some value (e.g. 8).
4. (if needed) In a second prompt, ask the LLM to build and visit the resulting URL (e.g. ...?n=8).
5. A correct result is 42. If the LLM says 21, it hallucinated.

Click on the links to see the results in the chats with the LLMs.

Even without anything special in place, modern LLMs can already interact with well-described APIs. The demo shows the minimal setup needed to close the gap between LLM and real-world functions. This principle scales: By standardizing function calling itself, the direct text input/output interface to LLMs, MCS makes this integration seamless and universal.

LLMs are becoming the core of modern software stacks, but connecting them to external systems remains unnecessarily complex. MCP deserves credit for being the first serious attempt to standardize function calling. It sparked the revolution and gave developers a protocol to build upon.

But let's be real, MCP comes with a significant overhead.

MCP creates a new protocol stack on top of JSON-RPC, essentially reimplementing what HTTP has solved for decades. This means:

• Reinventing proven solutions: Authentication, request handling, error management – all rebuilt from scratch
• New security vulnerabilities: Recent discoveries show the risks of custom protocol implementations 2, 3, 4, 5, 6 etc.
• Additional complexity: Developers must learn new patterns instead of leveraging existing knowledge
• New Tooling: You can not simply reuse what you build with MCP

The irony: What you can accomplish with MCP, you can already do with REST over HTTP using battle-tested security, established tooling, and decades of optimization.

MCP's architecture forces you to create wrapper servers for every integration:

Your API → MCP Wrapper Server → MCP Client → App → LLM

This creates several problems:

• Double infrastructure: Every API needs its own wrapper server running alongside the original server
• Maintenance overhead: Each wrapper must be updated, monitored, and secured independently
• Resource waste: Unnecessary processes consuming CPU and memory
• Security multiplication: More servers mean more attack surfaces

This problem persists even with MCP frameworks that generate servers from OpenAPI specs. You're still creating an unnecessary translation layer instead of using the API directly.

MCP's STDIO-based autostart spawns processes with user privileges, creating potential security vulnerabilities:

• Untrusted code execution: Malicious MCP servers can run with full user access
• Process bloat: Background processes running even when not needed
• Privilege escalation risks: One compromised server can affect the entire system

Research suggests up to 8% of MCP servers on GitHub contain potentially malicious code 1.

For every MCP client/app developer:

• Master prompt engineering for each model (again)
• Handle protocol-specific authentication with a new protocol
• Learn new toolchains for debugging, monitoring and auditing
• Implement MCP client logic for each application

For every MCP server developer:

• Write a new wrapper server for each API you want to make available to LLMs
• If you create a MCP server with valuable logic, you cannot reuse it in classic applications without also reimplementing the MCP client
• Maintain separate codebases for MCP and non-MCP integrations

This creates a fragmented ecosystem where useful logic gets locked into MCP-specific implementations.

MCS addresses these pain points by recognizing that this is fundamentally a driver problem, not a protocol problem.

It trims function calling down to two building blocks:

Spec: Machine-readable function descriptions (OpenAPI, JSON-Schema, ...) – use standards if possible!
Bridge: Transport layers (HTTP, AS2, CAN, ...) – handled by parsers.

Just like operating systems use device drivers to communicate with hardware, LLMs need interface drivers to communicate with external systems. The key here is that an MCS driver doesn't handle a specific function or API, it handles all APIs using the same protocol over the same transport. This makes it behave like a driver for the protocol and transport, abstracting these details for the LLM and App developer.

• Write once, use everywhere: A REST-HTTP driver works across all LLM applications
• Leverage existing standards: OpenAPI, OAuth, HTTP – proven and secure
• No wrapper servers needed: Connect directly to existing APIs
• Built-in optimizations: Prompts are tested and optimized within the driver

Instead of:

Your API → MCP Wrapper → MCP Client → App → LLM

MCS enables:

Your API → MCS Driver → App → LLM

The crucial difference: A MCS driver for REST-HTTP can handle any REST API, not just one specific API. Point it to different OpenAPI specs, and it works universally without modification.

• No custom protocols: Leverage HTTP's proven security model
• Optional autostart: Only when needed, containerized and sandboxed
• Reduced attack surface: Fewer components, established security practices
• Standard authentication: OAuth, API keys, JWT – use what already works

MCS drivers are components like software modules that can be downloaded and used directly. This enables trusted driver repositories with checksum verification, similar to APT or Maven. Making secure deployment and auto-loading straightforward.

With MCS, developers get:

• Plug-and-play integration: Configure drivers instead of writing wrappers
• No prompt engineering: Optimized prompts are built into drivers
• Standard tooling: Use existing HTTP debugging and monitoring tools.
• Zero API changes: Existing services become LLM-accessible
• Modular architecture: Mix and match drivers and orchestrators as needed

Already using MCP? MCS provides a smooth transition:

• Wrap existing MCP servers as MCS drivers (mcs-driver-mcp-stdio) Gradual migration without breaking existing functionality Immediate benefits for new integrations

MCP pioneered the concept, but MCS makes it practical. While MCP requires rebuilding HTTP functionality and managing wrapper servers, MCS leverages existing standards and eliminates unnecessary complexity.

For developers, MCS delivers less code to write and maintain, faster integration with existing APIs, better security through proven protocols, and the freedom to focus on business logic instead of protocol details.

For organizations, this translates to lower infrastructure costs, reduced security risks, faster time to market, and better resource utilization across development teams.

MCS doesn't replace MCP, it evolves the concept by removing the barriers that make integration difficult. The result is a standard that's easier to adopt, safer to deploy, and simpler to maintain.

MCS is an open exploration of how LLM integration should work. The focus right now is refining the concept and proving it scales better than existing approaches.

This is a proof-of-concept, not a product. The Python SDK and examples exist to illustrate the idea and enable early experimentation. If you're looking for production-ready solutions, this isn't it...yet.
What I'm doing: Developing and refining the core concept, creating reference implementations, and exploring what's possible when we rethink LLM integration from first principles.
What I'm not doing: Providing free support, maintaining SLAs, or building your custom integrations. This is open source so everyone can learn. Take what's useful, leave what isn't.

• Specification refinement is crucial for MCS's success. We need contributors to identify edge cases, develop model-specific optimizations, and help us reduce complexity to the absolute essentials. Every improvement benefits the entire ecosystem.
• Driver development opens up new possibilities. Whether you're interested in creating drivers for GraphQL, MQTT, industrial protocols like CAN-Bus, or even specialized hardware like printers, your work enables countless applications. The Python SDK provides a solid foundation to get started.
• SDK expansion makes MCS accessible to more developers. We're particularly focused on fleshing out TypeScript support, with plans to generate interfaces from the Python implementation. If you're skilled in other languages, we'd welcome additional SDK contributions.
• Documentation and tutorials help newcomers understand MCS's potential. We're looking for contributors to create practical guides, such as "Building a Custom Driver" tutorials that demonstrate the complete workflow from initialization to automated prompt handling.
• Registry infrastructure will make driver discovery and sharing seamless. Help us launch mcs-pkg, a trusted repository system with checksum verification, similar to how APT or Maven handle package distribution. Not only for the drivers, but also with autoloading model specific prompts. See Dynamic Optimization
• Real-world examples and security best practices documentation ensure MCS deployments are both effective and secure. Share your integration experiences and help establish community standards.

• Want production support? Hire a consultant.
• Found a bug? PRs welcome, or fork it.
• Have a better approach? Be my guest. I'd genuinely love to see it.
• Just want to complain? There's a close button on your browser tab.

The best contributions are the ones that push the concept forward. If you're here to help shape the future of LLM integration, welcome aboard.

Visit our GitHub organization to explore the code, specs and drivers. For deeper discussions about the concept and its implications: modelcontextstandard.io.

While MCS takes a different approach, it builds on the groundwork laid by the MCP team. Their effort in structuring documentation and project layout has been instrumental and deeply appreciated while putting the idea behind MCS online. Without MCP, the need for a standard like MCS might not have become so visible.

"LLMs are the new operating systems." — Andrej Karpathy
MCS supplies the drivers.

This isn't about building another tool, it's about establishing the foundational layer for AI-powered computing.