Model Context Standard (MCS)

Unlock the Power of LLMs: A lightweight standard for connecting LLMs to external systems through reusable drivers, not wrappers or bloated protocols

Connecting LLMs to external systems is still harder than it should be. Most current solutions rely on custom wrappers or complex function-calling protocols that require heavy infrastructure and manual parsing. MCS offers a simpler alternative.

MCS treats integration as a driver problem. Just like operating systems use device drivers, LLMs can use interface drivers to connect to APIs, tools, databases or devices. Instead of writing custom glue code, you configure reusable drivers based on existing standards like OpenAPI and REST. These drivers translate between your LLM’s language output and actionable operations.

If you've used MCP before. MCS builds on the same core the function calling principle but avoids MCPs downsides, like a new stack, new security headaches and complex hosting. It’s faster to adopt, easier to maintain, and model-agnostic.

MCS is your plug-and-play standard for safe, scalable and elegant LLM integration.

Key Benefits: Why Choose MCS?

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 specs like OpenAPI.
Mix and match, extend or trim functions by URL, no redeploys, no prompt engineering headaches, and no code changes.

✅ Universal Driver Architecture

Write a driver once, and everyone can use it everywhere. Whether it's REST-HTTP, EDI-AS2, Filesystem-localfs or even CAN-Bus or MCP-STDIO. All LLM apps can plug into it. No more wrappers, no more reimplementation or endless prompt engineering. Just configure and connect.

✅ Utilizes Proven Protocols

MCS avoids custom stacks and leverages OpenAPI, REST, OAuth and similar proven standards. This ensures better compatibility, security, and maintainability. Easier auditing and integration into existing toolchains.

✅ No More Glue Code

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

✅ Optimized Prompts, Built-In

Drivers include refined prompts tested across models. No manual prompt tuning on the client 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.

✅ Autostart? Not Required

Unlike MCP's autostart dependency, 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. Say goodbye to malicious servers (up to 8% on Github) 1.

✅ Compatible with MCP, but Cleaner

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.

✅ Scales from Simple to Complex

From Web APIs to industrial IoT, MCS abstracts the complexity. Drivers handle the complexity, your app stays focused.

Quickstart: See It in Action in Under 2 Minutes

You don’t need a complex setup 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.

We provide 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.

Deploy the Demo (VPS, Docker, Cloud)

# 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

How It Works

The service exposes two endpoints:

Path	Purpose
/openapi-html	HTML-rendered OpenAPI spec (easy for LLMs web access to parse)
/tools/fibonacci?n=	returns 2 × Fibonacci(n) to detect hallucination

Try It Yourself

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.

Test Results with Web-Enabled LLMs

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

Model	Result	Notes
ChatGPT (Browser)	✅	requires two prompts
Claude 3 (web)	✅	two‑step flow, restricted so it could not be done in one call
Gemini	❌	refuses second request
Grok 4	⚠️ Partial	Seems to work, but result is not readable by Groks browser (Minimal HTML Output)
DeepSeek	❌	hallucination, server call never happened

What This Shows

Even without any special MCS driver 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. Just swap in your API specs, add MCS drivers, and you have full integration in your AI app.

Why MCS exists: The Problem with Current Solutions

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.

However, MCP introduces fundamental challenges that make it harder to adopt than necessary.

The Protocol Overhead Problem

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 et al.
• Additional complexity: Developers must learn new patterns instead of leveraging existing HTTP 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.

The Wrapper Server Multiplication

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

Your API → MCP Wrapper Server → MCP Client → 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

The Autostart Security Risk

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.

The Development Burden

For every MCP client developer:

• Master prompt engineering for each model
• 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 they want to make available to LLMs
• If they create an MCP server with valuable logic, they 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 is a pragmatic evolution

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

MCS 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 difference with MCS:

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 true driver for the protocol and transport, abstracting these details for the LLM and client developer.

Universal Protocol Support

• 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

Direct API Integration

Instead of:

Your API → MCP Wrapper → MCP Client → LLM

MCS enables:

Your API (with standard spec) → MCS Driver → LLM

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

Security by Design

• 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

CS drivers are static 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.

Developer Experience

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
• Modular architecture: Mix and match drivers as needed

Migration Path

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

The Bottom Line

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.

Aspect	MCP	MCS
Protocol	Custom JSON-RPC stack	Standards like HTTP/OpenAPI
Server Architecture	Wrapper server per API	Direct API connection
Autostart	Required via STDIO	Optional, containerized
Authentication	Custom implementation	Standard OAuth/JWT/API keys
Distribution	Complex server deployment	Simple module download (APT/Maven-like)
Prompt Engineering	App developer responsibility	Built into drivers
Reusability	API-specific servers	Universal protocol drivers
Security Model	New attack surfaces	Proven HTTP security
Tooling	Custom debugging/monitoring	Standard tools can be used
Integration Effort	High (wrapper + client code)	Low (configure driver)

What This Means

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.

Contributing: Building the Ecosystem Together

MCS thrives as an open standard, and we're building a collaborative ecosystem where your expertise makes a real difference.

Key Areas Where Support is Needed

• 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. Our 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 our 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.
• 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.

Getting Started

Visit our GitHub organization to explore the codebase and see current priorities. Check CONTRIBUTING.md for detailed guidelines on code standards, testing requirements, and submission processes.

Whether you're contributing code, documentation, or ideas, you're helping build the future of LLM integration. Every contribution, no matter how small, moves us closer to a world where connecting LLMs to external systems is as simple as plugging in a driver.