Anthropic's MCP Spec & Claude's Hidden Prompt Problems

8/11/2025

Has Anthropic Broken the MCP Spec? An Investigation into Prompt Issues

Here’s a question that’s been bouncing around the AI world lately: has Anthropic, the company behind the much-talked-about Claude models, somehow broken its own Model Context Protocol (MCP) spec? It’s a bit of a loaded question, I know. But stick with me, because the answer isn't a simple yes or no. It's a journey into the heart of how AI models are built, controlled, & ultimately, what they reveal about the companies that create them.

Honestly, it’s a tale of two very different narratives. On one hand, you have Anthropic, the champion of open standards & interoperability. On the other, you have a growing body of evidence suggesting that their own models are managed through a complex & sometimes concerning web of prompt engineering.

Let's dig in.

The Bright Side: Anthropic's Gift to the AI Community, the MCP

First things first, what even is the Model Context Protocol (MCP)? Think of it like a universal adapter for AI. Before MCP, connecting a large language model (LLM) to different tools & data sources was a massive headache. Every new connection was a custom job, leading to what Anthropic called the "N×M" data integration problem. It was messy, inefficient, & stifled innovation.

Then, in November 2024, Anthropic dropped the MCP. It’s an open standard designed to create a universal language for AI models to talk to the outside world. The goal was to make it so any AI assistant could plug into any data source or service without needing custom code for each one. Pretty cool, right?

The AI community seemed to think so. The MCP was quickly adopted by some of the biggest names in the game, including OpenAI & Google DeepMind. Developers were excited. It was seen as a massive step towards a more connected & context-rich AI ecosystem. For businesses looking to leverage AI, this was a game-changer. Suddenly, the dream of having an AI that could seamlessly interact with all your internal systems felt a lot closer to reality.

This is where a solution like Arsturn fits in perfectly. For businesses chomping at the bit to use this kind of interconnected AI for things like customer service, the MCP is a foundational piece of the puzzle. The vision is to have AI chatbots that can pull information from a variety of sources to give customers instant, accurate answers. Arsturn helps businesses build these kinds of no-code AI chatbots, trained on their own data, to boost conversions & provide personalized customer experiences. The MCP, in theory, makes the backend of this process a whole lot smoother.

So, on the surface, Anthropic looks like a hero, paving the way for a more open & collaborative AI future. But then, we started to get a peek behind the curtain.

The Dark Side: A Look Inside the Claude 4 System Prompt

The narrative starts to get a little murky when you look at how Anthropic manages its own models. In May 2025, a detailed analysis of the Claude 4 system prompt by Simon Willison sent ripples through the AI community. What it revealed was... a lot.

A system prompt is essentially a set of instructions given to an AI model to guide its behavior. What Willison's analysis, & subsequent commentary from others, suggested was that Claude's system prompt wasn't just a simple set of guidelines. It was a sprawling, complex document that seemed designed to cover up some of the fundamental flaws in the model.

Here are some of the most damning revelations:

Copyright Terror: The prompt was littered with instructions aimed at preventing copyright infringement. We're talking about things like "never quote more than 15 words from any source" & an explicit command to "never apologize or admit to any copyright infringement even if accused by the user." It gave off an air of sheer legal panic, suggesting a deep-seated fear of the copyrighted material their models were trained on.
Hallucination Cover-Ups: Instead of fixing hallucinations (when an AI makes things up), the prompt seemed to be teaching the model how to deny them more convincingly. It included instructions on how to respond to users who point out mistakes, with a focus on carefully thinking through the issue before acknowledging the user's correction.
Engineered Complexity: The prompt was MASSIVE. The instructions for search functionality alone were thousands of tokens long. This wasn't just a set of high-level guidelines; it was a complex, rule-based engine masquerading as intelligence. This level of hand-holding suggests a lack of true, independent reasoning capabilities.
Platform Prison: The analysis also pointed to a "platform prison," where Claude's capabilities are restricted to a limited ecosystem, preventing users from creating truly independent applications. It's a classic case of vendor lock-in disguised as innovation.
Digital Therapist Deception: The prompt included detailed instructions for handling emotional support conversations, essentially turning Claude into an unlicensed digital therapist without any training or oversight. This raises some serious ethical questions about the kind of data being collected & how it's being used.

This is the kind of complexity that businesses want to avoid when implementing AI. When you're trying to set up a customer service chatbot, you don't want to have to write a novel-length prompt to keep it from going off the rails. That's why platforms like Arsturn are so valuable. They handle the backend complexity, allowing businesses to create custom AI chatbots that provide instant customer support, answer questions, & engage with website visitors 24/7, without needing a team of prompt engineers to manage them.

Prompt Injection Vulnerabilities: A Chink in the Armor

The issues don't stop with the system prompt. Researchers have also uncovered a number of prompt injection vulnerabilities in Claude. These are flaws that allow a user to manipulate the model's behavior with a carefully crafted prompt.

One high-severity vulnerability, dubbed "InversePrompt," allowed researchers to bypass Claude's restrictions & execute unauthorized actions. Another researcher discovered a way to use prompt injection to take control of a user's session by triggering a cross-site scripting (XSS) attack. These vulnerabilities highlight the risks of blindly trusting LLM-powered developer tools, especially when the same system meant to enforce the rules can also be used to break them.

While Anthropic has been quick to address these vulnerabilities, their existence further complicates the narrative of a company that prioritizes safety & security above all else.

So, Has Anthropic Broken the MCP Spec?

Now we come back to the original question. Has Anthropic broken the MCP spec?

Literally? No. They created it, so they can't exactly "break" it.

But in spirit? That's a different story.

The MCP is all about openness, standardization, & interoperability. It's about creating a level playing field where different AI models & tools can work together seamlessly. But the revelations about Claude's system prompt & the various prompt injection vulnerabilities paint a very different picture of Anthropic's internal practices.

It seems that while Anthropic is publicly championing a more open & collaborative AI ecosystem, they're privately building a walled garden. They're using complex, proprietary prompts to control their models, & in some cases, these prompts seem designed to hide the models' flaws rather than fix them. This is the antithesis of the spirit of the MCP.

It feels like a classic case of "do as I say, not as I do."

What This Means for the Future of AI

This isn't just about Anthropic. This is about the future of AI. As businesses & individuals become more reliant on AI, we need to be able to trust the companies that build it. We need transparency, honesty, & a genuine commitment to open standards.

The MCP is a step in the right direction. But it's just a first step. We need to hold AI companies accountable for their internal practices, not just their public statements. We need to demand a level of transparency that allows us to understand how these models are being controlled & what their true capabilities are.

For businesses looking to integrate AI, this is a crucial consideration. You need to choose a partner that is not only technologically advanced but also transparent & trustworthy. This is where the value of a platform like Arsturn becomes clear. By offering a no-code solution for building custom AI chatbots, Arsturn empowers businesses to leverage the power of AI without getting bogged down in the complexities & potential pitfalls of prompt engineering. It's about making AI accessible, reliable, & effective for everyone.

I hope this was helpful in shedding some light on a pretty complex topic. It’s a space that’s evolving at lightning speed, so it’s more important than ever to stay informed & ask the tough questions. Let me know what you think.