Model Context Protocol (MCP): Hype or Reality?

Model Context Protocol (MCP): Hype or Reality?

When Anthropic released its Model Context Protocol (MCP) last fall, it was received as the “USB-C for AI” — a universal plug that allows large language models (LLMs) to connect to tools, APIs and data sources. Instead of developing reams of tedious “glue code” to work out any integration, devs could present services through MCP servers and any AI supporting MCP could discover and use them on the fly.

The promise is huge. But like any new protocol, the reality is more complicated. MCP has some clear advantages but also significant technical, security, and strategic challenges that organizations will need to assess before diving in.

Technical Challenges

MCP introduces yet another layer of abstraction — AI applications talk to an MCP server, which connects with tools and APIs. This does standardize integrations, but also adds these features:

Latency and overhead from the additional layer

• Complex architecture — all of your tools are now a microservice that you need to deploy and maintain

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• Ecosystem immaturity — for now, MCP is being served by the efforts of one company, Anthropic’s Claude (with other AI vendors merely dabbling at this stage)

Now, if you are doing very small simple projects, this might be a rather big hammer to hit your nail with compared to calling the API directly.

Security & Privacy Risks

The dangers increase in line with the capacities when it comes to MCP.

• - Unsecured servers: Thousands of MCP servers have been found out in the open, without proper authentication.

• - “Keys to the kingdom” effect: If one MCP server is compromised, it could dump all of the API tokens that it manages (email, Drive, databases)

• - Quick injection attacks: You can trick AI into abusing tools through hidden instructions in files or emails.

• - Over-permissioning: Numerous connectors request wide, full access to services. When not handled properly, such tools can have catastrophic effects (e.g., an actual incident, where one MCP-tool-enabled AI deleted a production database).

• - Wimpy permission UX: Some of our clients only ask for access once, and then never ask again (an invitation to abuse).

In brief: MCP is a centralizing of power, efficient but risky when not well regulated.

Adoption & Integration Barriers

Adoption is not trivial, even if the tech gets you excited:

• - Learning curve: there are new concepts that developers need to learn (hosts, servers, JSON-RPC).

• - Tooling gaps: the debugging, monitoring are still a bit rough.

• - Partial support: MCP is Anthropic-first; OpenAI, Microsoft, and others didn’t fully standardize.

• - Operational overhead: enterprises need to deploy/manage MCP servers, or trust third-party connectors.

• - Training expense: Developers and business users both need to learn how permissions, connectors, risk factors work.

Issues with Anthropic’s Implementation :

An early adopter, Anthropic has had some growing pains:

• Early MCP did not even include password protection; servers were open to all-comers.

• New OAuth based auth model is powerful but hard for enterprises to use.

• Many of the reference connectors came with unsafe capabilities, such as shell commands.

• Claude Desktop’s permissions model occasionally resulted in consent that was too broad.

Anthropic has been responsive but MCP is still super-bleeding-edge.

MCP vs. Glue Code

• MCP provides: Traditional pooling Compared to traditional pools

• Standardisation (same connector works across multiple AIs).

• Dynamic discovery (AI can peer available tools at run time).

• Future scalability (fewer custom codes to manage).

But glue code still wins on as of now:

• Simplicity (access the API directly, no intermediate layer).

• Performance (no JSON-RPC hops).

• Predictability (the AI cannot be bamboozled into bizarre tool use).

Long-Term Concerns

For MCP to truly be the “USB-C of AI,”:

• The spec has to mature (for now, it’s still moving quickly and causing maintenance churn)

• Progress beyond enough in the Standardization direction, else there's a danger of fragmentation

• We need to keep connector ecosystems alive in perpetuity

• Enterprises will require strong SLAs and security commitments before broad deployment

Conclusion 

MCP represents a leap toward a day when AI assistants can engage with the digital universe as easily as we do. It offers scale, economies of systemization and flexibility.

But it also carries risks that can’t be brushed aside — security holes, architectural complexity and the instability of early-stage standards.

By : Suvayu K Chakraborty
Transformation Leader
M.Tech - IIT Kanpur
MBA - IIM Indore
Phd - AI Transformation

References :

1. Anthropic. (2024, November 25). Introducing the Model Context Protocol. Anthropic. https://www.anthropic.com/news/model-context-protocol

2. Descope. (2025, September 5). What is the Model Context Protocol (MCP) and how it works. Descope. https://www.descope.com/learn/post/mcp

3. Iyer, K. (2025, May 15). The Model Context Protocol: Getting beneath the hype. Thoughtworks. https://www.thoughtworks.com/en-us/insights/blog/generative-ai/model-context-protocol-beneath-hype

4. Kekulawala, C. (2025, May 28). Model Context Protocol (MCP) and its limitations. Medium. https://medium.com/@ckekula/model-context-protocol-mcp-and-its-limitations-4d3c2561b206

5. Omotayo, D. (2025, May 2). Understanding Anthropic’s Model Context Protocol (MCP). LogRocket Blog. https://blog.logrocket.com/understanding-anthropic-model-context-protocol-mcp/

6. Pillar Security. (2025, March 24). The security risks of Model Context Protocol (MCP). Pillar Security. https://www.pillar.security/blog/the-security-risks-of-model-context-protocol-mcp

7. Schulz, K., Martin, J., Kan, M., Yeung, K., McCauley, C., & Ring, L. (2025, April 10). MCP: Model context pitfalls in an agentic world. HiddenLayer. https://hiddenlayer.com/innovation-hub/mcp-model-context-pitfalls-in-an-agentic-world/

8. Treblle. (2025, March 19). What is the Model Context Protocol (MCP)? A complete guide. Treblle. https://treblle.com/blog/model-context-protocol-guide

9. WillowTree. (2024, December 18). Is Anthropic’s Model Context Protocol right for you? WillowTree. https://www.willowtreeapps.com/craft/is-anthropic-model-context-protocol-right-for-you

10. Sharwood, S. (2025, July 21). Vibe coding service Replit deleted production database. The Register. https://www.theregister.com/2025/07/21/replit_saastr_vibe_coding_incident/

11. Reddit. (2024, November). Introducing the Model Context Protocol [Online forum post]. Reddit (r/LocalLLaMA). https://www.reddit.com/r/LocalLLaMA/comments/1gzqasu/introducing_the_model_context_protocol/