MCP 101: An Introduction to Model Context Protocol

Model Context Protocol (MCP) has emerged as a hot topic in AI circles. Scrolling through social media, we’ve been seeing MCP posts by explainers, debaters, and memers alike. A quick search on Google or YouTube reveals pages upon pages of new content covering MCP. Clearly, the people are excited. But about what exactly? Well, it’s quite simple: if models are only as good as the context provided to them, a mechanism that standardizes how this context augmentation occurs is a critical frontier of improving agentic capabilities.

For those who have not had the time to dive into this concept, fear not. The goal of this article is to give you an intuitive understanding around the ins and outs of MCP.

Prerequisites

While this explanation of Model Context Protocol (MCP) aims to be accessible, understanding its role in the evolving landscape of AI applications will be greatly enhanced by a foundational understanding of the capabilities of Large Language Models (LLMs) – particularly how they process information and utilize tools.

Introduced November 2024 by Anthropic as an open-source protocol, MCP allows for the integration between LLM applications and external data sources and tools.

Some exciting applications built with MCP have emerged. For example, Blender-MCP allows Claude to directly interact with and control Blender, resulting in prompt assisted 3D modeling, scene creation, and manipulation.

Protocols are a set of rules for formatting and processing data. As its name suggests, MCP (Model Context Protocol) is indeed a protocol – specifically a set of rules that standardizes how LLMs connect with external sources of information.

Before MCP, there was LSP

The Language Server Protocol (LSP) emerged as a standard for how integrated development environments (IDEs) communicate with language-specific tools. Essentially, LSP allows any IDE that supports it to seamlessly integrate with various programming languages.

With LSP, a single Go language server, for instance, can be built following the LSP standard, and any LSP-compatible IDE (ex: VS Code, JetBrains products, or Neovim) can automatically leverage it to offer features like autocompletion, error detection, and code navigation for Go.

Inspired by LSP, MCP overcomes the MxN integration problem we see with language model integrations, where each new language model (M) requires custom connectors and prompts to interface with each enterprise tool (N). By adopting MCP, both models and tools conform to a common interface, reducing the integration complexity from M×N to M+N.

Standardization

The beauty of standardization is that we don’t need to maintain a different connector for each data source. For AI applications that intend to preserve contextual information while navigating across various sources of information in their tool and data stack, standardization allows us to transition towards building systems that are more robust and scalable.

There are three key components to MCP:

MCP Host: User-facing AI interface (Claude app, IDE plugin, etc.) that connects to multiple MCP servers.

MCP Client: Intermediary that manages secure connections between host and servers, with one client per server for isolation. The Client is in the host application.

MCP Server: External program providing specific capabilities (tools, data access, domain prompts) that connects to various data sources like Google Drive, Slack, GitHub, databases, and web browsers.

MCP wins over other protocols because baked into its build is the intuition Anthropic has developed around building agents as articulated in their “Building Effective Agents” blog post.

Significant growth has been observed on the server side, with over a thousand community-built, open-source servers as well as official integrations from companies. There’s also been substantial open-source adoption, with contributors enhancing the core protocol and infrastructure.

Roots

A Root defines a specific location within the host’s file system or environment that the server is authorized to interact with. Roots define the boundaries where servers can operate and allow clients to inform servers about relevant resources and their locations.

Sampling

Sampling is an underutilized yet powerful feature offered by MCP that reverses the traditional client-server relationship for LLM interactions. Instead of clients making requests to servers, sampling allows MCP servers to request LLM completions from the client. This gives clients full control over model selection, hosting, privacy, and cost management. Servers can request specific inference parameters like model preferences, system prompts, temperature settings, and token limits, while clients maintain the authority to decline potentially malicious requests or limit resource usage. This approach is valuable in scenarios where clients interact with unfamiliar servers that still require access to intelligent capabilities.

Q: How does MCP improve AI agent reliability and prevent prompt injection attacks?

MCP enhances AI system security and reliability through structured data validation and standardized interfaces:

• Input validation using schema enforcement prevents malformed or malicious prompts from reaching the model.
• Structured context makes it harder to embed hidden instructions or manipulate model behavior through carefully crafted inputs.
• Type safety ensures all data conforms to expected formats, reducing parsing errors and unexpected behavior.
• Audit trails provide clear visibility into what context is being passed to models.
• Sandboxing isolates different context sources and validates them independently.
• Role-based access controls what context different components can provide. Implementation: Use Pydantic schemas for validation, implement context sanitization, and maintain clear separation between user input and system context. MCP doesn’t eliminate all security risks but significantly reduces attack surface through principled context management.

Q: What are the key components of implementing MCP in production AI systems?

Production MCP implementation requires several core components:

• Schema definition using tools like Pydantic or JSON Schema to define context structure and validation rules.
• Context aggregation layer that collects, validates, and formats context from multiple sources.
• Protocol enforcement ensuring all components adhere to MCP standards and handle errors gracefully.
• Version management for schema evolution and backward compatibility.
• Monitoring and logging to track context usage, validation failures, and system performance.
• Error handling with graceful degradation when context validation fails.
• Integration layer connecting MCP to existing AI frameworks and applications.
• Documentation for context schemas and integration patterns.
• Testing framework for validating context processing and schema compliance.
• Implement gradually, starting with critical context sources and expanding coverage over time.

Q: MCP vs traditional prompt engineering: advantages and disadvantages?

MCP and traditional prompt engineering serve different purposes with distinct trade-offs:

• MCP advantages: Structured, validated context reduces errors, enables version control of context schemas, provides better debugging and monitoring, supports complex multi-agent systems, and scales better across teams.
• Traditional prompting advantages: Simpler to implement initially, more flexible for rapid prototyping, requires less infrastructure setup, and works with any LLM without special support.
• MCP disadvantages: Higher implementation complexity, requires schema design and maintenance, additional infrastructure overhead, and potential performance impact from validation.
• Prompting disadvantages: Error-prone with complex context, difficult to debug and maintain, prone to prompt injection, and inconsistent across different models.
• Best approach: Use MCP for production systems requiring reliability and traditional prompting for rapid prototyping and simple applications.

Q: How to migrate existing AI applications to use Model Context Protocol?

Migrating to MCP requires systematic planning and gradual implementation:

• Assessment phase: Identify current context sources, map data flows, and catalog existing prompt structures.
• Schema design: Create Pydantic or JSON schemas for existing context types, starting with most critical data sources.
• Pilot implementation: Begin with one context source, implement validation and formatting, test thoroughly.
• Gradual rollout: Add context sources incrementally, maintaining backward compatibility during transition.
• Integration updates: Modify AI application code to use structured context instead of raw prompts.
• Validation and testing: Ensure MCP implementation maintains or improves application performance and accuracy.
• Monitoring setup: Implement logging and metrics for context validation and system performance.
• Team training: Educate developers on MCP principles and implementation patterns.
• Documentation: Update system documentation and create migration guides for similar applications.

Q: What tools and libraries support Model Context Protocol implementation?

MCP ecosystem includes various tools and libraries:

• Schema validation: Pydantic for Python-based validation, JSON Schema for language-agnostic validation, Zod for TypeScript validation.
• Framework integration: LangChain extensions for MCP support, custom wrappers for existing AI frameworks.
• Development tools: Schema generators for existing data structures, validation testing frameworks, and debugging utilities.
• Monitoring: Integration with observability platforms like Weights & Biases, custom metrics dashboards.
• Implementation libraries: Open-source MCP implementations in Python, TypeScript, and other languages.
• Cloud services: Some AI platforms beginning to offer MCP-compatible APIs and services.
• Best practices: Start with well-established validation libraries, contribute to open-source MCP implementations, and build community standards for common context types. The MCP ecosystem is rapidly evolving with new tools and integrations appearing regularly.

By establishing a common protocol for connecting language models with external tools and data sources, MCP eliminates the need for custom connectors and creates a more robust ecosystem.

The community approach creates what one might call “compounding innovation” or “3D chess”. Each contributor builds atop others’ work. The network effects are substantial and the pie grows larger for everyone.

Anthropic’s bet was that empowering developers with an open MCP would allow it to grow faster, evolve more robustly, and ultimately deliver more value than any closed system they could build alone. Time will tell if they were right, but history suggests they were.

Why MCP Won (Latent Space)

The Model Context Protocol (MCP) by Anthropic: Origins, functionality, and impact

Model Context Protocol (MCP), clearly explained (why it matters)

Building Agents with Model Context Protocol - Full Workshop with Mahesh Murag of Anthropic