What you need to know about the Model Context Protocol (MCP)

Anthropic's recently-released Model Context Protocol (MCP) reaffirms that large language models (LLMs) need customer data to provide reliable, personalized, and useful outputs.

The protocol will likely play an invaluable role in helping AI models and 3rd-party applications integrate with one another. There are just a few things to consider when building and maintaining connections with this protocol.

You can read on to learn how MCP works, the benefits it provides, and how unified API solutions can complement it.

What is the Model Context Protocol? 

MCP is an open standard protocol released by Anthropic. It allows AI models to connect directly with external data sources so that the models can read data from and write data to the connected applications.

Benefits of using MCP

The Model Context Protocol offers several benefits that'll help support widespread adoption:

• Simplifies the build process: By providing a single, standard protocol, LLM providers and SaaS applications have a clearer and easier path to integrating with one another

• Supports workflow definitions: It provides a structured way for LLMs to retain, update, and get context, which allows the LLMs to manage and progress workflows  autonomously

• Enhances LLM efficiency: By standardizing context management, MCP minimizes unnecessary processing for LLMs

• Strengthens security and compliance: It offers standardized governance over how context is stored, shared, and updated across different environments

What MCP doesn’t address at the moment

Here are a few areas it doesn’t cover:

• Managing rate limits optimally: MCP doesn’t optimize data syncing within an integration provider’s rate limits, requiring you to implement throttling strategies

• Authenticating to an endpoint: MCP doesn’t handle authentication or specify how it should be implemented, leaving that decision to the integration provider

• Handling errors: MCP doesn’t enforce a standardized error-handling framework or response status codes—these are defined by each API provider. That said, MPC allows you to use error messages in JSON RPC 2.0, which includes code, message, and data fields. Learn more here 

• Supporting webhooks: The protocol doesn’t include webhooks or event-driven architecture for instant data updates (although they do support real-time syncs via server-sent events)

How unified APIs relate to MCP

Unified API solutions, which let you add hundreds of integrations to your product through a single, aggregated API, complement MCP for any integration, whether that’s managing authentication, data normalization, security, or sync speeds.

Data normalization

A unified API solution normalizes all of the integrated customer data, or converts that data to a predefined data model. This ultimately allows an LLM to handle prompts with more precision.

Security

Unified API solutions can secure your integrations by giving you full control of the customer data you can access and who on your team can access it.

For example, a unified API solution can offer scopes—or the ability for either you or your customers to toggle off the specific fields that customers don’t want you to access and sync.

Observability

Unified API solutions can offer a full suite of integration observability features to help your customer-facing team manage any of your MCP-based integrations. This includes everything from automated issue detection to fully-searchable logs.

Performance

Finally, unified API solutions can support integrations with fast sync speeds. And many support webhooks to sync data in real-time—allowing an LLM to use the latest data for each customer.