Who Created MCP Protocol? Origins and Impact

What the Model Context Protocol is

Anthropic introduced the Model Context Protocol, or MCP, on November 25, 2024. Engineers David Soria Parra and Justin Spahr-Summers created the protocol. It gives AI apps one standard way to connect with data and tools.

That direct answer covers who created MCP protocol and who created the MCP protocol. MCP is an open-source framework for AI data access. It helps assistants reach files, databases, code, and business tools through shared rules.

Before MCP, many AI apps used custom links for each data source. Each link needed its own code, tests, and upkeep. MCP aims to replace that patchwork with a common connection layer.

The timing mattered. AI systems grew more useful when they could act on fresh data. Yet key data remained trapped in information silos and legacy systems.

Who created MCP, and who creates an MCP server?

David Soria Parra and Justin Spahr-Summers created the first MCP design at Anthropic. Their work shaped the protocol and its early software tools. Anthropic then released MCP as an open project for wider use.

An MCP server is different from the MCP protocol itself. A server is a small program that exposes a source through MCP rules. A team, company, or individual developer can create one.

For example, a team might build an MCP server for a help desk database. Another team might build one for a code repository. This distinction answers the related question, who created the MCP server? There is no single creator for every MCP server.

The original release came from Anthropic. The wider community now builds servers for many tools and data stores. The Anthropic MCP announcement explains the project’s first release and goal.

How MCP works under the hood

MCP uses a client and server model. An AI application acts as the client. An MCP server connects that client to a chosen data source or tool.

The client sends a request through a shared protocol. The server checks the request and returns useful data. The same link can also let the AI app call an approved tool.

This creates two-way communication between AI applications and data servers. The server can offer resources, prompts, and tools. The AI app can discover those options before it uses them.

MCP supports common languages such as Python, TypeScript, and Java. Developers can pick a language that fits their existing stack. They can also reuse shared patterns across different MCP servers.

Part	Purpose
AI application	Requests context or asks a tool to act
MCP client	Manages the link to an MCP server
MCP server	Connects the app with data or tools
Data source	Provides files, records, code, or other context

The protocol does not force every system into one database design. Instead, it sets a shared way to ask for and return context. That makes integration simpler across varied software.

Developers can review the MCP specification for the formal message and server rules.

What MCP servers can do

MCP servers can expose data in a controlled and useful form. A server may let an assistant query a structured database. It may also provide selected files from a team workspace.

Code work is another strong use case. An MCP server can access a code repository, search files, and return relevant sections. It can then help an AI assistant explain a bug or suggest a safe change.

Other servers can connect calendars, ticket systems, cloud storage, or research tools. Each server can set limits on what the AI may read or change. That control helps teams reduce risk.

• Query structured databases and return filtered records
• Search code repositories and inspect selected files
• Read approved documents from shared storage
• Call business tools with clear input rules
• Return current context instead of stale training data

A good server should expose only the data that an app needs. It should also log requests and protect private records. These steps matter when an AI assistant can trigger real actions.

Why MCP matters for AI integration

MCP targets a basic problem in the AI ecosystem: information silos. A company may hold customer data, code, and reports in separate systems. Each system often has a different access method.

Legacy systems make this problem harder. They may lack modern APIs or clear data formats. MCP can place a thin connection layer in front of them.

This does not fix poor data quality by itself. It does reduce the need for a new custom connector for every AI app. That can lower build time and make tool access more consistent.

MCP also gives developers a shared mental model. They can build one server and connect it to several supported AI clients. The result is a wider path from business data to useful AI action.

Adoption by major AI providers

MCP gained attention quickly after its November 2024 release. OpenAI and Google DeepMind are among the major AI providers linked with MCP adoption. Their support helped move MCP beyond an Anthropic-only project.

Adoption does not mean every provider uses the same setup. Each product may support different tools, security steps, or server lists. Teams should check each provider’s current docs before they build a production link.

The wider impact is clear. A shared protocol can help AI apps reach more useful context. It can also give software makers a common target for new integrations.

Still, adoption brings hard questions. Teams must manage access rights, server trust, data leaks, and tool misuse. Strong review remains vital when an AI app can read private data or change records.

The future of MCP

MCP could become a common bridge between AI applications and software tools. Its value will depend on broad support and careful server design. More language tools and hosted services may follow.

The protocol may also shape how teams think about AI features. Instead of building one closed assistant, a team can connect an assistant to many approved sources. That makes new use cases easier to test.

Yet MCP is not a magic fix for every data problem. Teams still need clean records, clear permissions, and strong system checks. The protocol supplies the link. Good engineering supplies trust.

In short, Anthropic engineers David Soria Parra and Justin Spahr-Summers created MCP. The project’s main contribution is a shared way to connect AI with outside context. Its next test will be safe, reliable use across the wider AI ecosystem.