What is an MCP Server?

An MCP server is a lightweight program that exposes data, tools, or services to AI assistants through a standardized interface called the Model Context Protocol (MCP). It acts as a bridge between an AI model (like Claude or GPT) and an external system — a database, a codebase, a set of documents, or any other data source. When an AI assistant needs information that lives outside its training data, it connects to an MCP server and retrieves it in real time.

Think of an MCP server the same way you think of a web server: a web server responds to HTTP requests from browsers, and an MCP server responds to tool calls from AI assistants. The difference is the protocol they speak and who they serve.

What Does MCP Stand For?

MCP stands for Model Context Protocol. Anthropic introduced it as an open standard in late 2024 to solve a specific problem: every AI tool was inventing its own way to connect to external data. Cursor had its own plugin system, Claude Desktop had its own integration format, and every new AI agent framework rolled its own tool-calling mechanism.

MCP provides a single, open protocol that any AI client and any data provider can implement. One server works across all compatible clients. One configuration format. One way to discover available tools and call them.

The protocol specification is open source and maintained at modelcontextprotocol.io. Anyone can implement a client or server.

How MCP Servers Work

The MCP architecture follows a client-server model with three main participants:

• Host — The application the user interacts with (Cursor, Claude Desktop, a custom agent)
• Client — A component inside the host that manages the connection to one MCP server
• Server — The program that provides data or functionality to the client

What a Server Exposes

An MCP server can expose three types of capabilities:

Tools — Functions the AI can call. A search tool, a file reader, a database query. The AI decides when to invoke them based on the user's request.

Resources — Static or semi-static data the AI can read. Configuration files, documentation pages, reference material. Think of these as context the AI can pull in.

Prompts — Reusable prompt templates that guide the AI's behavior for specific tasks. Less common than tools and resources, but useful for standardized workflows.

Transport

MCP servers communicate over one of two transport mechanisms:

1. stdio — The server runs as a local subprocess. The client starts it, sends messages over stdin, and reads responses from stdout. Used for local tools like filesystem access or Git operations.
2. SSE (Server-Sent Events) — The server runs as a remote HTTP service. The client connects over the network using standard HTTP. Used for hosted services and shared infrastructure.

A typical interaction looks like this:

1. The client connects to the server and requests a list of available tools
2. The server responds with tool definitions (name, description, input schema)
3. The user asks the AI a question
4. The AI determines it needs external data and calls a tool
5. The client forwards the tool call to the server
6. The server executes the request and returns results
7. The AI uses the results to formulate its response

MCP Server vs API: What's the Difference?

If you already have a REST API, you might wonder why MCP exists at all. The key difference is who the consumer is and how discovery works.

A REST API requires a developer to write code that calls specific endpoints with specific parameters. An MCP server describes its capabilities in a format the AI can parse, so the AI itself decides which tools to call based on the user's natural language request.

This does not mean MCP replaces APIs. Many MCP servers are wrappers around existing APIs. The protocol just standardizes how AI assistants discover and interact with those capabilities.

Why Would You Use an MCP Server?

MCP servers are useful whenever you want an AI assistant to access data or perform actions beyond its built-in capabilities. Common use cases include:

Code and Documentation Search

Point an MCP server at your codebase or docs, and your AI assistant can search them semantically. Instead of copying code snippets into chat, the assistant pulls relevant context on its own. This is particularly powerful with vector search — the AI can find related code by meaning, not just by filename or keyword.

Knowledge Bases

Combine internal wikis, runbooks, and reference docs into a single searchable MCP server. Your team gets AI-powered knowledge base search across scattered documentation without building a custom search system.

Database Access

An MCP server can expose read-only database queries as tools. The AI generates SQL (or calls predefined queries) and returns results directly in the conversation.

Third-Party Integrations

Slack messages, GitHub issues, Jira tickets, Linear tasks — MCP servers can connect AI assistants to the tools your team already uses, letting the AI read and sometimes write data across services.

Automated Workflows

MCP servers can expose action-oriented tools: deploy a service, create a PR, send a notification. The AI becomes an interface to your infrastructure.

Who Creates MCP Servers?

Anthropic created the protocol specification and maintains the reference implementations. But MCP is an open standard — anyone can build a server.

In practice, MCP servers come from three sources:

1. Official and community open-source servers — The MCP project maintains a growing directory of pre-built servers for common tools: filesystem access, GitHub, Slack, PostgreSQL, Playwright, and dozens more.
2. Tool vendors — Companies building developer tools increasingly ship MCP servers alongside their products. If a service has an API, there is likely an MCP server for it (or one in progress).
3. Individuals and teams — Any developer can build an MCP server using the TypeScript or Python SDK. If you have a data source and want AI assistants to access it, you can write a server.
4. No-code platforms — Services like vecr.io let you generate MCP servers from raw data (GitHub repos, websites, documents) without writing code. You upload your content, vecr.io creates vector embeddings and hosts an SSE-based MCP endpoint. This approach works well when your goal is searchable context rather than custom tool logic.

Common MCP Servers

Here are some widely used MCP servers to give you a sense of what is available:

• Filesystem — Read, write, and search local files. Ships with most MCP client installations.
• GitHub — Search repos, read files, create issues and PRs.
• Slack — Read channels, search messages, post updates.
• PostgreSQL — Run read-only SQL queries against a database.
• Playwright — Control a browser for web scraping and testing.
• Memory — Persistent key-value storage for AI assistant context across sessions.
• Fetch — Make HTTP requests and extract content from web pages.
• vecr.io — Semantic search over indexed content from any data source (repos, websites, documents).

The ecosystem is growing fast. The official MCP server directory at github.com/modelcontextprotocol/servers lists current options.

How to Set Up an MCP Server

Setting up an MCP server depends on the client you are using. Here is the general process:

1. Pick a Server

Choose an existing MCP server or generate one. For a search-oriented server over your own content, you can create one on vecr.io in minutes.

2. Configure Your Client

MCP clients use a JSON configuration file to know which servers to connect to.

Cursor (.cursor/mcp.json in your project root or global settings):

{
  "mcpServers": {
    "my-docs": {
      "url": "https://mcp.vecr.io/sse/YOUR_INTEGRATION_ID",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      }
    }
  }
}

Claude Desktop (claude_desktop_config.json):

For remote SSE servers:

{
  "mcpServers": {
    "my-docs": {
      "url": "https://mcp.vecr.io/sse/YOUR_INTEGRATION_ID",
      "headers": {
        "Authorization": "Bearer YOUR_TOKEN"
      }
    }
  }
}

For local stdio servers:

{
  "mcpServers": {
    "filesystem": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-filesystem", "/path/to/directory"]
    }
  }
}

3. Verify the Connection

Restart your client. The MCP server should appear in the available tools list. Try a query that would trigger a tool call — if the AI uses the tool and returns results, the setup is working.

For a detailed walkthrough with screenshots, see our guide: How to Create an MCP Server from Any Data Source.

FAQ

Is an MCP server an actual server?

Yes, in the same sense that any program listening for and responding to requests is a server. A local (stdio) MCP server runs as a subprocess on your machine — there is no network involved. A remote (SSE) MCP server runs over HTTP, similar to a web API. Both receive requests from MCP clients and return structured responses.

What is the difference between MCP and function calling?

Function calling (or tool use) is a capability built into LLMs — the model can output structured requests to call predefined functions. MCP is a protocol that standardizes how those functions are defined, discovered, and executed. An AI model uses function calling to invoke a tool; MCP defines how that tool is registered, described, and reached across client-server boundaries. They are complementary, not competing.

Do I need to write code to create an MCP server?

Not necessarily. If you want a custom server with specific tool logic, you will use the MCP TypeScript or Python SDK. But if your use case is search over existing content, platforms like vecr.io generate a fully functional MCP server from your data — no code required. Upload your GitHub repo, website, or documents, and you get an SSE endpoint that works with any MCP client.

Which AI tools support MCP?

As of early 2026, MCP is supported by Claude Desktop, Cursor, Windsurf, Cline, Zed, Sourcegraph Cody, and a growing number of AI agents and frameworks. Any tool that implements the MCP client specification can connect to any MCP server.

Is MCP only for Anthropic models?

No. MCP is model-agnostic. While Anthropic created the protocol, it works with any AI model that supports tool use. Cursor uses MCP with multiple model providers. The protocol does not depend on any specific LLM.

How is MCP different from OpenAI's plugins or GPT Actions?

OpenAI plugins and GPT Actions are proprietary to OpenAI's ecosystem. MCP is an open protocol that works across clients and model providers. An MCP server you build today works with Claude Desktop, Cursor, and any future client that implements the spec — you are not locked into one vendor.

Get Started

If you want to give your AI assistant access to your codebase, documentation, or knowledge base, vecr.io generates MCP servers from any data source with no code required. The free tier includes 500 API calls per month and up to 10 pages of indexed content — enough to test the full workflow with your own data.

Create your MCP server on vecr.io