MCP Server Requirements: What You Actually Need to Run One

An MCP server needs a runtime environment, a way to accept requests from a client, and access to the data or system it exposes. It does not need to run an AI model, and it does not need powerful hardware — most servers are lightweight processes that forward requests to an existing service. Tools like fenbs let your team record the verified setup and ownership decisions so everyone knows what the server depends on and who maintains it.

The confusion about requirements usually comes from conflating two things: the MCP server and the AI model. The model lives in the client — Claude, Cursor, ChatGPT, or another app. The server lives next to the data source it connects to. Understanding that separation makes the real requirements much more manageable.

The minimum software requirements

An MCP server is a process that speaks the Model Context Protocol. You need a runtime that can run that process — a JVM for Java, Node.js for JavaScript, a Python interpreter for Python, a .NET runtime for C#, or a compiled binary for Go or Rust. The official SDKs are available for all major languages. Beyond the runtime, you need the MCP SDK package and the client libraries for whatever system the server connects to.

• A supported language runtime: Node.js, Python, Java, .NET, Go, or Rust.
• The official MCP SDK for your chosen language, installed via the language's package manager.
• Any client libraries needed to call the service the server exposes (a database driver, an API client, etc.).
• A way to start and stop the process: a shell script, a service manager, or a container.

Hardware requirements are minimal

An MCP server itself uses very little CPU or memory. It receives a request, calls a service, formats the response, and returns it. The resource cost is almost entirely determined by what the server calls downstream — a database query, an API request, or a file read. A server running on the same machine as a developer's IDE has no meaningful hardware requirements beyond what that machine already provides.

For team deployments where the server runs on shared infrastructure, the same applies: size the host for the downstream service, not for the MCP server itself. A small container or a lightweight VM is usually more than enough unless the server handles very high request volume.

Transport requirements

MCP uses two transport mechanisms. The stdio transport requires no network access: the client spawns the server as a child process and communicates over standard input and output. This is the simplest setup for local use. The HTTP-based server-sent events (SSE) transport requires the server to listen on a network port and the client to be able to reach it. This needs a public or private network address, HTTPS for production, and an authentication mechanism so only authorized clients can connect.

Choose the transport based on who needs to reach the server. A developer tool used on one machine is a good fit for stdio. A server accessed by multiple clients, remote teammates, or a cloud-hosted AI app needs the SSE transport. The MCP spec covers both transports in detail.

Access and permissions

The server needs read or write access to whatever it exposes. A database server needs database credentials with appropriate scope — typically read-only unless write operations are explicitly required. A file server needs filesystem permissions on the directories it reads. Grant the minimum access the server actually needs; it is much easier to expand permissions later than to deal with the consequences of excessive access.

For remote deployments, also decide who may connect to the server itself. The SSE transport accepts connections from any client that can reach the endpoint unless you add authentication. Use bearer tokens, API keys, or OAuth to restrict access. Record the access decisions so your team knows the intended scope, especially when the server connects to production data.

What an MCP server does not need

• An AI model: the model runs in the client, not the server.
• GPU hardware: MCP servers are standard network or IPC processes with no ML inference.
• A database of its own: the server connects to existing services; it does not store data.
• A public internet address: stdio servers run entirely locally; SSE servers can run on a private network.

How to record your team's MCP setup with fenbs

A shared setup document goes stale quickly. Keep the verified requirements and ownership decisions in fenbs where your AI assistant can read them before each session.

1. Create a free account and start a project for the MCP connection. Describe the service it connects to and who owns it.
2. Write the first Decisions and rules: what transport is in use, what access scope was granted, and who approved it. Name the person responsible for each decision.
3. Add a task for each piece of setup that still needs verification or documentation.
4. Connect your development assistant via Integrations so it reads the current setup before each session.
5. Update the project notes when requirements change — a credential rotation, a new transport, or an expanded permission scope.