The basics of MCP (Model Context Protocol) 2025

What is the Model Context Protocol (MCP)?

The Model Context Protocol (MCP) is a new standard for connecting AI assistants to the systems where data lives, including content repositories, business tools, and development environments.

Anthropic announced the Model Context Protocol (MCP) on November 26, 2024. It seems difficult to understand the MCP from the announcement. But, it’s not difficult to understand the MCP. MCP is a protocol for connecting AI assistants to the systems where data lives without reinventing the wheel. If some developers have already implemented the connector for their systems, they can connect their systems to the AI assistants without implementing the connector again.

Example

Playwright MCP is a great example.

Frontend developers have concerns about the quality of the code generated by AI assistants. They want to make sure the code is correct and works in the browser. Playwright is a tool for testing web applications. Playwright MCP is a connector for Playwright that allows you to connect Playwright to the AI assistants.

You can use Playwright MCP following the steps below:

What MCP Standardizes

The official specification describes a host-client-server architecture. A host application creates a client connection for each server. Servers can expose three main primitives:

• Resources: contextual data that a user or model can read
• Prompts: reusable message and workflow templates
• Tools: operations that a model can request

MCP messages use JSON-RPC. The supported transports and authorization requirements depend on the negotiated protocol revision, so clients and servers should not assume that every MCP implementation supports the same revision or capabilities.

• MCP architecture
• Stable 2025-11-25 specification
• Protocol version negotiation

Practical Benefits

MCP can reduce one category of repeated integration work: defining a separate tool interface for every host-server pair. A server that follows a compatible MCP revision can advertise its capabilities to more than one compatible host.

That does not make an integration automatic. Teams still need to verify:

• Whether the host and server negotiate a compatible protocol revision
• Whether tool and resource schemas match the intended workflow
• How credentials and authorization are handled
• Which actions require user confirmation
• How failures, retries, timeouts, and cancellation behave
• What data is logged or sent to another service

The result depends on the host, the server, the selected tools, and the surrounding access controls. MCP compatibility by itself does not establish security, reliability, or business value.

Security and Trust Boundaries

An MCP server can expose data and operations with significant consequences. The official specification therefore treats tool descriptions and annotations as untrusted unless they come from a trusted server, and places user consent and control at the center of the host’s responsibilities.

For a production integration:

1. Allow only reviewed servers and pin their distribution or deployment source.
2. Grant the minimum credentials and filesystem or network access needed for the task.
3. Show the user what a tool will do before consequential execution.
4. Validate tool inputs and outputs independently of their natural-language descriptions.
5. Keep secrets out of prompts, logs, and client-visible error messages.
6. Record server identity, protocol revision, tool name, result, and approval state for audit.
7. Test prompt-injection, confused-deputy, token-forwarding, and excessive-permission scenarios.

For remote HTTP servers, follow the authorization requirements of the implemented protocol revision. For local stdio servers, credential and process isolation remain the responsibility of the host and operating environment.

• MCP security and trust guidance
• MCP authorization

Evaluation Checklist

Before adopting an MCP server, run a bounded evaluation:

• Confirm the exact server version and protocol revision
• Review the source, publisher, release artifacts, and update mechanism
• Enumerate every resource and tool the server exposes
• Run first with test data and least-privilege credentials
• Capture the actual wire-level requests and responses
• Verify cancellation, timeout, malformed input, and unavailable dependency behavior
• Separate a successful demonstration from production readiness

Conclusion

MCP provides a shared protocol for connecting AI hosts with contextual data and tools. Its value is interoperability, not a guarantee of quality or safety. A useful deployment combines protocol compatibility with explicit authorization, least privilege, implementation review, observable runtime behavior, and task-specific testing.

This article was reviewed and updated on September 1, 2026. The stable specification at that time was the 2025-11-25 revision; later release candidates may differ.