If you've recently started following AI Agents, you've definitely seen the three letters 'MCP' everywhere. Claude has MCP, Cursor has MCP, GitHub Copilot is integrating MCP, every Agent framework says 'supports MCP.' But many people's understanding of MCP stops at 'a protocol that lets AI connect to tools' — this description isn't wrong, but it's too vague. It doesn't explain what specific problem MCP is solving or why it matters for your use of AI Agents.
This article explains MCP from the ground up: what problem it appeared to solve, how it works, and the practical impact on your use of AI Agents.
MCP (Model Context Protocol) is an open standard proposed by Anthropic in November 2024, defining how AI models and external tools communicate. Simply put: it's a 'universal adapter' between AI and the external world.
Why 'universal adapter'? Think of electrical plugs: different countries have different sockets, so your European appliance needs a converter to work in the US. AI and tools had the same problem before MCP — each AI company had its own tool integration format; every developer wanting Claude to use some tool had to write their own 'converter code' (the Function Calling formatting logic).
MCP's arrival is equivalent to: the whole world standardizing on the same socket — as long as your tool server supports MCP, it can be used by any AI model that supports MCP, without each AI company needing to adapt separately.
Why did it suddenly become important in 2025? Because more and more mainstream tools (GitHub, Slack, Google Drive, Notion) started releasing official MCP Servers, while more and more AI products (Claude, Cursor, Windsurf, Zed) declared MCP support. The standard is reaching 'critical mass' — building tools with MCP means one-time development usable by all major AI models; without MCP, you need to maintain a separate integration for each AI platform. For developers and enterprises, MCP's gravitational pull keeps growing.
To understand what MCP solves, look at the world before MCP.
Before MCP, if you wanted an AI Agent to 'query Slack messages,' you needed to: first, read Slack's API documentation and write code calling the Slack API; second, wrap the Slack API into a format the LLM can understand (each LLM platform's format differs slightly — OpenAI's Function Calling format, Anthropic's Tool Use format); third, hardcode this tool's description, parameter format, and invocation logic into your Agent code.
If you wanted both Claude and GPT-4 to use this tool, you'd write two different adapter codebases. If you later wanted to share this tool with other developers, they'd still need to re-read your code to understand how to use it.
Even more cumbersome is the 'dynamic tool' problem: some tools' capabilities are dynamic (e.g., a DeFi protocol's tool where available functions change as the protocol version updates). Before MCP, every tool update meant manually updating the Agent's code too.
All these problems share a root cause: no unified standard, so every 'AI and tool conversation' was one-off, customized work.
MCP has three core components:
MCP Server (tool provider side): exposes some tool's (Slack, GitHub, database, DeFi protocol) capabilities as MCP-format interfaces. The MCP Server's job: when asked 'what can you do?' respond with a standardized tool list; when sent 'please execute this operation,' execute and return results. GitHub's official MCP Server knows how to read GitHub repositories, create PRs, query Issues; Slack's MCP Server knows how to send messages, read channels, search history.
MCP Client (the AI model side): Claude (or other MCP-supporting AI) plays the MCP Client role. Its job: ask the MCP Server 'what tools do you have?', add these tool descriptions to its own Context, choose to call the appropriate tool when needed, parse the tool's return results.
MCP Protocol (standardized communication format): defines the communication format between Client and Server — just as HTTP is the communication standard between browsers and servers. MCP Protocol defines: how to enumerate tools (`tools/list`), how to call tools (`tools/call`), how to subscribe to real-time data (`resources/subscribe`), and more.
An analogy: the MCP Server is like a restaurant's menu system; the MCP Client (Claude) is the customer; the MCP Protocol is the ordering format ('I'll have number 3, less spicy'). No matter which restaurant you go to, the ordering format is the same — that's the meaning of 'universal adapter.'
In a real Agent, a typical MCP workflow: user says 'help me summarize last week's discussion about Q3 product planning in Slack #general' → Claude identifies need for Slack tool → requests tool list from connected Slack MCP Server → calls `get_channel_messages(channel='#general', date_range='last_week')` → receives results → generates summary reply. Throughout this process, Claude doesn't need to know Slack API specifics — just the tool descriptions provided by the Slack MCP Server.
MCP solves many problems, but as a nascent standard, it also introduces new risks that users need to understand:
Tool description trust issue: MCP Server tool descriptions are defined by the tool provider themselves, and Claude trusts this description. If a malicious MCP Server describes its tool as 'helping you summarize documents' but actually executes 'read your private key and send it to a remote server,' Claude may not detect this discrepancy (Claude only sees the description, not the tool's actual code). This is 'Prompt Injection via MCP Tool Description' — attackers manipulate AI behavior by disguising tool descriptions.
What users should do: only connect MCP Servers you trust (officially released, from reputable open-source communities, or ones you wrote yourself). When connecting a new MCP Server to Claude, ask 'in the worst case, what can this Server's tools do?' — if the answer involves 'access my private GitHub repositories' or 'access my bank account,' ensure you understand that this Server's tool descriptions are legitimate and that actual tool behavior matches the descriptions.
Version inconsistency issues: The MCP protocol itself is still rapidly evolving, and different versions may have incompatibilities. An MCP Server written in an older format may have tools that fail to be recognized in a newer MCP Client. When using community or official MCP Servers, watch for version compatibility.
Context cost of tool count: each connected MCP Server's tool descriptions occupies Claude's Context Window. If you connect 10 MCP Servers each with 20 tools, just the tool descriptions may occupy several thousand tokens, reducing the Context space available for processing your actual needs. Recommendation: only connect MCP Servers needed for the current task; disconnect unneeded connections after completing the task.
As a user (using Claude.ai or other MCP-supported tools), MCP lets you connect Claude to real working tools, making Claude not just a Q&A tool, but an assistant that can genuinely access your GitHub, Slack, and Google Drive. In Claude.ai, you can find MCP connection options (also called 'Connectors') in settings and choose which tools to connect.
As a developer (building your own Agent), MCP means: you no longer need to write adapter code for every tool from scratch. If you want your Agent to use GitHub, just connect GitHub's official MCP Server; if you want to use your own internal database, write an MCP Server in MCP format — that Server can then be used by any MCP-supporting AI model. Learning how to write one MCP Server has better ROI than learning each AI platform's tool format separately.
For DeFi and Onchain Agent developers: MCP's potential lies in letting Agents dynamically discover and use onchain tool interfaces (protocol MCP Servers) rather than hardcoding each protocol's API calls. This direction is still developing, but MCP standardization gives Onchain Agent tool integration a more sustainable architectural foundation.