What Is MCP and Why Should Developers Care

The Problem MCP Solves

Before MCP, connecting an AI assistant to an external capability required writing custom integration code specific to both the AI client and the tool. If you wanted Claude to query your database, you built a function calling wrapper for Claude's API. If you wanted GPT to do the same thing, you built a different wrapper for OpenAI's function calling format. If you wanted the same capability in a coding assistant, you built yet another integration. The tool itself did not change, but the glue code was different for every combination of AI client and tool.

This created an N-times-M integration problem. For N AI clients and M tools, you needed N times M integrations. Each integration had to handle capability discovery (what can this tool do?), parameter validation (what does this tool expect?), invocation (how do I call it?), and result formatting (how do I present the output?). The overhead of building and maintaining these integrations slowed down adoption and created fragmentation in the ecosystem.

MCP collapses this to N plus M. Each AI client implements the MCP client protocol once. Each tool implements the MCP server protocol once. Any client can then connect to any server. The protocol handles discovery, validation, invocation, and result formatting in a standard way that both sides understand without custom code.

How MCP Works

MCP uses a client-server architecture with JSON-RPC 2.0 as the message format. The AI client (Claude Code, Cursor, or any MCP-compatible application) acts as the client. The tool or service acts as the server. Communication flows through one of two transport mechanisms: stdio (for local servers) or Streamable HTTP (for remote servers).

When a client connects to a server, it sends an initialization request. The server responds with its capabilities: the tools it offers, the resources it exposes, and the prompts it provides. The client presents these capabilities to the language model as available actions. During a conversation, when the model decides to use a tool, the client sends an invocation request to the server, the server executes the logic and returns the result, and the client incorporates the result into the conversation.

The key insight is that the language model makes the decision about when to use which tool based on the tool descriptions provided by the server. The descriptions are natural language, so the model can match them to the user's intent without explicit routing logic. A well-described tool gets called when the model recognizes that the user's request matches the tool's purpose, and ignored when it does not.

The Three Primitives

MCP organizes server capabilities into three types. Tools are functions the AI can call to perform actions: query a database, store a memory, create a file, or call an API. Resources are read-only data the AI can pull into its context: configuration files, documentation, system status, or schemas. Prompts are reusable templates that structure the AI's behavior for specific workflows: code review checklists, analysis frameworks, or multi-step procedures.

Most servers focus on tools because tools provide the most immediate value. A server with three well-described tools that solve specific problems is more useful than a server with dozens of generic capabilities. The descriptions are what make tools discoverable, so invest in writing clear, specific descriptions that tell the model exactly when and why to use each tool.

Who Created MCP and Who Uses It

Anthropic released MCP as an open specification in November 2024. The protocol design was informed by experience building Claude's tool use capabilities and by feedback from developers building AI-powered applications. Anthropic open-sourced the specification, the reference SDKs for Python and TypeScript, and several example servers.

Adoption has been rapid. Claude Code, Claude Desktop, Cursor, Windsurf, Cline, Continue, Zed, and dozens of other AI clients support MCP natively. The ecosystem includes thousands of community-built servers covering databases (PostgreSQL, SQLite, MongoDB), developer tools (Git, GitHub, Docker), cloud services (AWS, GCP, Cloudflare), productivity tools (Slack, Google Drive, Notion), and specialized domains like memory management, search, and analytics.

The combination of an open specification, official SDKs in popular languages, and broad client support created a network effect. Developers build servers because clients support them. Clients add support because servers exist. The result is that MCP has become the de facto standard for connecting AI assistants to external capabilities in a relatively short time.

Why Developers Should Care

For tool builders, MCP means you build your integration once and it works everywhere. Instead of maintaining separate plugins for each AI client, you build one MCP server and any MCP-compatible client can use it. This dramatically reduces the effort to make your tool available to AI assistants and ensures that new clients automatically work with your server as they add MCP support.

For application developers, MCP means you can compose AI capabilities from existing servers rather than building everything from scratch. Need your AI app to access a database? Use an existing database MCP server. Need persistent memory? Connect to a memory MCP server like Adaptive Recall. Need file access? Use a filesystem MCP server. You assemble capabilities rather than implementing them.

For teams, MCP means consistent tooling. When you commit an MCP configuration to your repository, every developer who clones it gets the same AI-powered tools without manual setup. The AI assistant for each developer has access to the same databases, memory stores, and services, which means the assistant can give consistent, informed answers regardless of who is asking.

MCP and Memory Systems

MCP is particularly well-suited for memory systems because it provides the persistent, bidirectional connection that memory requires. A memory server needs to store information during one session and recall it in another. With MCP, the memory server runs continuously (or starts on demand), and the AI client connects to it at the beginning of each session. The model can store observations, recall past context, and explore the knowledge graph through standard tool calls, without any custom integration code.

Adaptive Recall is built as an MCP server precisely because the protocol provides the right abstraction for memory operations. The seven tools (store, recall, update, forget, reflect, graph, and status) map directly to MCP tool definitions. Any MCP-compatible client gains full memory capabilities by adding a single configuration entry, no code changes, no plugins, no custom API wrappers.