Modern AI agents and large language models (LLMs) are exceptionally capable at reasoning and generating text, but they often struggle to access real-time data or external tools without a lot of custom engineering. Traditionally, integrating an LLM with a database, an API, or other software required brittle glue code and bespoke connectors. The Model Context Protocol (MCP) addresses this by providing an open, standardized interface that connects AI applications to external data sources and tools. In MCP, an AI host (the agent or application) can dynamically discover and call tools or access data exposed by one or more MCP servers, all through a common protocol. In effect, MCP lets agents “plug into” data sources much like a USB-C port standardizes device connections.

MCP was developed by Anthropic and other partners as an open-source standard. Anthropic’s announcement explains that MCP is a “new standard for connecting AI assistants to the systems where data lives”. The official docs define MCP as “an open-source standard for connecting AI applications to external systems” (like local files, databases, search engines, calculators, etc.). In short, MCP decouples AI models from specific APIs by letting servers declare what capabilities (tools, data, prompts) they support in a machine-readable way. This means any MCP-capable agent can tap into any MCP server’s functionality without custom coding for each new service.

Why MCPs Matter for Agentic AI

As AI agents become more agentic – able to take multi-step actions and use tools autonomously – having a unified way to integrate tools is crucial. Today’s models are powerful but “typically work in a vacuum,” relying on lengthy prompts or custom function-calling wrappers to use external resources. Every new integration is like building a bespoke pipeline: it’s time-consuming, brittle, and hard to scale. MCP solves this by replacing fragmented point-to-point connectors with one standard protocol. In Anthropic’s words, instead of “maintaining separate connectors for each data source, developers can now build against a standard protocol”. As a result, AI agents can maintain consistent context as they move between tools and datasets, dramatically simplifying development and making systems more reliable.

MCP offers concrete benefits across the AI ecosystem. For developers, MCP reduces development time and complexity because you build one MCP-compatible interface rather than many ad-hoc integrations. AI applications or agents gain access to an ecosystem of tools and data sources; with MCP, an agent can use any connected server’s capabilities without extra coding. Finally, end users get more powerful agents: MCP-enabled AI can access their data and perform actions on their behalf (e.g. scheduling events, querying documents) in a unified way. In short, MCP transforms isolated LLMs into context-aware assistants by breaking down “data silos”. By making connected systems the norm, MCP helps AI agents give more relevant, grounded responses (and reduces hallucination) while cutting the integration burden on developers.

  • Scalability & Maintenance: MCP eliminates the need for many custom connectors. One open standard replaces “fragmented integrations” so systems can scale without endless bespoke code.
  • Unified Tool Interface: Agents automatically discover available tools, resources, and prompts on each connected server. This “contract” tells the model what functions it can call and what data it can read, all in machine-readable form.
  • Better Agent Capabilities: Because MCP agents always “know what tools are available, what they do, how they work,” they can invoke real data and functions rather than guessing or hallucinating. For example, instead of making up a weather report, an AI can call a get_forecast function on a connected weather service server and return factual results. This shift – from novelty to team-player – is a central promise of MCP.

By contrast, without MCP an agent must rely on fixed prompt engineering or static code: anytime a dataset or API changes, the integration must be rewritten. MCP turns that upside-down: the server advertises its interface, and any compliant agent can pick it up. The net effect is a modern AI stack where “every tool, every system, every dataset is natively usable by an AI model with minimal glue”.

MCP Architecture: Host, Clients, and Servers

MCP follows a simple client-server architecture. An AI application (the MCP host) creates one MCP client connection per data source, and each MCP client talks to one MCP server. In practice, the host is the AI agent or application (for example, the Claude Desktop app or a custom agent runtime) that wants context. The MCP client is a software component in the host that manages a single connection to a server. The MCP server is a program that runs independently and provides context (tools, data, prompts) to any client that connects.

  • MCP Host: The AI application or agent that coordinates MCP clients (e.g. Claude Desktop, a Python agent, or an IDE with an agent plugin).
  • MCP Client: The connector module that the host uses to link to one MCP server. Each client maintains its own channel and fetches context from its server for the host to use.
  • MCP Server: The external program exposing tools and data. It speaks the MCP protocol so that any host’s client can query it. Servers may run on the same machine (local) or on the network (remote).

For example, imagine Visual Studio Code as an MCP host. When VS Code connects to a GitHub MCP server or a Slack MCP server, VS Code’s runtime spawns an MCP client for each. The documentation notes that “each MCP client maintains a dedicated one-to-one connection with its corresponding MCP server”. Because of this, hosts can aggregate multiple contexts: one client might fetch a user’s calendar events, another might query a database, and another might call a code-completion tool. The host simply uses the MCP protocol to coordinate all these clients.

MCP supports different communication layers under the hood, but the high-level flow is the same. The data layer (JSON-RPC) defines messages for lifecycle management, capabilities discovery, tool invocation, etc. The transport layer handles how messages are sent: for local servers it might use STDIO pipes, and for remote servers it uses HTTP/SSE with authentication. In either case, the host asks “what can you do?” on connection, and the server replies with a list of tools, resources, and prompts. The model can then call a tool or query data exactly as specified by the protocol.

Servers can run locally or remotely. A local MCP server might be a process on your machine (communicating over STDIO), while a remote one could be hosted in the cloud (using HTTP with OAuth tokens). For example, Claude Desktop might launch a local “filesystem” server when started (communicating via STDIO), or connect to a cloud-based GitHub server over HTTPS. The protocol is the same on both ends.

How Developers and Users Interact with MCP

For a developer, using MCP means building or using MCP servers and clients rather than writing custom integration code. Anthropic provides a reference specification and SDKs on GitHub, making it relatively straightforward to create MCP components. In practice, a developer might do the following:

  • Build/Deploy an MCP Server: Use an MCP SDK or library to write a server that exposes some tools, resources, and prompt templates. For instance, Anthropic’s docs walk through creating a “weather” MCP server. That server registers two functions (get_forecast and get_alerts) and then waits for MCP connections. The developer can run this server locally (for testing) or deploy it on a cloud machine.
  • Connect via an MCP Client: On the AI app side, the developer configures an MCP client to connect to the server (providing host URL, credentials, etc.). This client can be part of an agent framework or an LLM application. Once connected, the host knows exactly what tools the server offers.
  • Invoke Tools in Prompts/Code: When building prompts or implementing the agent, the developer can use MCP calls as if they were built-in functions. For example, instead of writing fetch_weather(city) with custom code, the agent’s program logic calls the get_forecast tool via MCP. Under the hood, the MCP client sends a JSON-RPC call to the server, which executes the function and returns the result.

From the user’s perspective, MCP integration is mostly transparent. A user interacts with an agent through natural language or UI, and the agent handles MCP behind the scenes. For example, consider a user asking, “What’s the weather in Paris today?” With MCP, the agent (host) would format this as a get_forecast(location="Paris") call to the weather server, get the actual data, and then present the answer. The user doesn’t need to know the server’s API or code; the agent did the lookup via the MCP-defined tool. In effect, MCP lets users leverage all their tools and data sources just by talking to the AI.

Under the hood, MCP handles the initialization handshake and data exchange so the developer doesn’t have to manage it manually. For instance, when the Claude host connects to the weather server, they exchange messages to negotiate protocol versions and agree on authentication. Once set up, each tool call is simply a JSON message back and forth. The MCP SDKs (for Python, Node.js, etc.) abstract these details. As Anthropic notes, tools in MCP are “functions that the model can call” and resources are “file-like data that can be read by clients”. Developers define these in code or via schemas, and the MCP system makes them available to the LLM.

In practice, an AI agent with MCP support works like this: The host starts and connects all configured servers. The agent’s prompt includes the discovered tools. When the user issues a query requiring an action, the agent picks the relevant tool and calls it through the client. The server executes and returns the answer, and the agent continues reasoning with that factual information. This workflow ensures every step is either an LLM inference or an actual tool invocation – eliminating unsupported guesswork. As one Red Hat blog put it, “the assistant doesn’t hallucinate, and instead uses a defined prompt” or tool. MCP essentially automates the agent’s use of external tools, making AI much more reliable and capable.

Real-World MCP Implementations

MCP is already seeing production use in industry. Anthropic’s Claude has built-in MCP support: every Claude plan lets teams connect MCP servers via the Claude desktop app or API. Anthropic open-sourced the MCP specification and shared example server implementations for common enterprise systems (Google Drive, Slack, GitHub, PostgreSQL, Puppeteer, etc.). In other words, if your organization uses Slack and GitHub, you can plug those into Claude with prebuilt MCP connectors. Anthropic also announced that companies like Block and Apollo (the blockchain firm) have integrated MCP in their workflows, and developer platform vendors (Zed, Replit, Codeium, Sourcegraph) are collaborating on MCP to enrich code editing and automation. These examples show how MCP unifies many tools under a single protocol.

Google’s Data Commons project provides another example. In September 2025, Google launched a Data Commons MCP Server to make its public datasets accessible to agents. The Google blog explains that with this server, developers no longer need to learn complex Data Commons APIs; an AI agent can query Data Commons natively. The server supports queries from simple data discovery to complex reports – for example, questions like “What health data do you have for Africa?” or “Generate a report on income vs diabetes” can be directly handled by the agent. Google even highlights the ONE Data Agent as a case study: by using the MCP server, the ONE Data AI platform lets users search tens of millions of health financing data points in seconds with plain-language queries. This shows how MCP enables non-experts to interrogate large datasets without programming, significantly reducing AI hallucinations and manual data wrangling.

Beyond Claude and Data Commons, MCP is gaining ecosystem momentum. Red Hat reports that major AI platform vendors have embraced MCP: Anthropic of course created it, but “OpenAI is integrating MCP into ChatGPT and the Agents SDK, Microsoft supports it in Copilot Studio, and developer tools like Replit, Cursor, Sourcegraph, and Zed list MCP among their integrations”. In other words, an MCP-capable agent could theoretically work the same way with Claude, ChatGPT, Microsoft Copilot, or other LLM backends – it’s a cross-platform standard. This broad support means MCP is fast becoming the common language of agentic systems. (For developers, this means that building against MCP today helps future-proof integrations across multiple AI services.)

The Future: SerpApi and MCP

At SerpApi, we see MCP as a powerful way to bring our web search data into the AI agent ecosystem. Today SerpApi provides real-time, structured search results via API. SerpApi already provides an MCP connector to test for developers. Currently it supports only a limited number of endpoints, but we plan to expand it's functionality soon and make it fully compatible with the API. This would let developers build agents that incorporate live search results (e.g. fetching current news, facts, or product data) without extra glue code, all through the standard MCP interface. Watch for SerpApi’s upcoming MCP compatibility – it will let any MCP-capable AI seamlessly use our search and analytics tools as part of its context.

In summary, the Model Context Protocol is an open standard designed to glue together AI models with the world of data and tools in a unified way. By defining how an MCP host (the AI) and servers (external systems) share context, MCP eliminates custom integration complexity and unlocks new agent capabilities. Official sources emphasize that MCP is essentially a universal “USB-C port” for AI, letting agents plug into anything from databases to calculators. As companies like Anthropic and Google have shown, building on MCP makes agents far more powerful and robust: they can retrieve live data, call real functions, and maintain context across tools – paving the way for truly intelligent, multi-tool AI assistants.