Model Context Protocol

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- · MT Research Labs

What is MCP?

Model Context Protocol is an open standard that lets apps talk to external tools in a structured, secure way.

Think of it as a universal adapter: it allows AI models to access local tools, APIs, databases, search engines, or custom scripts through a consistent protocol. Instead of every app inventing its own plugin format, MCP creates one shared language.

Key points:

• It runs servers that expose “tools” such as search, file access, or custom functions.
• LLMs like ChatGPT or LM Studio, AnythingLLM, ollama connect to these servers and call the tools.
• It works locally or over a network and gives you control over permissions.
• Developers can build their own MCP servers to integrate anything from Google Search to Python scripts.

It is designed to make AI setups modular, customizable, and more powerful.

An MCP AI agent refers to an AI agent that uses the Model Context Protocol (MCP) to communicate with external tools and data sources, making it more capable and versatile. MCP is an open standard that acts as a standardized interface, enabling AI agents to securely access and interact with APIs, databases, and applications by providing a common language for calling tools and getting data.

Agent-to-Server Communication:
The AI agent (the client) uses the MCP protocol to communicate with MCP servers.

Tool Discovery:
When the agent needs to perform a task, it queries an MCP server to find out which tools or services are available.

Context and Execution:

The server returns information about the tool’s capabilities and requirements. The agent then uses this information to make a request to the server, which executes the action on the underlying data source (e.g., a database or API).

Response to Agent:

The server sends the results of the action back to the agent, which incorporates the information into its response to the user.

With MCP, you can do web enquiries in a locally run LLM system.

But….I can use Google if i want to run a live search, right? So whats the benefit?

1. Search & Live Data becomes part of your workflow
Instead of switching tabs to Google Chrome and manually searching, the local model can query Google inside your prompt, retrieve results, filter them, summarise them, compare them, cross check them, or turn them into structured data.

2. Fully automated research
You can run a single prompt like
“Find the top 10 Singapore hotels near Marina Bay with prices, sort by rating, and summarise the pros and cons.”
(although these can be done with OpenAI Atlas and Perplexity Comet now, Nov 2025)

The MCP server calls Google search APIs, fetches data, and the model processes everything for you in one chain.

3. Local control and privacy
You stay inside your locally run LLM, not a cloud browser. The model uses your API keys, your rules, your local environment.

Contact MT Research Labs if you’ll like us to incorporate and systemize a local LLM for your enterprise.