MCP and AI

How MCP Gives AI Assistants Real Capabilities

AI assistants are powerful, but they have limits. They know a lot about the world from their training, but they cannot access your live data or use your tools. The Model Context Protocol, or MCP, changes that. This post explains how MCP works and why it matters.

The Limitation of Training Data

When you talk to an AI assistant like Claude or ChatGPT, it responds based on what it learned during training. That training data is a snapshot of information from when the model was created. It is like reading a book that was published last year. The information might be good, but it does not include anything that happened after publication.

This creates several problems:

• The assistant cannot see your current data

• The assistant cannot perform actions in your systems

• The assistant cannot access real-time information

• The assistant cannot work with your specific tools and workflows

You might ask the assistant to check your customer support calls from yesterday. Without MCP, the assistant cannot do that. It does not have access to your systems. It can only work with the information in its training data.

What is Model Context Protocol?

Model Context Protocol, or MCP, is an open standard that lets AI assistants interact with external tools and data sources. Think of it as a common language that AI assistants and your systems can both understand.

MCP defines three main ways for assistants to interact with external systems:

Tools - These are actions the assistant can perform. A tool might create a new record, search a database, or update information. Tools are like functions the assistant can call.

Resources - These are read-only data sources the assistant can access. A resource might be a specific file, a database record, or a web page. Resources are like URLs the assistant can fetch.

Prompts - These are templates that guide the assistant on how to do something. A prompt might explain how to search effectively or what information to include. Prompts are like instructions or recipes.

Together, these three mechanisms give the assistant capabilities it did not have before.

A Simple Analogy

Imagine you have a smart assistant in your office. Without MCP, the assistant only knows what was in the training materials. It is like having someone who read a manual but has never actually used your office equipment.

With MCP, you give the assistant:

• Tools it can use, like your phone system, your database, and your file system

• Resources it can read, like your customer records and your company documents

• Prompts that explain how your office works, like how to file paperwork or who to contact for different issues

Now the assistant can actually do work, not just answer questions about what it read in a manual. It can look things up, perform actions, and work with your actual systems.

How MCP Extends AI Capabilities

Let us look at each part of MCP in more detail.

Tools: Actions the Assistant Can Perform

Tools are executable operations. When you ask the assistant to do something, it can choose a tool that performs that action. For example, if you ask the assistant to find customer support calls about billing, it might use a search tool provided by your conversation database.

Each tool has:

• A name that describes what it does

• A description that explains when to use it

• Input parameters that define what information it needs

• Output that describes what it returns

The assistant understands these tool definitions and can decide when to use each one. It is like giving the assistant a toolbox where each tool is labeled and has instructions.

Resources: Data the Assistant Can Read

Resources are URI-based data access points. The assistant can request a resource and get data back. For example, if you ask about a specific conversation, the assistant might fetch a resource that points to that conversation's data.

Resources are read-only. The assistant cannot change data through resources. It can only read. This makes resources safe for the assistant to explore your data without accidentally modifying anything.

Resources are discoverable. The assistant can ask what resources are available and then access them. It is like giving the assistant a catalog of all the data it can read.

Prompts: Guidance for the Assistant

Prompts are templates that help the assistant understand how to accomplish tasks. They provide context and step-by-step guidance. For example, a prompt might explain how to search for conversations by tags, or what information to include when creating a new conversation record.

Prompts are not just instructions. They can include examples, best practices, and warnings about common mistakes. They help the assistant work more effectively with your specific systems and requirements.

How the vCon MCP Server Uses MCP

The vCon MCP Server implements MCP to give AI assistants access to conversation data. It provides:

Over 27 tools for managing conversations. These include creating conversations, searching them, updating them, adding analysis, managing tags, and getting analytics.

Resources that let the assistant directly read conversation data using URI paths. For example, the assistant can request a resource like vcon://uuid/abc123 to get a specific conversation, or vcon://uuid/abc123/parties to get just the participant information.

9 prompts that guide the assistant on effective searching and retrieval. These prompts help the assistant understand when to use different search strategies and how to structure queries.

When you ask the assistant to work with conversation data, it uses these tools, resources, and prompts. The assistant does not need to know the technical details of how your database works. It just needs to know which tools to use and how to use them.

An Example Conversation

Let us see how this works in practice. You are talking to your AI assistant:

You: "Find all the customer support calls from last week where the customer was frustrated."

Assistant: The assistant sees you want to search for conversations. It looks at the available tools and sees several search options. It decides to use semantic search because you mentioned "frustrated," which is about sentiment rather than exact words. The assistant uses the search_vcons_semantic tool with parameters like the date range and a query about customer frustration.

Assistant: "I found 12 support calls from last week where customers showed frustration. Would you like me to summarize the common issues, or show you specific calls?"

You: "What were the main issues?"

Assistant: The assistant uses the search results it already has and analyzes them. It might use additional tools to get more details about specific conversations, or it might analyze the data directly from the search results.

Assistant: "The main issues were: delivery delays (5 calls), billing errors (4 calls), and product defects (3 calls). Should I get more details on any of these?"

This conversation flows naturally, but behind the scenes the assistant is using MCP tools to access your actual conversation data. It is not making things up based on training data. It is working with your real data.

Why Standards Matter

MCP is an open standard, not a proprietary system. This means:

• Any AI assistant can implement MCP support

• Any system can provide MCP tools, resources, and prompts

• You are not locked into one vendor

• The community can improve and extend the standard

• Documentation is public and accessible

This is important because it means MCP will work with future AI assistants, not just the ones available today. If a new assistant comes along that you prefer, it can still use the same MCP servers you have set up.

It also means you can build your own MCP servers for your specific needs. You are not limited to what vendors provide. You can create tools that match exactly what your business needs.

Benefits of the MCP Approach

Using MCP with AI assistants provides several benefits:

Real-time access - The assistant can work with your current data, not just historical training data.

Actionable capabilities - The assistant can perform actions, not just answer questions.

System integration - The assistant can work with your existing tools and databases.

Natural interaction - You talk to the assistant in plain language, and it figures out which tools to use.

Extensibility - You can add new tools, resources, and prompts as your needs grow.

Security - The assistant only has access to what you explicitly provide through MCP. You control what it can see and do.

How It Differs from Traditional APIs

You might wonder how MCP differs from traditional APIs. Traditional APIs require you to know specific endpoints, parameters, and response formats. You need to write code or configure integrations.

MCP works at a higher level. The assistant understands what tools are available and how to use them. You do not need to write code or configure complex integrations. You just talk to the assistant, and it handles the details.

This does not mean MCP replaces APIs. MCP often uses APIs under the hood. But it presents them to the assistant in a way the assistant can understand and use intelligently.

The Future of AI Integration

MCP represents a new way of integrating AI assistants into your work. Instead of treating the assistant like a separate tool, MCP lets you treat it like a team member who has access to your systems.

As MCP grows and more systems adopt it, AI assistants will become more capable. They will be able to work with more types of data and perform more types of actions. The vCon MCP Server is one example of this future. It gives assistants the ability to work with conversation data in a standard way.

Conclusion

MCP bridges the gap between AI assistants and your systems. It gives assistants real capabilities by providing tools, resources, and prompts they can understand and use. The vCon MCP Server implements MCP to make conversation data accessible to AI assistants.

The next post in this series covers the complete scope of what the vCon MCP Server can do. It goes into detail about all the features and capabilities available.