Building AI Assistants with Tool Use: A Practical Guide to MCP
Learn how to build AI assistants that can actually do things—send emails, query databases, control applications. A practical introduction to the Model Context Protocol (MCP) and tool use patterns.
Building AI Assistants with Tool Use: A Practical Guide to MCP
The difference between a chatbot that talks and an AI assistant that works comes down to one thing: tool use. Modern AI assistants can send emails, query databases, control applications, and interact with the real world—but only if they're properly connected to tools.
Enter the Model Context Protocol (MCP)—an open standard that's rapidly becoming the universal way to connect AI models to external capabilities.
The Tool Use Revolution
Large language models are remarkable at understanding and generating text. But until recently, they were trapped inside a text-only world. They could tell you how to send an email, but they couldn't actually send one.
Tool use changes everything. With the right integration, an AI assistant can:
- Query live data from databases, APIs, and documents
- Take actions like sending messages, creating files, or updating systems
- Access fresh information instead of relying on training data
- Automate workflows that span multiple systems
The challenge was always: how do you connect an AI to your tools in a standardised, secure way?
What is the Model Context Protocol (MCP)?
MCP is an open standard developed by Anthropic that provides a universal interface between AI models and external tools. Think of it as USB for AI—a common protocol that lets any AI model connect to any tool.
Key Concepts
MCP Servers expose tools and resources to AI models. A server might provide:
- Tools (functions the AI can call)
- Resources (data the AI can read)
- Prompts (templates for common tasks)
MCP Clients (like Claude Desktop or custom applications) connect to MCP servers and let the AI use their capabilities.
The Protocol handles the communication—describing available tools, executing function calls, and returning results.
Why MCP Matters
Before MCP, every AI integration was custom. Want Claude to query your database? Build a custom integration. Want GPT-4 to control your CRM? Another custom build. Each model, each tool, each combination—custom code.
MCP changes this:
- Write once, use anywhere: An MCP server works with any MCP-compatible client
- Composable: Connect multiple servers to give your assistant diverse capabilities
- Secure by design: Explicit permission models, sandboxed execution
- Open standard: Not locked to any vendor
Practical Examples
Example 1: AI-Powered Database Queries
Imagine giving your AI assistant access to your business database:
User: "Show me all orders over £10,000 from the last quarter"
AI: [Uses database tool to query orders table]
"Here are the 23 orders over £10,000 from Q4 2025:
1. Acme Corp - £45,200 (15 Dec)
2. TechFlow Ltd - £32,100 (3 Dec)
..."
The AI translates natural language into SQL, executes the query safely, and presents the results—all through MCP tool calls.
Example 2: Email Triage and Response
Connect your email system as an MCP server:
User: "Check my inbox and draft responses to anything urgent"
AI: [Uses email tool to list unread messages]
[Identifies 3 urgent emails requiring response]
[Uses email tool to create draft responses]
"I've drafted responses to 3 urgent emails:
- RE: Contract renewal deadline (accepting proposed terms)
- RE: Server outage (acknowledging and requesting status)
- RE: Budget approval needed (requesting specific figures)
The drafts are ready for your review."
Example 3: Multi-Tool Workflows
The real power emerges when you combine multiple tools:
User: "Schedule a meeting with John next week to discuss the Q1 budget"
AI: [Uses contacts tool to find John's email]
[Uses calendar tool to find mutual availability]
[Uses email tool to send invitation]
[Uses calendar tool to create event]
"Done. I've scheduled a 1-hour meeting with John Smith for
Tuesday 11th Feb at 2pm. Calendar invite sent."
Building Your First MCP Integration
Option 1: Use Existing MCP Servers
The fastest path is leveraging pre-built MCP servers. Popular options include:
- Filesystem server: Read/write files and directories
- Database servers: PostgreSQL, SQLite, MongoDB connectors
- Git server: Repository operations and history
- Web search: Brave Search, Google integration
- Slack/Discord: Team communication
These can be added to Claude Desktop or other MCP clients with minimal configuration.
Option 2: Build Custom MCP Servers
For bespoke business systems, you'll need custom MCP servers. The good news: MCP SDKs make this straightforward.
Key design principles:
- Start narrow: Begin with a few well-defined tools
- Explicit descriptions: AI models need clear tool descriptions to use them correctly
- Error handling: Return informative errors—the AI will use them to adjust
- Security first: Validate all inputs, limit capabilities appropriately
- Logging: Track what tools are called and why
Architecture Considerations
When designing AI assistant systems with tool use:
Separation of concerns: Keep your MCP servers focused. One server per domain (email, calendar, CRM) rather than one monolithic server.
Permission boundaries: Not every tool should be available to every user or every context. Design your permission model carefully.
Audit trails: Log all tool invocations. You need visibility into what your AI assistants are doing.
Human-in-the-loop: For high-stakes actions (sending external emails, making payments), require human confirmation.
Best Practices for Production
Security
- Principle of least privilege: Give AI assistants only the tools they need
- Input validation: Never trust AI-generated parameters blindly
- Rate limiting: Prevent runaway tool calls
- Sandboxing: Isolate tool execution from critical systems
User Experience
- Transparency: Show users what tools are being used
- Confirmation for actions: "I'm about to send this email. Proceed?"
- Graceful degradation: Handle tool failures elegantly
- Progress feedback: For long-running operations, keep users informed
Reliability
- Retry logic: Network failures happen—build in resilience
- Timeouts: Don't let tool calls hang indefinitely
- Fallback behaviours: What happens when a tool is unavailable?
- Testing: Unit test your tools, integration test your workflows
The Future of Tool Use
We're in the early days of AI tool use, and the landscape is evolving rapidly:
Agentic loops: AI assistants that can plan multi-step workflows, execute them, and adapt based on results—with minimal human intervention.
Tool discovery: AI models that can find and learn to use new tools automatically.
Collaborative agents: Multiple AI assistants working together, each with specialised tools and capabilities.
Standardisation: As MCP and similar protocols mature, we'll see richer ecosystems of interoperable tools.
Getting Started
If you're ready to move beyond chatbots to truly capable AI assistants:
- Audit your workflows: Which processes would benefit from AI tool use?
- Identify data sources: What systems should your AI access?
- Start small: One integration, one workflow, one success story
- Iterate: Learn what works and expand gradually
The barrier to building AI assistants that actually do things has never been lower. The question isn't whether to adopt tool use—it's how quickly you can deploy it effectively.
Building AI assistants with tool use? Get in touch to discuss how Caversham Digital can help you design and implement AI systems that work.