Guide
MCP for Beginners
A practical introduction to connecting tools to AI agents.
Author: Manus AI - January 12, 2026
Introduction
The Model Context Protocol (MCP) is an open standard for connecting AI clients to external tools and data sources. It gives models a consistent way to use tools (actions), access resources (data), and run prompts (templates) without custom per-app integrations.
With MCP, a single server can work with multiple clients that support the protocol. The result is reusable integrations, clearer permissions, and workflows that are easier to standardize across teams.
TL;DR
What is MCP?
A standard protocol for tool connections
MCP defines a consistent, JSON-RPC-based interface that clients and servers can use to exchange tools, resources, and prompts. That standardization is what makes integrations reusable.
How it differs from one-off plugins
Rather than building bespoke integrations per client, MCP lets a server expose capabilities once and makes them available to any MCP-capable client. This reduces maintenance and improves consistency across tools.
When MCP is the right fit
Use MCP when your workflows need data or actions from outside your AI client: files, tickets, databases, email, calendars, or web sources. If copy-paste is part of the workflow, MCP is usually the next step.
Core concepts
Servers
Servers expose capabilities to clients. They can provide tools (actions), resources (data), and prompts (templates) over the MCP protocol.
Tools
Model-controlled actions like API calls, file writes, or database queries. Clients can apply permission gates before execution.
Resources
Client-attached context such as file contents, database records, or API responses. Resources are typically read-only.
Prompts
User-controlled templates that standardize common workflows. In Claude Code, MCP prompts can appear as slash commands.
Getting started
Use a client that supports MCP servers, prompts, and tools. Claude Code includes MCP support and can discover prompts dynamically.
Start with a local, read-only server like filesystem or a sandbox database. Avoid write permissions until the workflow is stable.
Add the server using your client’s configuration or CLI. In Claude Code, the MCP CLI supports local, project, and user scopes.
List available tools and prompts, then run a simple read-only action to confirm output shape and permissions.
Use least privilege, audit logs, and separate credentials for dev vs production. Add human review for write actions.
Use cases
Codebase analysis
Connect filesystem and version control servers to map architecture, find ownership, and review changes quickly.
Database exploration
Query schemas and run read-only analyses directly from your AI client without context switching.
Email and calendar
Summarize inboxes, prepare meeting briefs, and draft replies with explicit approval steps.
Web search and retrieval
Gather sources, extract key facts, and build structured research briefs without copy-paste.
Reporting and analytics
Combine CRM, spreadsheets, and databases to generate weekly reporting and KPI rollups.
Workflow automation
Chain tools like tickets + chat + docs to automate triage, handoffs, and status updates.
Common issues
Server not appearing
- Verify the server command and working directory
- Restart the client to reload MCP configuration
- Check server logs for startup errors
Permission errors
- Confirm credentials and scopes
- Use read-only access while testing
- Verify the client’s allowed directories
Slow or failing requests
- Reduce query size or add pagination
- Check upstream API limits and quotas
- Add caching or batching in the server