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EngineeringFeb 28, 2026·2 min read

A Practical Guide to MCP Servers for AI Agents

Model Context Protocol is how agents connect to the world. Learn how to use built-in MCPs and build your own.

By Padiso Team

Model Context Protocol (MCP) is the open standard that lets AI agents connect to external tools and services. Think of it as the USB port for AI — a universal interface that works with any agent runtime.


What Is MCP?


MCP defines a standard way for agents to discover and use tools. An MCP server exposes capabilities (like "read a file" or "send a Slack message") that any MCP-compatible agent can call.


This means you connect an integration once, and every agent on your platform can use it.


Built-in MCP Servers on Padiso


Padiso ships with 50+ pre-built MCP integrations:


•Communication: Slack, Discord, Microsoft Teams
•Development: GitHub, Linear, Figma
•Data: PostgreSQL, Snowflake, MongoDB
•Productivity: Notion, Google Workspace, HubSpot
•Cloud: AWS S3, Google Cloud Storage, Dropbox

Each integration is managed by Padiso — we handle authentication, rate limiting, and error recovery.


Building Custom MCP Servers


Need to connect to an internal tool? Build a custom MCP server. Here's the basic structure:


1.Define your tools with input/output schemas
2.Implement the tool handlers
3.Deploy the MCP server (or point Padiso to your endpoint)
4.Your agents can now use the tools

Custom MCP servers run in isolated environments on Padiso, with full logging and monitoring.


Best Practices


•Scope tools narrowly: A tool that "manages all of GitHub" is too broad. Break it into specific actions.
•Handle errors gracefully: Agents will retry, so make your tools idempotent.
•Add descriptions: Good tool descriptions help agents choose the right tool for the job.
•Test with multiple runtimes: Different AI models may call your tools differently.

MCP is the backbone of agent interoperability. Master it, and your agents can do anything.