An AI model can perform basic tasks like writing a product description or designing your ad copy. But if you want to get a quick update of your CRM platform just by asking your AI agent, you need to first connect your AI tool with your CRM platform. MCP is the way to connect those two apps.

What Is MCP

MCP stands for Model Context Protocol. It is an open standard for connecting AI assistants to the places where data lives, such as content repositories, business tools, customer relationship management platforms, and development environments.

Any business has around 10 to 20 different business tools. Previously, if you wanted your business to work with AI, you needed a developer to build and maintain a custom integration for each tool. Now, MCP has made connecting with AI much easier. Any AI platform that understands MCP (all major AI platforms do) can connect with any business tool.

How MCP Works

MCP has three main layers:

• MCP host: MCP host is the AI application you use, such as ChatGPT or Claude.
• MCP client: MCP client is a small connector inside the host. The host creates one MCP client for each MCP server, and each client keeps its own dedicated connection to its server.
• MCP server: MCP server is a program that provides context to MCP clients. A GitHub MCP server, for example, gives the AI a standard way to read issues or open pull requests.

3 Things an MCP Server Can Offer to the AI

An MCP server can give AI three building blocks.

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1. Tools

Tools are actions AI can perform through an MCP server, like “Send a Slack message to the Marketing channel.” They are executable functions that AI applications can call to do things like file operations, API calls, and database queries. Tools are model-controlled, meaning they are functions exposed to the AI model so it can take actions. The model decides when a tool is needed based on what you asked.

2. Resources

Resources are information. They are data sources that give the AI app contextual information, such as file contents, database records, or API responses. Where a tool does something, a resource shows something. Resources are application-controlled, which means the client attaches and manages this contextual data, such as file contents and git history.

3. Prompts

Prompts are reusable templates. They help structure interactions with language models, such as system prompts or few-shot examples. Prompts are user-controlled. They are interactive templates the user chooses to run, often shown as slash commands or menu options. Think of a “/weekly-report” command that always pulls the same data and formats it the same way.

How an MCP Request Works: Step by Step

To understand how an MCP request works, let’s follow the example of Claude connected to GitHub and Slack. In this case, GitHub and Slack are MCP servers, and Claude is the MCP host. 

If you type this query in Claude, “Find all open GitHub issues labeled 'urgent' and post a summary in the #engineering Slack channel,” your MCP host and both MCP servers do a quick handshake. 

In this case, tools enable the request to search GitHub issues and send a Slack message, resources may provide access to issue data or other relevant information if the server exposes these resources, and prompts may provide a reusable workflow or instructions for summarizing and reporting urgent issues. 

MCP client (the connector) sends an initialize request so both sides can agree on a protocol version and declare which features they support. Then the real work begins. Your MCP client will connect separately to GitHub and Slack and perform the relevant tasks.

What Makes MCP Powerful for AI Agents

An AI agent is an AI system that can plan and carry out multi-step tasks. But to perform those tasks, your AI agents need access to the tools where your tasks live. Here’s why MCP is powerful for AI agents:

1. Agents can connect to many systems at once

Work rarely lives in one app. A single sales task can touch the CRM, email, and calendar. An MCP host can connect to one or more MCP servers at the same time, so one agent can work across all of them in a single task.

2. Tools can change without breaking the agent

Business systems change. New features appear, permissions shift. When a server’s tools change, it can send a notification to connected clients, and the client typically responds by requesting the updated tool list. The agent keeps working with the current set of tools instead of relying on a stale one.

3. Agents can check in with people

Good agents don’t guess on important decisions. MCP includes a feature called elicitation, which lets servers ask the user for more information or confirmation of an action. That’s useful before an agent performs a critical task.

4. The ecosystem does a lot of the work

Since MCP is a shared standard across all AI platforms, you don’t have to build every connection yourself. There are pre-built connections for thousands of apps.

WorkflowFiesta Speaks MCP Natively

WorkflowFiesta is an AI orchestration platform that connects AI models like Claude or GPT to business apps like Slack, Jira, Salesforce, and more, so the AI understands what you want in plain language and the platform makes it happen across your tools.

That’s the same problem MCP was built to solve, and it’s why WorkflowFiesta is built to work with it. The WorkflowFiesta CLI handles mid-run agent requests inline, including secure credential forms, browser-based OAuth, and MCP server configuration and connection. 

If an agent needs access to an MCP server partway through a task, you can set it up right there in the conversation.

Here’s what WorkflowFiesta adds around those connections.

• Plain Language Control: Teams describe what they need in plain language, and AI agents handle the rest with no developers or training required. It works with Anthropic, OpenAI, AWS Bedrock, and any AI model you can bring.
• Guardrails on Every Action: You set granular permissions per agent, app by app, from read-only to write to fully blocked. Every action is logged with who requested it, what ran, what changed, and when. Passwords, credit card numbers, and PII are detected and scrubbed before they reach a model, and any agent action can be reversed in one click.
• Built-In Guard Agents: Auth Cop runs security reviews and returns a decision of approved, awaiting confirmation, or declined, while Secret Safe posts redaction notices when it detects secrets.
• Safe Credential Handling: Credentials are encrypted and never exposed. Once saved, they’re referenced by name, and no one sees the actual value again, including you.
• Access to Local Systems: The WorkflowFiesta Runner is a lightweight app you install on your own computer or server, so agents can read local files, access internal systems, and run tasks on your machine without exposing anything to the internet.
• Multi-Agent Workflows: One plain-language request can dispatch a chain of specialized agents, each handling its part and passing results forward, in parallel or in sequence.