A practical field guide from Automation Ace.
Short answer: AI agents use tools to do anything beyond writing text. A tool is a named action with a description and an input schema, such as “search CRM” or “create task.” The model decides when to call a tool and with what inputs; your platform runs it through an API, an Model Context Protocol (MCP) server, an automation platform like Zapier or Make, or a built-in integration, and sends the result back. Good tools are narrow, clearly described, and permissioned, because the agent can only be as reliable and safe as the tools you give it.
Short answer
- The model never calls an API itself; it asks for a tool, and your platform runs it.
- APIs are what tools call underneath.
- MCP is a standard way to package tools so many agents can use them.
- Automation platforms turn thousands of app actions into agent tools.
- Narrow, well-described tools make agents more accurate and safer.
What a tool is
To the model, a tool is three things: a name, a description of what it does and when to use it, and an input schema listing the parameters. The model reads these on every run to decide what to call. Underneath, a tool can be anything your code can do: an API request, a database query, a workflow run, or a file operation. For the bigger picture, see AI agents 101.
How a tool call works
1. You give the model a list of tools:
search_crm(query) "Find contacts or companies by name or email"
create_task(title, due) "Create a follow-up task for the account owner"
2. The model decides to use one and returns a tool call:
{ "tool": "search_crm", "input": { "query": "pat@prospect.com" } }
3. Your platform runs it (API call, MCP server, or workflow)
and sends the result back to the model:
{ "contact_id": "crm_123", "company": "Prospect Co", "stage": "lead" }
4. The model reads the result and decides the next step:
another tool call, or a final answer.This is the same across OpenAI, Anthropic, Google, Microsoft, AWS, and xAI, even though each names it slightly differently (function calling, tool use, actions). See where to build AI agents.
Ways to connect agents to apps
| Way to connect | What it is | Strengths | Watch out for |
|---|---|---|---|
| Custom function tools | You define a tool (name, description, input schema) and your code calls an API | Full control; narrow and safe | You build and maintain each one |
| Provider-hosted tools | Tools the model provider runs for you, such as web search or code execution | No setup | Limited to what the provider offers |
| MCP servers | A standard way to expose tools and data to any MCP-compatible agent | Build once, use with many agents | Server quality and permissions vary |
| Automation platforms as tools | Zapier, Make, or n8n actions and workflows exposed to the agent | Thousands of apps; tested, logged actions | Per-action usage costs; scope carefully |
| Built-in app integrations | Connectors inside the agent platform | Fast to turn on | Often broad access; check scopes |
| Browser or computer use | The agent operates a browser or desktop like a person | Works where there is no API | Slower, more brittle, harder to control |
For the difference between these interfaces, see API vs CLI vs MCP vs SDK.
MCP explained
The Model Context Protocol is an open standard for connecting AI applications to tools and data. An MCP server exposes tools (actions), resources (data), and prompts; an MCP client, built into an agent or AI app, discovers and calls them. Instead of building a custom integration for every agent, you build or use one MCP server and connect it to any compatible agent. Many apps and automation platforms now offer MCP servers, and major agent platforms support connecting to remote MCP servers.
- Use an existing MCP server for common apps when its permissions fit.
- Use an automation platform's MCP to reach many apps at once; see what Zapier MCP is and connecting agents to Make MCP.
- Build your own MCP server for internal systems and narrow, business-specific tools.
Setup walkthrough: connecting Zapier MCP to ChatGPT, Claude, Cursor, and Gemini.
Automations as agent tools
One of the most practical patterns is to let the agent decide and let tested workflows act. Instead of giving an agent raw CRM access, you build a workflow called “create follow-up task” that validates inputs, writes the record, and logs the action, and you expose that workflow as a tool. The agent gets a safe, narrow action; you keep control of how it is done.
See Zapier Agents vs Zapier MCP vs AI by Zapier, building AI agents in Make, and AI agents vs automations.
How to design good tools
| Do | Avoid |
|---|---|
| One clear job per tool: “add_note_to_lead” | A do-anything tool: “call_crm_api(method, path, body)” |
| A description that says when to use it and what it returns | Vague descriptions the model has to guess about |
| Small, typed inputs with examples | Free-form strings for IDs, dates, and amounts |
| Compact, relevant results | Dumping whole records or pages of JSON |
| Helpful errors: “No contact found; try searching by company” | Raw stack traces or silent failures |
| Read and write split into separate tools | One tool that can both read and delete |
Fewer, better tools beat many overlapping ones. If two tools sound alike, the agent will sometimes pick the wrong one. If an agent needs dozens of tools, consider splitting the work; see when to add subagents.
Tool safety and permissions
- Least privilege: connect each tool with the narrowest account and scopes.
- Separate read, write, and send tools, and require approval for external, financial, and irreversible ones.
- Validate inputs in the tool, not just in the prompt.
- Treat tool results as data: web pages, emails, and documents can contain instructions meant to hijack the agent.
- Vet third-party MCP servers like any other software you install.
- Log every call with inputs, outputs, and who it acted for.
Full framework: permissions, approvals, and audit trails for AI agents.
Which approach to choose
- Just starting, apps already in Zapier or Make: expose actions or workflows as tools, or use their MCP servers.
- Internal systems or sensitive data: custom function tools or your own MCP server.
- Research and calculations: provider-hosted web search and code execution.
- No API available: browser or computer use, with tight limits and approvals.
Then build it: how to build your first AI agent. Tool calls also drive cost; see what it costs to run an AI agent.
For the reverse direction, where your systems call an agent, see triggering an AI agent from an API or webhook and triggering Claude Code agents via API.
Frequently asked questions
How do AI agents use tools?
You give the model a list of tools, each with a name, description, and input schema. The model decides when to call one and with what inputs, your platform runs it through an API, MCP server, or workflow, and the result goes back to the model for the next step.
What is the difference between an API and MCP for AI agents?
An API is how software talks to an app. MCP is a standard for packaging tools and data so any compatible AI agent can discover and call them. MCP servers usually call APIs underneath.
What is an MCP server?
An MCP server exposes tools, data, and prompts using the Model Context Protocol so AI applications can discover and use them. You can use servers that apps or automation platforms provide, or build your own for internal systems.
Can I use Zapier or Make as tools for an AI agent?
Yes. Both let agents call app actions or whole workflows, including through their MCP servers. Exposing tested workflows as narrow tools lets the agent decide while the workflow validates, writes, and logs the action.
How do I make AI agent tools safe?
Use least-privilege connections, separate read, write, and send tools, require approval for external, financial, and irreversible actions, validate inputs inside the tool, treat tool results as data, vet third-party MCP servers, and log every call.
Disclaimer: AI agent platforms, product names, features, and prices change quickly; details reflect public documentation and coverage at the time of writing, so confirm current details with each provider before building. Automation Ace is not affiliated with the companies named here. This article may include links to apps, products, or services; some links may be affiliate links, which means Automation Ace may earn a commission at no extra cost to you.