# Give Your Employee Any MCP Server

Paste the URL of any external MCP (Model Context Protocol) server into an employee's Tools tab, and Sistava discovers every tool it offers and hands them to that employee, with no code required. The connection is granted to the employee's whole team, so a database server, an internal API, or a community-built MCP server becomes a tool your team can call by name in every conversation. This is the fastest path to extending an employee past the built-in tool set.

The Model Context Protocol is the open standard for giving AI agents access to external tools and data. Point your employee at any MCP server and it gains new capabilities immediately, no code changes, no redeployment, no waiting.

Use public MCP servers from the community, connect to your company's internal MCP servers, or build your own. Each server can expose tools, resources, and prompts that your employee discovers and uses automatically.

A server you add is granted to the team the employee belongs to, so the whole team picks up the capability at once, and you can still allow or deny one individual on top of that. Your engineering team connects to your code MCP server. Your data analyst gets the database one. Each employee ends up with exactly the capabilities it needs, without you working through the list once per hire.

Adding one is a paste, not a project. Give the server a name, paste its HTTPS address, add an auth header if it needs one, and Sistava connects, asks the server what it can do, and lists every tool it found by name. From that moment the employee can call those tools in an ordinary conversation, and they sit in the Tools tab alongside every other capability it has.

Sistava connects to remote MCP servers over HTTPS. Servers that would run as a local command inside our infrastructure are refused on purpose, because that means running someone else's program next to every other customer's work, and the address you give is checked so it cannot be pointed at private internal infrastructure. A server you host yourself is fully supported as long as it is reachable over HTTPS.

Servers change, so there is a refresh. Trigger it and Sistava asks the server for its tool list again and updates what the employee can call, which is how you pick up something your engineering team shipped this morning without touching any settings. Remove a server and the connection is archived rather than deleted outright, so the record of what happened while it was connected stays intact.

## Connect Your AI Employee to Any MCP Server

Model Context Protocol (MCP) is an open standard for extending AI agents with external tools and data sources. Sistava acts as an MCP client, meaning your AI employee can connect to any MCP server and instantly gain access to the tools it exposes.

This gives you a direct path to extend your AI agent's capabilities without writing custom code. If a tool, database, or service provides an MCP server, your employee can use it. The agent discovers available tools automatically on connection.

## Extend Capabilities Without Building From Scratch

The MCP ecosystem is growing fast. Servers exist for databases, code execution environments, internal tooling, developer platforms, and specialized data sources. Each one your AI employee connects to adds a new set of usable tools to its repertoire.

For engineering teams, MCP is the fastest path to giving an AI agent access to internal systems. If you have existing tooling you want to expose to the agent, wrapping it as an MCP server is a well-documented, standardized approach.

## Live Tool Discovery at Connection Time

When your AI employee connects to an MCP server, it queries the server for its available tools and their descriptions. There is no manual configuration required on the Sistava side. The agent learns what tools exist and how to use them directly from the server.

This means your agent stays in sync with the MCP server automatically. If the server adds new tools or updates existing ones, the agent sees those changes the next time it connects, without any intervention from you.

## How It Works

**Your AI agent connects to any MCP server, discovers the tools it exposes, and starts calling them automatically without extra configuration.**

Add an MCP server URL and your AI employee queries the server to discover every tool it offers. From that point forward, those tools are part of the agent's toolkit. Internal databases, proprietary APIs, specialized retrieval systems, or custom tooling built by your engineering team. If it speaks the Model Context Protocol, your agent can use it.

Tool discovery is automatic and live. When the MCP server adds a new capability, the agent picks it up without requiring reconfiguration. This makes MCP the ideal path for connecting AI agents to internal systems that are too sensitive or too custom for a public OAuth catalog. Your team ships a tool server once, and every AI employee that needs it can connect.

Tools that arrive this way behave like every other tool the employee has. They obey the same approval setting, so anything that writes to a live system can be made to ask you first instead of acting on its own. They show up in the activity log with the same detail, and they pass through the same content policies on the way out. Connecting a server extends what an employee can do without opening a side door around the controls you already set.

## Use Cases

### Engineering team connecting an AI agent to internal tooling

Expose any internal capability as an MCP server and the AI employee can use it immediately.

### Platform team building reusable AI capabilities across the organization

Publish capabilities as MCP servers once and every AI agent in the platform can connect to them.

### Developer giving an AI agent access to a proprietary database

Wrap your database in an MCP server and the AI employee queries it like any other tool.

### Infrastructure team integrating an AI agent with CI/CD systems

MCP connects the AI employee to your pipeline. It can trigger builds, check status, and report results.

## Comparison

| Before | After |
|---|---|
| Custom integrations require building and maintaining bespoke connectors. | Any MCP server instantly extends what the AI employee can do. |
| Internal tools are inaccessible to AI agents. | Wrap internal capabilities in MCP and the agent uses them natively. |
| Sharing a capability across agents means duplicating the integration. | One MCP server serves every AI agent in the organization. |
| Extending agent capabilities requires platform changes. | Plug in a new MCP server and the AI employee gains the capability immediately. |

## FAQ

### What is Model Context Protocol (MCP)?

MCP is an open standard that lets AI agents connect to external servers to discover and use tools, access data, and call services. It gives developers a consistent way to extend agent capabilities without building custom integrations for every AI platform.

### Does Sistava act as an MCP server or an MCP client?

Sistava acts as an MCP client. Your AI employee connects to external MCP servers and uses the tools they expose. This is the opposite of acting as a server, it means your agent gains capabilities from the ecosystem rather than publishing its own.

### How do I connect my AI employee to an MCP server?

Add the MCP server's connection details in the employee's Integrations tab. The agent will connect to the server, discover its tools, and make them available for use in any conversation or automated workflow.

### Can I run my own private MCP server for internal tools?

Yes. MCP servers can be self-hosted. If your team runs an internal MCP server exposing proprietary data or tooling, your AI employee can connect to it just like any public MCP server, with full tool discovery and usage.

### Can my AI agent connect to internal tools through an MCP server?

Yes. The MCP client lets your AI employee connect to any server that implements the Model Context Protocol, exposing your internal tools and data as actions the agent can take. No custom API wrappers are needed.

### Do I need a developer to add an MCP server to my AI employee?

Not to add one. Adding an MCP server in Sistava means pasting its address into the employee's Tools tab and saving, and the tools it offers appear by name straight away. You need a developer only if the server itself does not exist yet and somebody has to build it.

### What happens when the MCP server adds a new tool?

Refresh the connection and Sistava asks the server for its tool list again, then updates what your employees can call. Nothing else has to change: no reconfiguration, no re-hiring, no code on your side. This is how a tool your team shipped this morning reaches your workforce this afternoon.

### Can Sistava connect to an MCP server running locally on my own machine?

Only if it is reachable over HTTPS. Sistava talks to remote MCP servers by address, and servers that would run as a local command inside our infrastructure are deliberately not supported for security reasons. Host it somewhere with an HTTPS address, or expose it through a secure tunnel, and it works like any other server.

> We wrapped our internal data warehouse as an MCP server. Every AI agent on our team can now query it directly. We went from zero integrations to full data access in a day.
>
> Tobias W., Platform Engineer · SaaS company

## Where Give Your Employee Any MCP Server fits

Give Your Employee Any MCP Server is part of Things they connect to.

Connect your AI agents to Gmail, Slack, Notion, Jira, HubSpot, and thousands more apps with one-click OAuth. Extend their reach with MCP servers and A2A agents for AI-to-AI interoperability, or call any REST or GraphQL API directly. Each connection is scoped per employee.

- [Things they connect to](/en/features/connections): 1,000+ tools. One click to connect.

## Read the guide

- [Guide: Give Your Employee Any MCP Server](/en/guide/equip/tools)

## More in Integrations

- [1,000+ App Integrations](/en/features/connections/integrations_900): Connect your AI employee to Gmail, Slack, Notion, HubSpot, and over a thousand other apps through secure OAuth, no API keys required. Authorize an app once for your company, then switch it on for any employee with one click. Your employee takes real action inside these apps, sending the Slack message, updating the HubSpot record, drafting the Gmail reply, instead of just describing what it would do.
- [E-commerce Management](/en/features/connections/ecommerce_management): Manage a Shopify store with an AI employee that reviews catalog, inventory, orders, and market evidence, then asks you before every live store change. Other e-commerce connections are catalog-listed and stay review-first while their provider actions are verified.
- [Give Your Employee an External AI Agent](/en/features/connections/custom_a2a_agent): Point an employee at any external A2A (agent-to-agent) agent's URL and it becomes a tool the employee can call on its own. Sistava fetches the agent's public Agent Card, turns each of its skills into a usable tool automatically, and keeps them refreshed as the remote agent changes. No manual mapping of inputs or outputs required.
- [Let Your Employee Trigger Webhooks](/en/features/connections/custom_webhook_tool): Point an employee at any outbound webhook, your own automation, a Zapier or Make scenario, an internal service, anything that accepts an HTTP call, and it becomes a tool the employee can trigger on its own during a task, not something you fire manually from a dashboard.
- [Let Your Employee Call Any API](/en/features/connections/custom_api_tool): Give an employee a config for any REST or GraphQL endpoint, the method, headers, auth, and the shape of the request and response, and it will become a tool the employee can call during a task. Unlike a one-way webhook trigger, this reads data back: the employee gets a structured response it can reason about and act on, not just a fired event. No code required on your side. This is on the roadmap and is not available yet.

## Explore

- [Every feature](/en/features)
- [Hire an AI employee](/en/market)
- [Pricing](/en/pricing)