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.
Connect to MCP Servers
Connect to any Model Context Protocol server and give your employee new tools instantly.
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.
MCP connections are scoped per employee. Your engineering employee connects to your GitHub MCP server. Your data analyst connects to your database MCP server. Each employee gets exactly the capabilities it needs.
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.
Use Cases
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.
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.