# Let Other AI Clients Use Your Employee

Connect Claude Desktop, Claude Code, Cursor, Windsurf, ChatGPT, or any Model Context Protocol client directly to your AI employees. Generate an API key, add one config block, and your coding tools can send prompts to any employee by ID and get a real response back. No custom integration code required.

Your AI employees are not trapped inside the workspace. Expose any employee as an MCP (Model Context Protocol) server, and external AI tools can use it as a resource. Claude Desktop, Cursor, Windsurf, or any MCP-compatible client can talk to your employees, send them tasks, and receive results.

This creates powerful compositions. A developer using Cursor can ask their coding assistant to "check with the marketing employee about the new feature name" and Cursor routes that request to your marketing employee through MCP. The marketing employee uses its training, memory, and tools to respond. Two AI systems collaborating through a standard protocol.

MCP server exposure is per-employee, so you control which employees are available externally. Your internal-only employees stay internal. Your API-facing employees become tools that any MCP client in the world can use. This turns your specialized, trained AI workforce into a library of capabilities other systems can call.

## Expose Your AI Employee as an MCP Server

Model Context Protocol (MCP) is the emerging standard for connecting AI tools and agents across different platforms. With the MCP Endpoint feature, every AI employee in your Sistava workspace can be exposed as an MCP server, making it instantly accessible from Claude, Cursor, Windsurf, and any other MCP-compatible client.

This means a developer using Cursor can pull in your research AI employee as an MCP tool and have it synthesize documentation, answer codebase questions, or run analysis, all without leaving their editor. Your AI workforce stops being platform-specific and becomes part of the broader AI tooling ecosystem.

## One Click to Generate an MCP Endpoint

Enabling an MCP endpoint for an AI employee requires no configuration beyond turning it on. Sistava generates a unique MCP server URL and access credentials for that employee. Paste these into your MCP client's configuration, and the employee is immediately available as a tool.

The MCP server exposes the employee's capabilities as MCP tools: send message, read journal, access Drive files, query memory. Clients see a properly formatted MCP tool manifest so they understand what the employee can do and how to call it. The underlying Sistava infrastructure handles auth, rate limiting, and execution.

Multiple clients can connect to the same MCP endpoint simultaneously. A developer in Cursor and a product manager in Claude Desktop can both use the same AI employee as an MCP tool without interference. Each session is isolated and the employee maintains context per session.

## MCP as the Standard for Agent Interoperability

MCP is rapidly becoming the lingua franca for AI agent integration. Claude, the AI powering many leading tools, natively supports MCP. IDE-based AI tools like Cursor and Windsurf have adopted it as their primary integration mechanism. By supporting MCP natively, Sistava ensures your AI employees can participate in this ecosystem without custom integration work.

For organizations building internal AI tooling, the MCP endpoint means Sistava employees can serve as specialized tools within a larger AI architecture. A coding assistant in Cursor can call your knowledge management AI employee via MCP to retrieve company-specific context, combining the strengths of both systems in a single workflow.

## How It Works

**Your AI employee is exposed as a Model Context Protocol server, making it callable by any MCP-compatible client or orchestration layer.**

Enabling the MCP server turns your AI agent into a structured tool that other systems can call using the Model Context Protocol. The agent advertises its capabilities as MCP tools, and any compatible client, whether another AI system, a developer IDE, or an internal orchestrator, can invoke them directly. You configure which capabilities are exposed and set the access credentials.

This is the integration path for engineering teams that already run MCP-compatible infrastructure. Your Sistava employee becomes a node in a larger system, receiving structured calls and returning structured results. You can chain it with other MCP servers, expose specialized agents as dedicated tools, or plug your AI workforce into an AI development workflow where Claude, Cursor, or another client orchestrates the work.

## Use Cases

### Developer exposes AI employee as a tool for Claude

The AI agent's capabilities become callable tools in Claude or any MCP-compatible client, extending what the model can do with real business logic.

### Platform team builds internal AI tool registry

Each AI employee is exposed as an MCP server. Internal models and agents discover and call them through a standard protocol.

### Product team connects AI employees to AI coding assistants

Cursor, Windsurf, or any MCP-aware IDE can call the AI employee directly, letting developers invoke business workflows from their editor.

### Enterprise integrates AI workforce into existing AI infrastructure

The AI employee registers as an MCP endpoint in the company's AI hub, making its capabilities available to all connected models and agents.

## Comparison

| Before | After |
|---|---|
| AI employees are siloed from other AI tools and models. | Any MCP-compatible client can call the AI employee as a native tool. |
| Integrating business logic into LLM workflows requires custom wrappers. | The MCP endpoint exposes business capabilities in a standard protocol. |
| Each integration needs a bespoke connector built from scratch. | One MCP endpoint makes the AI agent discoverable by any MCP client. |
| Developers switch context between their IDE and the AI platform. | The AI employee is callable directly from the coding environment. |

## FAQ

### Which MCP clients are supported?

Any client that implements the Model Context Protocol specification is supported. This includes Claude Desktop, Cursor, Windsurf, and any tool that follows the MCP standard. New clients are automatically compatible as long as they implement the spec.

### Does the MCP endpoint expose the full employee or just a subset of capabilities?

The MCP server exposes the employee's core capabilities: send message, read recent activity, access Drive, and query memory. The full employee brain, including all skills and tools, is available through the send message capability. Granular per-capability exposure can be configured per endpoint.

### How is the MCP endpoint secured?

Each MCP endpoint has its own access token. Clients must present this token to connect. Tokens can be rotated or revoked at any time from the employee settings page. All traffic to MCP endpoints is encrypted in transit.

### Can I expose multiple employees as separate MCP servers?

Yes. Each AI employee can have its own independent MCP endpoint. From the client side, each employee appears as a distinct MCP server with its own tool manifest. You can add as many as needed to your MCP client configuration.

### Can I connect my AI agent to Claude or other MCP-compatible clients?

Yes, each AI employee can be exposed as an MCP server, making it callable from Claude, Cursor, and any other MCP-compatible client. Other agents and tools can invoke your employee's capabilities through a standard interface.

> We exposed our Sista agent as an MCP server and connected it to our internal AI orchestrator. It now receives tasks from three different systems without any manual routing.
>
> Jordan K., Platform Engineer · SaaS company

## Where Let Other AI Clients Use Your Employee fits

Let Other AI Clients Use Your Employee is part of How you plug them into your systems.

Build on your AI workforce with a full REST API, client SDKs, and webhooks in both directions. Expose any employee as an MCP server for Claude, Cursor, and Windsurf, or as an A2A agent for cross-platform agent collaboration. Everything the UI can do, the API can do.

- [How you plug them into your systems](/en/features/developer): Programmatic access to your workforce.

## Read the guide

- [Guide: Let Other AI Clients Use Your Employee](/en/guide/channels/mcp-server)

## More in Developer API

- [REST API](/en/features/developer/channel_api): Every AI employee is reachable over a REST API: send a prompt to POST /api/v1/chat with the employee's ID and get their response back as JSON, running through the same execution pipeline as web chat with full access to that employee's skills, tools, and memory. Requests are synchronous, blocking for up to five minutes, and authenticate with a Bearer API key shared across every programmatic channel (REST API, MCP Server, A2A, Webhooks, Email). The channel is off by default and must be enabled from Settings > Technical > Channels before it accepts requests.
- [Webhook Inbound](/en/features/developer/channel_webhook): Send a POST request to a single endpoint whenever something happens in one of your other systems, and your AI employee picks it up and acts on it in the background. You get a 202 response immediately; there is no waiting for a reply. The same employee that answers you in chat can react to a failed deploy, a new order, or a support ticket the moment it fires.
- [Let Other AI Agents Work with Your Employee](/en/features/developer/channel_a2a): Turn your workforce into an agent that other AI agents can call directly. Any client that speaks Google's A2A protocol can discover your employees, hand them a task, and get the result back, all without touching your web UI or writing REST API glue code.
- [Build with the Sistava SDK](/en/features/developer/channel_sdk): A planned client library for JavaScript, Python, and other runtimes that wraps the existing employee REST API. It is on the roadmap, not published: today you call employees from code through the REST API guide directly, and the SDK would only make that same integration point faster to reach.
- [Outbound Webhooks](/en/features/developer/outbound_webhooks): Connect a webhook URL, your own backend, a Zapier or Make scenario, a Slack incoming webhook, anything that accepts an HTTP POST, and your employee gets a tool it can call on its own during a task. It sends a JSON payload to that URL, so the rest of your stack finds out the moment the work is done instead of you checking manually. Because the call sends data outside Sistava, each send waits for your approval by default, the same as an outbound email.

## Explore

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