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.
Expose as MCP Server
Expose any employee as an MCP server so Claude, Cursor, Windsurf, or any MCP client can use it as a tool.
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.
Use Cases
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.