Tensorify MCP Server for IDEs

The Tensorify Platform MCP Server gives AI agents full access to manage your Tensorify workspace — create workflows, deploy them, manage environment variables, and more. Use it from Cursor, Claude Desktop, or any MCP-compatible agent.

With the platform server, an AI agent can:

• Create and edit workflows using TSL (Tensorify Scripting Language)
• Deploy and undeploy workflows
• Execute workflows and check run results
• Manage environment variables
• Browse available plugins to understand what building blocks exist

Go to Settings → API Keys in the Tensorify dashboard. Create a key with these scopes:

• read:workflows — list and read workflows
• write:workflows — create, edit, deploy workflows
• run:workflows — execute workflows

Cursor

Add to .cursor/mcp.json:

{
  "mcpServers": {
    "tensorify": {
      "url": "https://mcp.tensorify.io/v1/platform",
      "headers": {
        "Authorization": "Bearer tfk_your_api_key"
      }
    }
  }
}

Claude Desktop

Add to your Claude Desktop config:

{
  "mcpServers": {
    "tensorify": {
      "url": "https://mcp.tensorify.io/v1/platform",
      "headers": {
        "Authorization": "Bearer tfk_your_api_key"
      }
    }
  }
}

CLI Helper

Run tensorify mcp config to auto-generate these snippets with your key.

After restarting your client, the agent has access to these tools:

| Tool | Description | |------|-------------| | read_workflow_tsl | Get current workflow as TSL code | | apply_tsl | Create/replace workflow from TSL code | | patch_tsl | Incrementally modify a workflow | | validate_tsl | Check TSL code for errors |

| Tool | Description | |------|-------------| | list_projects | List all projects | | create_project | Create a new project | | list_workflows | List workflows in a project | | get_workflow | Get workflow metadata | | create_workflow | Create a new workflow | | delete_workflow | Delete a workflow |

| Tool | Description | |------|-------------| | deploy_workflow | Deploy a workflow | | undeploy_workflow | Stop a deployment | | execute_workflow | Trigger and get result | | get_run | Get run details | | list_runs | List recent runs |

| Tool | Description | |------|-------------| | set_env_var | Set an environment variable | | list_env_vars | List variable names | | delete_env_var | Remove a variable | | list_plugins | Available plugins | | get_plugin_manifest | Plugin details for TSL authoring |

TSL is the language agents use to create workflows:

import WebhookTrigger from "@tensorify/webhook-trigger"
import HTTPRequest from "@tensorify/http-request"
import Return from "@tensorify/return"

trigger = WebhookTrigger {
  path: "/my-api"
  method: "POST"
  responseMode: "use-workflow-response"
}

fetch_data = HTTPRequest {
  url: "https://api.example.com/data"
  method: "GET"
}

response = Return {
  body: "{{ fetch_data.response.body }}"
  status: 200
}

trigger.payload -> fetch_data.input
fetch_data.response -> response.input

• API keys are scoped — give agents only the permissions they need
• Keys can be revoked instantly from the dashboard
• All operations are scoped to your teamspace
• The MCP server validates permissions on every request

"Create a workflow that receives a webhook, fetches data from our API, transforms it, and sends a Slack notification"

The agent will:

1. Call list_plugins to see available plugins
2. Call get_plugin_manifest for the relevant plugins
3. Call create_workflow to make a new workflow
4. Call apply_tsl with the generated TSL code
5. Optionally deploy_workflow when ready

You can configure both the platform server and trigger server:

{
  "mcpServers": {
    "tensorify": {
      "url": "https://mcp.tensorify.io/v1/platform",
      "headers": { "Authorization": "Bearer tfk_..." }
    },
    "tensorify-tools": {
      "url": "https://mcp.tensorify.io/v1/triggers",
      "headers": { "Authorization": "Bearer tfk_..." }
    }
  }
}

This gives the agent both the ability to manage your workspace and call your deployed workflow tools.