MCP management

Use AI → MCP to configure tools that the AI assistant and built-in MCP server can call on behalf of a user.

Access to AI is controlled by the global view:ai-page permission. Separate global read: and edit: permissions control MCP server, tool, and collection configuration. For details, refer to the role model.

Overview

The AI assistant displays the following tool types:

• internal — Built-in platform tools described in the MCP server documentation.
• external — Tools retrieved from a connected upstream MCP server.
• custom — Custom MCP tools.

Tools of the external and custom types can be called only if they belong to an enabled MCP collection available to the user.

MCP servers

Connect an upstream MCP server to import its tools into the platform catalog.

1. Go to AI → MCP → MCP servers.
2. Select Connect.
3. On the General information tab, specify Name, Identifier, Description, Owner, and Team.
4. On the Configuration tab:
   1. Enable the Enabled toggle.
   2. Select a Transport: HTTP or SSE.
   3. Specify the upstream MCP server URL.
   4. If necessary, add HTTP headers and Credentials for authentication.
5. Select Save.

After saving, open the server card and select Synchronize to load the tool catalog. On the Tools tab, enable the required tools and add Tags if necessary.

MCP tools

Create a custom MCP tool that the AI assistant calls directly without an upstream MCP server.

1. Go to AI → MCP → MCP tools.
2. Select Add.
3. On the General information tab, specify Name, Description, Owner, Team, and Tags.
4. On the Configuration tab:
   1. Enable the Enabled toggle.
   2. Define the Argument schema as a JSON Schema with the root type object.
   3. If necessary, specify Path parameter mapping as a JSON object that maps argument names to {placeholder} segments in the executor URL.
   4. If necessary, specify Query parameter mapping as a JSON object that maps argument names to URL query parameter names.
5. On the Authorization tab, specify the executor endpoint URL, HTTP method, HTTP headers, and Credentials. The URL can contain {placeholder} segments for path parameters and credential placeholders such as {{ .credentials.tenant_id }}.
6. Select Save.

For example, assume the argument schema defines the space_id argument, the executor URL is https://api.example.com/v1/spaces/{spaceId}/boards, and the path parameter mapping is {"space_id": "spaceId"}. If the tool is called with space_id=42, the request is sent to https://api.example.com/v1/spaces/42/boards.

For GET and DELETE, arguments not mapped to path parameters are passed only as query parameters. For POST, PUT, and PATCH, arguments not mapped to path or query parameters are passed in the JSON request body.

MCP collections

An MCP collection groups catalog tools and determines which tools are available to AI assistant users and external MCP clients.

1. Go to AI → MCP → MCP collections.
2. Select Create.
3. On the General information tab, specify Name, Identifier, Description, Owner, and Team.
4. On the Configuration tab:
   1. Enable the Enabled toggle.
   2. Under Tools, select tools from the available catalog. Each item includes the MCP server identifier in parentheses.
5. Select Save.

To let a user call collection tools, open the collection card menu and select Configure access. Assign users or teams a role with the use:mcp-collections permission. For the permission list, refer to the role model.

The collection owner and super administrator automatically receive access to the collection.

Catalog

The Catalog section displays all tools available to the current user based on MCP collections and access permissions. This section is read-only. Edit tools on the MCP servers, MCP tools, and MCP collections pages.

Integration with the AI assistant and MCP server

Tools from MCP collections are used as follows:

• In the AI assistant, Available tools displays built-in tools and tools from available collections.
• The platform MCP server returns and calls the same collection tools available to the user based on their RBAC permissions.