MCP Servers

The Model Context Protocol (MCP) is an open standard that lets AI models call tools hosted on external servers — turning any API, database, or internal system into a capability that an AI Agent can use autonomously. If you are an administrator looking to extend your agents beyond the 27 built-in tools, MCP Servers let you connect to virtually any external service without writing custom code inside Turing ES.

An MCP Server extends the capabilities of an AI Agent by connecting it to any external server that implements the Model Context Protocol (MCP). This allows Turing ES agents to use tools defined completely outside the platform — a company-internal knowledge system, a proprietary data API, a database query interface, or any of the growing ecosystem of public MCP servers.

MCP Servers are configured in Administration → MCP Servers and then selected per AI Agent.

Configuration Form

Field	Description
Name	Display name for the MCP server — shown when selecting it in an AI Agent
Transport type	`HTTP` or `stdio` — how Turing ES communicates with the server
Command	For `stdio`: the executable to launch the MCP server process (e.g., `npx`)
Arguments	For `stdio`: command-line arguments passed to the process (e.g., `@modelcontextprotocol/server-filesystem /data`)
URL	For `HTTP`: the MCP server endpoint URL
Execution mode	`Synchronous` or `Asynchronous`

Transport Types

stdio

Launches the MCP server as a local subprocess. The MCP client communicates with it via standard input/output streams. This is the standard mode for locally installed MCP servers.

# Example: filesystem MCP server
npx @modelcontextprotocol/server-filesystem /path/to/allowed/directory

Use stdio when the MCP server is installed locally (via npm, pip, or a system binary) and does not expose an HTTP endpoint.

HTTP

Connects to a remote MCP server over HTTP. This mode supports both public MCP services and internally hosted servers accessible over the network.

https://mcp.example.com/api/tools

Use HTTP when the MCP server is a hosted service or when it runs on a different machine or container.

Execution Modes

Synchronous

Waits for the MCP tool result before continuing the agent's reasoning chain. The LLM receives the tool result and can incorporate it into its next reasoning step before deciding whether to call another tool or generate a final response.

Use synchronous mode when the tool result is needed as context for subsequent steps.

Asynchronous

Dispatches the tool call without blocking. Useful for long-running operations or when the result does not need to be incorporated into the immediate reasoning chain.

MCP Server Examples

Internal ticketing system (stdio):

Name:      JIRA Internal
Transport: stdio
Command:   node
Arguments: /opt/mcp-jira/index.js --token <TOKEN>
Mode:      Synchronous

Company knowledge API (HTTP):

Name:      Internal Knowledge Base API
Transport: HTTP
URL:       https://internal-api.example.com/mcp
Mode:      Synchronous

Public MCP service (HTTP):

Name:      Brave Search
Transport: HTTP
URL:       https://api.search.brave.com/mcp
Mode:      Asynchronous

Assigning MCP Servers to Agents

Once configured, MCP servers appear in the MCP Servers multi-select field of the AI Agent configuration form. Each agent independently selects which MCP servers it can use. The tools exposed by each selected MCP server become available to the LLM during that agent's conversations.

Page	Description
AI Agents	Compose agents with native tools + MCP servers
Tool Calling	The 27 native tools built into Turing ES
Chat	Front-end chat interface where agents are used

Configuration Form​

Transport Types​

stdio​

HTTP​

Execution Modes​

Synchronous​

Asynchronous​

MCP Server Examples​

Assigning MCP Servers to Agents​

Related Pages​