Use Langflow as an MCP client

Langflow integrates with the Model Context Protocol (MCP) as both an MCP server and an MCP client.

This page describes how to use Langflow as an MCP client with the MCP Tools component and connected MCP servers.

For information about using Langflow as an MCP server, see Use Langflow as an MCP server.

Use the MCP tools component

The MCP Tools component connects to an MCP server so that a Langflow agent can use the server's tools when responding to user queries.

This component has two modes, depending on the type of server you want to access:

• Connect to a non-Langflow MCP server with a JSON configuration file, server start command, or HTTP/SSE URL to access tools provided by external, non-Langflow MCP servers.
• Connect to a Langflow MCP server to use flows from your Langflow projects as MCP tools.

Connect to a non-Langflow MCP server

tip

uvx is included with uv in the Langflow package.

To use npx server commands, you must first install an LTS release of Node.js. If you run Langflow in Docker, install Node.js inside the container image and rebuild so that npx-based MCP servers are available at runtime. For more information, see Package management.

For an example of an npx MCP server in Langflow, see Connect an Astra DB MCP server to Langflow.

1. Add an MCP Tools component to your flow.

2. In the MCP Server field, select a previously connected server or click Add MCP Server.

   There are multiple ways to add a new server:

   • JSON: Paste the MCP server's JSON configuration object into the field, including required and optional parameters that you want to use, and then click Add Server.
   • STDIO: Enter the MCP server's Name, Command, and any Arguments and Environment Variables the server uses, and then click Add Server. For example, to start a Fetch server, the Command is uvx mcp-server-fetch.
   • HTTP/SSE: Enter your MCP server's Name, URL, and any Headers and Environment Variables the server uses, and then click Add Server. The default URL for Langflow MCP servers is http://localhost:7860/api/v1/mcp/project/PROJECT_ID/streamable or http://localhost:7860/api/v1/mcp/streamable. For more information, see Connect to a Langflow MCP server.

3. To configure headers for your MCP server, enter each header in the Headers fields as key-value pairs. You can use global variables in header values by entering the global variable name as the header value. For more information, see Use global variables in MCP server headers.

4. To use environment variables in your server command, enter each variable in the Env fields as key-value pairs.

5. In the Tool field, select a tool that you want this component to use, or leave the field blank to allow access to all tools provided by the MCP server.

   If you select a specific tool, you might need to configure additional tool-specific fields. For information about tool-specific fields, see your MCP server's documentation.

   At this point, the MCP Tools component is serving a tool from the connected server, but nothing is using the tool. The next steps explain how to make the tool available to an Agent component so that the agent can use the tool in its responses.

6. In the component's header menu, enable Tool mode so you can use the component with an agent.

7. Connect the MCP Tools component's Toolset port to an Agent component's Tools port.

   If not already present in your flow, make sure you also attach Chat Input and Chat Output components to the Agent component.

   

8. Test your flow to make sure the MCP server is connected and the selected tool is used by the agent. Open the Playground, and then enter a prompt that uses the tool you connected through the MCP Tools component.

   For example, if you use mcp-server-fetch with the fetch tool, you could ask the agent to summarize recent tech news. The agent calls the MCP server function fetch, and then returns the response.

9. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.

Connect a Langflow MCP server

Every Langflow project runs a separate MCP server that exposes the project's flows as MCP tools. For more information about your projects' MCP servers, including exposing flows as MCP tools, see Use Langflow as an MCP server.

Langflow MCP servers support both the streamable HTTP transport and Server-Sent Events (SSE) as a fallback.

To leverage flows-as-tools, use the MCP Tools component to connect to a project's MCP endpoint:

1. Add an MCP Tools component to your flow, click Add MCP Server, and then select HTTP/SSE mode.

2. In the MCP URL field, enter your Langflow server's MCP endpoint.

   • For project-specific servers: http://localhost:7860/api/v1/mcp/project/PROJECT_ID/streamable
   • For global MCP server: http://localhost:7860/api/v1/mcp/streamable
   • Default for Langflow Desktop: http://localhost:7868/

   All flows available from the targeted server are treated as tools.

3. In the component's header menu, enable Tool Mode so you can use the component with an agent.

4. Connect the MCP Tools component's Toolset port to an Agent component's Tools port.

5. If not already present in your flow, make sure you also attach Chat Input and Chat Output components to the Agent component.

   

6. Test your flow to make sure the agent uses your flows to respond to queries. Open the Playground, and then enter a prompt that uses a flow that you connected through the MCP Tools component.

7. If you want the agent to be able to use more tools, repeat these steps to add more tools components with different servers or tools.

MCP Tools parameters

Name	Type	Description
mcp_server	String	Input parameter. The MCP server to connect to. Select from previously configured servers or add a new one.
tool	String	Input parameter. The specific tool to execute from the connected MCP server. Leave blank to allow access to all tools.
use_cache	Boolean	Input parameter. Enable caching of MCP server and tools to improve performance. Default: false.
verify_ssl	Boolean	Input parameter. Enable SSL certificate verification for HTTPS connections. Default: true.
response	DataFrame	Output parameter. DataFrame containing the response from the executed tool.

Manage connected MCP servers

To manage all MCP server connections for your Langflow client, click MCP servers in the visual editor, or click your profile icon, select Settings, and then click MCP Servers.

To add a new MCP server, click Add MCP Server, and then follow the steps in Use the MCP Tools component to configure the connection and use the server in a flow.

Click More to edit or delete an MCP server connection.

Modify MCP server environment variables with the API

You can modify MCP server environment variables at runtime when running flows through the Langflow API by tweaking the MCP Tools component.

You can include tweaks with any Langflow API request that supports the tweaks parameter, such as POST requests to the /run or /webhook endpoints. For more information, see Input schema (tweaks).

To modify the MCP Tools component's environment variables with tweaks, do the following:

1. Open the flow that contains your MCP Tools component.

2. To find the MCP Tools component's unique ID, in your MCP Tools component, click Controls. The component's ID is displayed in the Controls pane, such as MCPTools-Bzahc.

3. Send a POST request to the Langflow server's /run endpoint, and include tweaks to the MCP Tools component.

   The following examples demonstrate a request structure with the env object nested under mcp_server in the tweaks payload:

   Python

   
   

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   import requests

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   import os

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   LANGFLOW_SERVER_ADDRESS = "http://localhost:7860"

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   FLOW_ID = "your-flow-id"

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   LANGFLOW_API_KEY = os.getenv("LANGFLOW_API_KEY")

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   MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc"

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   url = f"{LANGFLOW_SERVER_ADDRESS}/api/v1/run/{FLOW_ID}?stream=false"

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   headers = {

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   "Content-Type": "application/json",

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   "x-api-key": LANGFLOW_API_KEY

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   }

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   payload = {

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   "output_type": "chat",

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   "input_type": "chat",

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   "input_value": "What sales data is available to me?",

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   "tweaks": {

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   MCP_TOOLS_COMPONENT_ID: {

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   "mcp_server": {

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   "env": {

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   "API_URL": "https://api.example.com",

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   "API_KEY": "your-mcp-server-api-key",

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   "ENVIRONMENT": "production"

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   }

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   }

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   }

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   }

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   }

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   response = requests.post(url, json=payload, headers=headers)

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   print(response.json())

   
   

   TypeScript

   
   

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   const LANGFLOW_SERVER_ADDRESS = "http://localhost:7860";

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   const FLOW_ID = "your-flow-id";

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   const LANGFLOW_API_KEY = process.env.LANGFLOW_API_KEY || "";

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   const MCP_TOOLS_COMPONENT_ID = "MCPTools-Bzahc";

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   const url = `${LANGFLOW_SERVER_ADDRESS}/api/v1/run/${FLOW_ID}?stream=false`;

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   const response = await fetch(url, {

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   method: "POST",

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   headers: {

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   "Content-Type": "application/json",

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   "x-api-key": LANGFLOW_API_KEY,

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   },

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   body: JSON.stringify({

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   output_type: "chat",

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   input_type: "chat",

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   input_value: "What sales data is available to me?",

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   tweaks: {

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   [MCP_TOOLS_COMPONENT_ID]: {

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   mcp_server: {

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   env: {

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   API_URL: "https://api.example.com",

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   API_KEY: "your-mcp-server-api-key",

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   ENVIRONMENT: "production",

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   },

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   },

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   },

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   },

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   }),

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   });

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   const data = await response.json();

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   console.log(data);

   
   

   cURL

   
   

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   curl --request POST \

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   --url "http://LANGFLOW_SERVER_ADDRESS/api/v1/run/FLOW_ID?stream=false" \

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   --header "Content-Type: application/json" \

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   --header "x-api-key: LANGFLOW_API_KEY" \

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   --data '{

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   "output_type": "chat",

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   "input_type": "chat",

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   "input_value": "What sales data is available to me?",

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   "tweaks": {

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   "MCP_TOOLS_COMPONENT_ID": {

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   "mcp_server": {

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   "env": {

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   "API_URL": "https://api.example.com",

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   "API_KEY": "your-mcp-server-api-key",

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   "ENVIRONMENT": "production"

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   }

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   }

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   }

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   }

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   }'

   
   

   Replace MCP_TOOLS_COMPONENT_ID, LANGFLOW_API_KEY, LANGFLOW_SERVER_ADDRESS, and FLOW_ID with the actual values from your Langflow deployment.

   Langflow doesn't automatically discover or expose which environment variables your MCP server accepts from the MCP Tools component. To determine which environment variables your MCP server accepts, see the MCP server's documentation. For example, the Astra DB MCP server requires ASTRA_DB_APPLICATION_TOKEN and ASTRA_DB_API_ENDPOINT, with an optional variable for ASTRA_DB_KEYSPACE, as documented in its repository.

Use global variables in MCP server headers

You can use global variables in MCP server header values to securely store and reference API keys, authentication tokens, and other sensitive values. This is particularly useful for deployment scenarios where you need to pass user-specific credentials at runtime.

Enter a global variable name as the header value, and Langflow resolves the global variable name to its actual value before making the MCP server request. Langflow only passes the token value to your server; it doesn't validate tokens on behalf of your MCP server.

For example, to create a global variable named TEST_BEARER_TOKEN for MCP server bearer authentication, do the following:

1. To open the Global Variables pane, click your profile icon, select Settings, and then click Global Variables.

2. Create a Credential global variable named TEST_BEARER_TOKEN.

3. In the Value field, enter your MCP server's bearer token value. The value must include the Bearer prefix with a space, for example: Bearer eyJhbG....

4. Click Save Variable.

5. To manage MCP server connections for your Langflow client, click MCP servers in the visual editor, or click your profile icon, select Settings, and then click MCP Servers.

6. Click Add MCP Server.

7. Select the following:

   • Name: test-mcp-server
   • Streamable HTTP/SSE URL: Your MCP server's URL, such as http://127.0.0.1:8000/mcp.
   • Headers: In the key field, enter the literal string Authorization. For the key's value, enter TEST_BEARER_TOKEN, or the exact name of your global variable.

8. Click Create Server.

   If the connection succeeds, Langflow shows the number of tools exposed by the server.

   After creating the server and global variable, you can connect to the server with the MCP Tools component, as explained in the next steps.

9. Add the MCP Tools component to a flow.

10. In the MCP Tools component, select the MCP Server you created. The MCP server configuration already includes the headers you configured earlier, so no further configuration is needed in the component. The global variable TEST_BEARER_TOKEN is automatically resolved when the component makes requests to the MCP server.

11. Optional: To override headers or add additional headers to the MCP Tools component, click the component to view the Headers parameter in the component inspection panel, and then add header key values. Headers configured in the component take precedence over the headers configured in the MCP server settings.

12. Test your flow to make sure the agent uses your server to respond to queries. Open the Playground, and then enter a prompt that uses a tool that you connected through the MCP Tools component.

    Langflow automatically resolves TEST_BEARER_TOKEN to its actual value before sending the request to the MCP server. When your MCP server receives the request, the Authorization header contains the resolved token value.

See also

• Use Langflow as an MCP server
• Use a DataStax Astra DB MCP server with the MCP Tools component