From c76aeb6e9e46a67ff21648e2c385b0215197bdff Mon Sep 17 00:00:00 2001
From: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
Date: Mon, 31 Mar 2025 15:19:09 -0400
Subject: [PATCH] docs: mcp server component and integrations (#7286)
* mcp-server-component-update
* update-image
* step-number
* more-content
* astra-npx
* mcp-see-mode-and-env-var
* fix-build
* docs-add-mcp-inspector
* create-section-for-mcp
* code-review
---
docs/docs/Components/components-tools.md | 81 +++++---
.../docs/Integrations/MCP/integrations-mcp.md | 180 ++++++++++++++++++
.../Integrations/MCP/mcp-component-astra.md | 40 ++++
docs/docs/Integrations/integrations-mcp.md | 76 --------
docs/sidebars.js | 21 +-
docs/static/img/mcp-server-component-sse.png | Bin 0 -> 178493 bytes
docs/static/img/mcp-server-component.png | Bin 0 -> 383706 bytes
docs/static/img/mcp-stdio-component.png | Bin 391381 -> 0 bytes
8 files changed, 284 insertions(+), 114 deletions(-)
create mode 100644 docs/docs/Integrations/MCP/integrations-mcp.md
create mode 100644 docs/docs/Integrations/MCP/mcp-component-astra.md
delete mode 100644 docs/docs/Integrations/integrations-mcp.md
create mode 100644 docs/static/img/mcp-server-component-sse.png
create mode 100644 docs/static/img/mcp-server-component.png
delete mode 100644 docs/static/img/mcp-stdio-component.png
diff --git a/docs/docs/Components/components-tools.md b/docs/docs/Components/components-tools.md
index f60482ea1..7b6d37231 100644
--- a/docs/docs/Components/components-tools.md
+++ b/docs/docs/Components/components-tools.md
@@ -3,6 +3,8 @@ title: Tools
slug: /components-tools
---
+import Icon from "@site/src/components/icon";
+
# Tool components in Langflow
Tools are typically connected to agent components at the **Tools** port. Agents use LLMs as a reasoning engine to decide which of the connected tool components to use to solve a problem.
@@ -261,25 +263,56 @@ This component allows you to call the Serper.dev Google Search API.
| results | List[Data]| List of search results |
| tool | Tool | Google Serper search tool for use in LangChain|
-## MCP Tools (stdio)
-This component connects to a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) server over `stdio` and exposes its tools as Langflow tools to be used by an Agent component.
+## MCP server
-To use the MCP stdio component, follow these steps:
+This component connects to a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) server and exposes the MCP server's tools as tools.
-1. Add the MCP stdio component to your workflow, and connect it to an agent. The flow looks like this:
+In addition to being an MCP client that can leverage MCP servers, Langflow is also an MCP server that exposes flows as tools through the `/api/v1/mcp/sse` API endpoint. For more information, see [MCP integrations](/integrations-mcp).
-
+To use the MCP server component with an agent component, follow these steps:
-2. In the MCP stdio component, in the **mcp command** field, enter the command to start your MCP server. For a [Fetch](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch) server, the command is:
+1. Add the MCP server component to your workflow.
+2. In the MCP server component, in the **MCP Command** field, enter the command to start your MCP server. For example, to start a [Fetch](https://github.com/modelcontextprotocol/servers/tree/main/src/fetch) server, the command is:
```bash
uvx mcp-server-fetch
```
-3. Open the **Playground**.
+`uvx` is included with `uv` in the Langflow package.
+To use `npx` server commands, you must first install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
+For an example of starting `npx` MCP servers, see [Connect an Astra DB MCP server to Langflow](/mcp-component-astra).
+
+3. Click to get the server's list of **Tools**.
+4. In the **Tool** field, select the server tool you want the component to use.
+The available fields change based on the selected tool.
+For information on the parameters, see the MCP server's documentation.
+5. In the MCP server component, enable **Tool mode**.
+Connect the MCP server component's **Toolset** port to an **Agent** component's **Tools** port.
+
+The flow looks similar to this:
+
+
+6. Open the **Playground**.
Ask the agent to summarize recent tech news. The agent calls the MCP server function `fetch` and returns the summary.
-This confirms the MCP server is connected and working.
+This confirms the MCP server is connected, and its tools are being used in Langflow.
+
+For more information, see [MCP integrations](/integrations-mcp).
+
+### MCP Server-Sent Events (SSE) mode
+
+1. In the **MCP Server** component, select **SSE**.
+A default address appears in the **MCP SSE URL** field.
+2. In the **MCP SSE URL** field, modify the default address to point at the SSE endpoint of the Langflow server you're currently running.
+The default value is `http://localhost:7860/api/v1/mcp/sse`.
+3. In the **MCP Server** component, click to retrieve the server's list of **Tools**.
+4. Click the **Tools** field.
+All of your flows are listed as tools.
+5. Enable **Tool Mode**, and then connect the **MCP Server** component to an agent component's tool port.
+The flow looks like this:
+
+6. Open the **Playground** and chat with your tool.
+The agent chooses the correct tool based on your query.
### Inputs
@@ -293,28 +326,18 @@ This confirms the MCP server is connected and working.
|-------|-----------|-------------------------------------------|
| tools | List[Tool]| List of tools exposed by the MCP server |
+## MCP Tools (stdio)
+:::important
+This component is deprecated as of Langflow version 1.3.
+Instead, use the [MCP server component](/components-tools#mcp-server)
+:::
+
+
## MCP Tools (SSE)
-
-This component connects to a [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction) server over [SSE (Server-Sent Events)](https://developer.mozilla.org/en-US/docs/Web/API/Server-sent_events/Using_server-sent_events) and exposes its tools as Langflow tools to be used by an Agent component.
-
-To use the MCP SSE component, follow these steps:
-
-1. Add the MCP SSE component to your workflow, and connect it to an agent. The flow looks similar to the MCP stdio component flow.
-
-2. In the MCP SSE component, in the **url** field, enter the URL of your current Langflow server's `mcp/sse` endpoint.
-This will fetch all currently available tools from the Langflow server.
-
-### Inputs
-
-| Name | Type | Description |
-|------|--------|------------------------------------------------------|
-| url | String | SSE URL (default: `http://localhost:7860/api/v1/mcp/sse`) |
-
-### Outputs
-
-| Name | Type | Description |
-|-------|-----------|-------------------------------------------|
-| tools | List[Tool]| List of tools exposed by the MCP server |
+:::important
+This component is deprecated as of Langflow version 1.3.
+Instead, use the [MCP server component](/components-tools#mcp-server)
+:::
## Python Code Structured Tool
diff --git a/docs/docs/Integrations/MCP/integrations-mcp.md b/docs/docs/Integrations/MCP/integrations-mcp.md
new file mode 100644
index 000000000..16e1230bd
--- /dev/null
+++ b/docs/docs/Integrations/MCP/integrations-mcp.md
@@ -0,0 +1,180 @@
+---
+title: Integrate Langflow with MCP
+slug: /integrations-mcp
+---
+
+import Tabs from '@theme/Tabs';
+import TabItem from '@theme/TabItem';
+
+Langflow integrates with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction). This allows you to use your Langflow flows as tools in client applications that support the MCP, or extend Langflow with the [MCP server component](/components-tools#mcp-tools-stdio) to access MCP servers.
+
+You can use Langflow as an MCP server with any [MCP client](https://modelcontextprotocol.io/clients).
+
+For configuring interactions between Langflow flows and MCP tools, see [Name and describe your flows for agentic use](#name-and-describe-your-flows-for-agentic-use).
+
+To connect [MCP Inspector](https://modelcontextprotocol.io/docs/tools/inspector) to Langflow for testing and debugging flows, see [Install MCP Inspector to test and debug flows](#install-mcp-inspector-to-test-and-debug-flows)
+
+## Access all of your flows as tools
+
+:::important
+Tool names must contain only letters, numbers, underscores, and dashes.
+Tool names cannot contain spaces.
+To re-name flows in the Langflow UI, click **Flow Name** > **Edit Details**.
+:::
+
+Connect an MCP client to Langflow to use your flows as tools.
+
+1. Install [Cursor](https://docs.cursor.com/) or [Claude for Desktop](https://claude.ai/download).
+2. Install [uv](https://docs.astral.sh/uv/getting-started/installation/) to run `uvx` commands. `uvx` is included with `uv` in the Langflow package.
+3. Optional: Install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm) to run `npx` commands.
+For an example `npx` server, see [Connect an Astra DB MCP server to Langflow](/mcp-component-astra).
+4. Create at least one flow, and note your host. For example, `http://127.0.0.1:7860`.
+
+
+
+
+In Cursor, you can configure a Langflow server in the same way as other MCP servers.
+For more information, see the [Cursor MCP documentation](https://docs.cursor.com/context/model-context-protocol).
+
+1. Open Cursor, and then go to **Cursor Settings**.
+2. Click MCP, and then click **Add New Global MCP Server**.
+Cursor's MCP servers are listed as JSON objects.
+3. To add a Langflow server, add an entry for your Langflow server's `/v1/mcp/sse` endpoint.
+This example assumes the default Langflow server address of `http://127.0.0.1:7860`.
+```json
+{
+ "mcpServers": {
+ "langflow": {
+ "url": "http://127.0.0.1:7860/api/v1/mcp/sse"
+ }
+ }
+}
+```
+4. Save the `mcp.json` file, and then click the **Reload** icon.
+5. Your Langflow server is now available to Cursor as an MCP server, and all of its flows are registered as tools.
+You can now use your flows as tools in Cursor.
+Cursor determines when to use tools based on your queries, and will request permissions when necessary.
+
+
+
+
+
+In Claude for Desktop, you can configure a Langflow server in the same way as other MCP servers.
+For more information, see the [Claude for Desktop MCP documentation](https://modelcontextprotocol.io/quickstart/user).
+
+1. Open Claude for Desktop, and then go to the program settings.
+For example, on the MacOS menu bar, click **Claude**, and then select **Settings**.
+2. In the **Settings** dialog, click **Developer**, and then click **Edit Config**.
+This creates a `claude_desktop_config.json` file if you don't already have one.
+3. Add the following code to `claude_desktop_config.json`.
+Your args may differ for your `uvx` and `Python` installations. To find the correct paths:
+
+ * For `uvx`: Run `which uvx` in your terminal
+ * For Python: Run `which python` in your terminal
+
+Replace `PATH/TO/PYTHON` with the Python path from your system.
+This command assumes the default Langflow server address of `http://127.0.0.1:7860`.
+
+```json
+{
+ "mcpServers": {
+ "langflow": {
+ "command": "/bin/sh",
+ "args": ["-c", "uvx --python PATH/TO/PYTHON mcp-sse-shim@latest"],
+ "env": {
+ "MCP_HOST": "http://127.0.0.1:7860",
+ "DEBUG": "true"
+ }
+ }
+ }
+}
+```
+
+This code adds a new MCP server called `langflow` and starts the [mcp-sse-shim](https://github.com/phact/mcp-sse-shim) package using the specified Python interpreter and uvx.
+
+4. Restart Claude for Desktop.
+Your new tools are available in your chat window. Click the tools icon to see a list of your flows.
+
+You can now use your flows as tools in Claude for Desktop.
+
+Claude determines when to use tools based on your queries, and will request permissions when necessary.
+
+
+
+
+
+## Name and describe your flows for agentic use
+
+MCP clients like Claude for Desktop and Cursor "see" Langflow as a single MCP server, with **all** of your flows listed as tools.
+
+This can confuse agents, who don't know that flow `adbbf8c7-0a34-493b-90ea-5e8b42f78b66` is a Document Q&A flow for a specific text file.
+To prevent this behavior, name and describe your flows clearly for agentic use. Imagine your names and descriptions as function names and code comments, with a clear statement of what problem they solve.
+
+For example, you have created a [Document Q&A](/tutorials-document-qa) flow that loads a sample resume for an LLM to chat with, and you want Cursor to use the tool.
+
+1. Click **Flow name**, and then select **Edit Details**.
+2. The **Name** field should make it clear what the flow does, both to a user and to the agent. For example, name it `Document QA for Resume`.
+3. The **Description** field should include a description of what the flow does. For example, describe the flow as `OpenAI LLM Chat with Alex's resume.`
+The **Endpoint Name** field does not affect the agent's behavior.
+4. To see how an MCP client understands your flow, in Cursor, examine the **MCP Servers**.
+The tool is listed as:
+```text
+document_qa_for_resume
+e967f47d-6783-4bab-b1ea-0aaa554194a3: OpenAI LLM Chat with Alex's resume.
+```
+Your flow name and description provided the agent with a clear purpose for the tool.
+
+5. Ask Cursor a question specifically about the resume, such as `What job experience does Alex have?`
+```text
+I'll help you explore a resume using the Document QA for Resume flow, which is specifically designed for analyzing resumes.
+Let me call this tool.
+```
+6. Click **Run tool** to continue. The agent requests permissions when necessary.
+```
+Based on the resume, here's a comprehensive breakdown of the experience:
+```
+7. Ask about a different resume.
+You've provided enough information in the description for the agent to make the correct decision:
+```text
+I notice you're asking about Emily's job experience.
+Based on the available tools, I can see there is a Document QA for Resume flow that's designed for analyzing resumes.
+However, the description mentions it's for "Alex's resume" not Emily's. I don't have access to Emily's resume or job experience information.
+```
+
+## Install MCP Inspector to test and debug flows
+
+[MCP inspector](https://modelcontextprotocol.io/docs/tools/inspector) is the standard tool for testing and debugging MCP servers.
+
+Use MCP Inspector to monitor your Langflow server's flows, and understand how they are being consumed by the MCP.
+
+To install and run MCP inspector, follow these steps:
+
+1. Install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
+2. To install and start MCP inspector, in a terminal window, run the following command:
+```
+npx @modelcontextprotocol/inspector
+```
+
+MCP inspector starts by default at `http://localhost:5173`.
+
+:::tip
+Optionally, specify a proxy port when starting MCP Inspector:
+```
+SERVER_PORT=9000 npx -y @modelcontextprotocol/inspector
+```
+:::
+
+3. In the browser, navigate to MCP Inspector.
+4. To inspect the Langflow server, enter the values for the Langflow server.
+
+* In the **Transport Type** field, select **SSE**.
+* In the **URL** field, enter the Langflow server's `/mcp/sse` endpoint.
+For a default deployment, the URL is `http://127.0.0.1:7860/api/v1/mcp/sse`.
+
+5. Click **Connect**.
+MCP Inspector connects to the Langflow server.
+6. To confirm the connection, click the **Tools** tab.
+The Langflow server's flows are listed as tools, which confirms MCP Inspector is connected.
+In the **Tools** tab, you can monitor how your flows are being registered as tools by MCP, and run flows with input values.
+
+To quit MCP Inspector, in the terminal where it's running, enter `Ctrl+C`.
diff --git a/docs/docs/Integrations/MCP/mcp-component-astra.md b/docs/docs/Integrations/MCP/mcp-component-astra.md
new file mode 100644
index 000000000..58e2bd1d5
--- /dev/null
+++ b/docs/docs/Integrations/MCP/mcp-component-astra.md
@@ -0,0 +1,40 @@
+---
+title: Connect an Astra DB MCP server to Langflow
+slug: /mcp-component-astra
+---
+
+Use the [MCP server component](/components-tools#mcp-server) to connect Langflow to a [Datastax Astra DB MCP server](https://github.com/datastax/astra-db-mcp).
+
+1. Install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
+2. Create an [OpenAI](https://platform.openai.com/) API key.
+3. Create an [Astra DB Serverless (Vector) database](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html#create-vector-database), if you don't already have one.
+4. Get your database's **Astra DB API endpoint** and an **Astra DB application token** with the Database Administrator role. For more information, see [Generate an application token for a database](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#database-token).
+5. Create a [Simple agent starter project](/starter-projects-simple-agent).
+6. Remove the **URL** tool and replace it with an [MCP server](/components-tools#mcp-server) component.
+The flow should look like this:
+
+7. In the **MCP server** component, in the **MCP command** field, add the following code.
+Replace the values for `ASTRA_TOKEN` and `ASTRA_ENDPOINT` with the values from your Astra database.
+
+```plain
+env ASTRA_DB_APPLICATION_TOKEN=ASTRA_TOKEN ASTRA_DB_API_ENDPOINT=ASTRA_ENDPOINT npx -y @datastax/astra-db-mcpnpx -y @datastax/astra-db-mcp
+```
+
+:::important
+Langflow passes environment variables from the `.env` file to MCP, but not global variables declared in the UI.
+To add the values for `ASTRA_DB_APPLICATION_TOKEN` and `ASTRA_DB_API_ENDPOINT` as global variables, add them to Langflow's `.env` file at startup.
+For more information, see [global variables](/configuration-global-variables).
+:::
+
+8. In the **Agent** component, add your **OpenAI API key**.
+9. Open the **Playground**, and then ask the agent, `What collections are available?`
+
+Since Langflow is connected to your Astra DB database through the MCP, the agent chooses the correct tool and connects to your database to retrieve the answer.
+```text
+The available collections in your database are:
+collection_002
+hardware_requirements
+load_collection
+nvidia_collection
+software_requirements
+```
\ No newline at end of file
diff --git a/docs/docs/Integrations/integrations-mcp.md b/docs/docs/Integrations/integrations-mcp.md
deleted file mode 100644
index 20f0fb93f..000000000
--- a/docs/docs/Integrations/integrations-mcp.md
+++ /dev/null
@@ -1,76 +0,0 @@
----
-title: Integrate Langflow with MCP (Model context protocol)
-slug: /integrations-mcp
----
-
-Langflow integrates with the [Model Context Protocol (MCP)](https://modelcontextprotocol.io/introduction). This allows you to use your Langflow flows as tools in other applications that support the MCP, or extend Langflow with the [MCP stdio component](/components-tools#mcp-tools-stdio) to access MCP servers.
-
-You can use Langflow as an MCP server with any [MCP client](https://modelcontextprotocol.io/clients).
-For example purposes, this guide presents two ways to interact with the MCP:
-
-* [Access all of your flows as tools from Claude for Desktop](#access-all-of-your-flows-as-tools-from-claude-for-desktop)
-* [Use the MCP stdio component to connect Langflow to a Datastax Astra DB MCP server](#connect-an-astra-db-mcp-server-to-langflow)
-
-## Access all of your flows as tools from Claude for Desktop
-
-1. Install [Claude for Desktop](https://claude.ai/download).
-2. Install [uv](https://docs.astral.sh/uv/getting-started/installation/) so that you can run `uvx` commands.
-3. Create at least one flow, and note your host. For example, `http://127.0.0.1:7863`.
-4. Open Claude for Desktop, and then go to the program settings.
-For example, on the MacOS menu bar, click **Claude**, and then select **Settings**.
-5. In the **Settings** dialog, click **Developer**, and then click **Edit Config**.
-This creates a `claude_desktop_config.json` file if you don't already have one.
-6. Add the following code to `claude_desktop_config.json`.
-Your args may differ for your `uvx` and `Python` installations. To find the correct paths:
-
- * For `uvx`: Run `which uvx` in your terminal
- * For Python: Run `which python` in your terminal
-
-Replace `/path/to/uvx` and `/path/to/python` with the paths from your system:
-
-```json
-{
- "mcpServers": {
- "langflow": {
- "command": "/bin/sh",
- "args": ["-c", "/path/to/uvx --python /path/to/python mcp-sse-shim@latest"],
- "env": {
- "MCP_HOST": "http://127.0.0.1:7864",
- "DEBUG": "true"
- }
- }
- }
-}
-```
-
-This code adds a new MCP server called `langflow` and starts the [mcp-sse-shim](https://github.com/phact/mcp-sse-shim) package using the specified Python interpreter and uvx.
-
-7. Restart Claude for Desktop.
-Your new tools are available in your chat window. Click the tools icon to see a list of your flows.
-
-You can now use your flows as tools in Claude for Desktop.
-Claude determines when to use tools based on your queries, and will request permissions when necessary.
-
-## Connect an Astra DB MCP server to Langflow
-
-Use the [MCP stdio component](/components-tools#mcp-tools-stdio) to connect Langflow to a [Datastax Astra DB MCP server](https://github.com/datastax/astra-db-mcp).
-
-1. Install an LTS release of [Node.js](https://docs.npmjs.com/downloading-and-installing-node-js-and-npm).
-2. Create an [OpenAI](https://platform.openai.com/) API key.
-3. Create an [Astra DB Serverless (Vector) database](https://docs.datastax.com/en/astra-db-serverless/databases/create-database.html#create-vector-database), if you don't already have one.
-4. Get your database's **Astra DB API endpoint** and an **Astra DB application token** with the Database Administrator role. For more information, see [Generate an application token for a database](https://docs.datastax.com/en/astra-db-serverless/administration/manage-application-tokens.html#database-token).
-5. Add your **Astra DB application token** and **Astra API endpoint** to Langflow as [global variables](/configuration-global-variables).
-6. Create a [Simple agent starter project](/starter-projects-simple-agent).
-7. Remove the **URL** tool and replace it with an [MCP stdio component](/components-tools#mcp-tools-stdio) component.
-The flow should look like this:
-
-8. In the **MCP stdio** component, in the **MCP command** field, add the following code:
-
-```plain
-npx -y @datastax/astra-db-mcp
-```
-
-9. In the **Agent** component, add your **OpenAI API key**.
-10. Open the **Playground**.
-Langflow is now connected to your Astra DB database through the MCP.
-You can use the MCP to create, read, update, and delete data from your database.
diff --git a/docs/sidebars.js b/docs/sidebars.js
index 09637c7f2..4e98f9ea8 100644
--- a/docs/sidebars.js
+++ b/docs/sidebars.js
@@ -165,15 +165,6 @@ module.exports = {
"Integrations/Arize/integrations-arize",
"Integrations/integrations-assemblyai",
"Integrations/Composio/integrations-composio",
- "Integrations/integrations-langfuse",
- "Integrations/integrations-langsmith",
- "Integrations/integrations-langwatch",
- "Integrations/integrations-opik",
- {
- type: "doc",
- id: "Integrations/integrations-mcp",
- label: "MCP (Model context protocol)"
- },
{
type: 'category',
label: 'Google',
@@ -182,6 +173,18 @@ module.exports = {
'Integrations/Google/integrations-setup-google-cloud-vertex-ai-langflow',
],
},
+ "Integrations/integrations-langfuse",
+ "Integrations/integrations-langsmith",
+ "Integrations/integrations-langwatch",
+ {
+ type: 'category',
+ label: 'MCP (Model Context Protocol)',
+ items: [
+ 'Integrations/MCP/integrations-mcp',
+ 'Integrations/MCP/mcp-component-astra',
+ ],
+ },
+ "Integrations/integrations-opik",
{
type: "category",
label: "Notion",
diff --git a/docs/static/img/mcp-server-component-sse.png b/docs/static/img/mcp-server-component-sse.png
new file mode 100644
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