Jez This workflow corresponds to n8n.io template #4547 — we link there as the canonical source.
This workflow follows the Agent → Execute Workflow Trigger recipe pattern — see all workflows that pair these two integrations.
The workflow JSON
Copy or download the full n8n JSON below. Paste it into a new n8n workflow, add your credentials, activate. Full import guide →
{
"id": "vqujIu65SFbZUz0n",
"meta": {
"templateCredsSetupCompleted": true
},
"name": "Context7 Smithery AI Agent MCP Server",
"tags": [],
"nodes": [
{
"id": "a42ae423-a348-460e-888c-f3df2b82021d",
"name": "Google Gemini Chat Model",
"type": "@n8n/n8n-nodes-langchain.lmChatGoogleGemini",
"position": [
440,
380
],
"parameters": {
"options": {},
"modelName": "models/gemini-2.5-flash-preview-05-20"
},
"credentials": {
"googlePalmApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "22f3e783-4310-4d15-a875-cbb400a82c1b",
"name": "context7-resolve-library-id",
"type": "n8n-nodes-mcp.mcpClientTool",
"position": [
1280,
480
],
"parameters": {
"toolName": "resolve-library-id",
"operation": "executeTool",
"connectionType": "http",
"toolParameters": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Tool_Parameters', `Generate a JSON object with the parameters for this tool.\nThe tool expects a JSON object with one **required** key:\n- \"libraryName\": (string) The common name of the software library or framework.\nExample: {\"libraryName\": \"react\"}`, 'json') }}",
"descriptionType": "manual",
"toolDescription": "Resolves a general library name (e.g., 'nextjs', 'mongodb') into a unique Context7-compatible library ID (e.g., '/vercel/next.js', '/mongodb/docs'). Use this tool first to get the correct ID before attempting to fetch its documentation."
},
"credentials": {
"mcpClientHttpApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "526eab1c-7a08-45eb-8169-7e8d4578c117",
"name": "context7-get-library-docs",
"type": "n8n-nodes-mcp.mcpClientTool",
"position": [
1840,
480
],
"parameters": {
"toolName": "get-library-docs",
"operation": "executeTool",
"connectionType": "http",
"toolParameters": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('Tool_Parameters', `Generate a JSON object with the parameters for this tool.\nThe tool expects a JSON object with the following keys:\n- \"context7CompatibleLibraryID\": (string, **required**) The exact Context7-compatible library ID (e.g., \"/vercel/next.js\").\n- \"topic\": (string, optional) A specific topic to focus the documentation on (e.g., \"routing\").\n- \"tokens\": (number, optional, default: 10000) Maximum number of tokens for the documentation. Minimum effective value is 10000.\nExample for required only: {\"context7CompatibleLibraryID\": \"/mongodb/docs\"}\nExample with optional: {\"context7CompatibleLibraryID\": \"/vercel/next.js\", \"topic\": \"authentication\", \"tokens\": 15000}`, 'json') }}",
"descriptionType": "manual",
"toolDescription": "Fetches documentation for a specified library using its Context7-compatible library ID. You can optionally focus on a specific topic within the documentation and set a maximum token limit for the returned content."
},
"credentials": {
"mcpClientHttpApi": {
"name": "<your credential>"
}
},
"typeVersion": 1
},
{
"id": "5cafb0d4-19ac-4589-88d8-34b88404e0f1",
"name": "Context7 MCP Server Trigger",
"type": "@n8n/n8n-nodes-langchain.mcpTrigger",
"position": [
120,
-860
],
"parameters": {
"path": "dafdfsdfsdf6e8-ea19-43b7-b337-a5iuodsf98vjfewf"
},
"typeVersion": 1
},
{
"id": "945d8818-c346-44ba-b4c1-56d166a4f381",
"name": "Context7 AI Agent",
"type": "@n8n/n8n-nodes-langchain.agent",
"position": [
880,
-20
],
"parameters": {
"text": "={{ $('Context7 Workflow Start').item.json.query }}",
"options": {
"systemMessage": "You are an AI assistant with access to Context7 tools to help users find software library documentation.\nYour primary functions are to:\n1. **Resolve Library IDs:** When a user asks for documentation for a library like 'Next.js' or 'MongoDB', first use the `resolve-library-id` tool to find its specific Context7-compatible ID (e.g., '/vercel/next.js'). The input for this tool is the `libraryName`.\n2. **Get Library Documentation:** Once you have the `context7CompatibleLibraryID` from the previous step, use the `get-library-docs` tool to fetch the documentation.\n * The required parameter is `context7CompatibleLibraryID`.\n * If the user specifies a particular `topic` (e.g., \"routing in Next.js\"), include this in the `topic` parameter.\n * You can also adjust the `tokens` parameter if a different length of documentation is implicitly or explicitly requested, keeping in mind the default and minimum is 10000.\n\nAlways use `resolve-library-id` before `get-library-docs` if you don't already have the exact Context7-compatible library ID. Be precise with the IDs."
},
"promptType": "define"
},
"typeVersion": 2
},
{
"id": "1b0cd3cc-8566-4cb5-932c-005149a2467b",
"name": "Context7 Workflow Start",
"type": "n8n-nodes-base.executeWorkflowTrigger",
"position": [
100,
-20
],
"parameters": {
"workflowInputs": {
"values": [
{
"name": "query"
}
]
}
},
"typeVersion": 1.1
},
{
"id": "e955aa60-2b80-45d5-885b-24b7fe56a426",
"name": "Simple Memory",
"type": "@n8n/n8n-nodes-langchain.memoryBufferWindow",
"position": [
700,
520
],
"parameters": {
"sessionKey": "={{ $execution.id }}",
"sessionIdType": "customKey"
},
"typeVersion": 1.3
},
{
"id": "4927c593-5762-4a26-87eb-8a7e0a4b3d32",
"name": "call_context7_ai_agent",
"type": "@n8n/n8n-nodes-langchain.toolWorkflow",
"position": [
280,
-460
],
"parameters": {
"workflowId": {
"__rl": true,
"mode": "list",
"value": "vqujIu65SFbZUz0n",
"cachedResultName": "Context7 Smithery AI Agent MCP Server"
},
"description": "=Processes a natural language query to find and retrieve software library documentation using Context7. It intelligently uses sub-tools to first resolve library names to Context7 IDs and then fetches the relevant documentation.",
"workflowInputs": {
"value": {
"query": "={{ /*n8n-auto-generated-fromAI-override*/ $fromAI('query', `The user's natural language question or request for software library documentation. For example: 'How do I implement authentication in Next.js?' or 'Get docs for MongoDB aggregation framework'.`, 'string') }}"
},
"schema": [
{
"id": "query",
"type": "string",
"display": true,
"removed": false,
"required": false,
"displayName": "query",
"defaultMatch": false,
"canBeUsedToMatch": true
}
],
"mappingMode": "defineBelow",
"matchingColumns": [],
"attemptToConvertTypes": false,
"convertFieldsToString": false
}
},
"typeVersion": 2.2
},
{
"id": "d72a2661-5622-4bfa-9d42-8c96b5fef877",
"name": "Sticky Note",
"type": "n8n-nodes-base.stickyNote",
"position": [
1700,
-40
],
"parameters": {
"width": 680,
"height": 320,
"content": "## MCP JSON for Roo Code etc\n\nTo use this in Roo Code, Cline etc use\n\n\"n8n-context7\": {\n \"url\": \"https://n8n.coolify.au/mcp/dafd56e8-ea19-43b7-b337-a5881c3bc541/sse\",\n \"alwaysAllow\": [\n \"call_context7_ai_agent\"\n ]\n }\n\nwhere the url is the the path that you get from the mcp server node in n8n"
},
"typeVersion": 1
},
{
"id": "c0dc815b-3d0f-49e2-8da0-08d2f45d0991",
"name": "Sticky Note1",
"type": "n8n-nodes-base.stickyNote",
"position": [
-260,
-1140
],
"parameters": {
"color": 4,
"width": 580,
"height": 420,
"content": "This node is the public entry point for the Context7 AI Agent service.\nIt exposes an MCP (Multi-Agent Collaboration Protocol) SSE (Server-Sent Events) endpoint.\n\n- Path: The long, random path (e.g., /dafdfsdfsdf... ) acts as a simple 'security by obscurity' measure.\n- Function: It listens for incoming HTTP POST requests to its SSE endpoint.\n- Tool Exposed: This trigger makes the 'call_context7_ai_agent' tool (defined by the connected Tool Workflow node) available to external MCP clients.\n- Input: Expects a JSON payload from the client, typically like: {\"input\": {\"query\": \"user's question\"}}\n- Output: Streams SSE events back to the client, containing the AI agent's responses and intermediate steps."
},
"typeVersion": 1
},
{
"id": "adc54a99-b04d-40be-a9a4-25f837453c14",
"name": "Sticky Note2",
"type": "n8n-nodes-base.stickyNote",
"position": [
-260,
-680
],
"parameters": {
"color": 4,
"width": 580,
"height": 340,
"content": "This node represents the tool that external clients call via the MCP Server Trigger.\nWhen triggered, it executes the separate \"Context7 Smithery AI Agent MCP Server\" sub-workflow (ID: vqujIu65SFbZUz0n).\n\n- Action: Invokes the AI agent sub-workflow to process the user's request.\n- Inputs to Sub-Workflow:\n - 'query': The user's natural language question, received from the external MCP client. This is passed directly to the sub-workflow.\n- Output: The result from the sub-workflow (the AI agent's final response or stream) is passed back through the MCP Server Trigger to the client."
},
"typeVersion": 1
},
{
"id": "5cc037d1-4155-49dc-86f5-7af9976149bf",
"name": "Sticky Note3",
"type": "n8n-nodes-base.stickyNote",
"position": [
1700,
-660
],
"parameters": {
"color": 2,
"width": 680,
"height": 600,
"content": "## Main MCP Server Workflow Overview\n\nThis workflow acts as the public gateway for the Context7 AI Documentation Agent.\n\n1. **`Context7 MCP Server Trigger`**:\n * Receives requests from external clients (e.g., Roo Code, Cline) at its unique SSE endpoint URL.\n * The path is intentionally long and random for basic access control.\n * Client JSON for Roo Code etc.:\n \"n8n-context7\": {\n \"url\": \"https://n8n.coolify.au/mcp/YOUR_LONG_RANDOM_PATH/sse\",\n \"alwaysAllow\": [\n \"call_context7_ai_agent\" // This is the tool name defined in the Tool Workflow node\n ]\n }\n (Replace YOUR_LONG_RANDOM_PATH with the actual path from the trigger node)\n\n2. **`call_context7_ai_agent` (Tool Workflow Node)**:\n * This is the \"tool\" the MCP Server Trigger exposes.\n * It calls the separate AI Agent Sub-Workflow (ID: vqujIu65SFbZUz0n), passing the user's `query`.\n\n**Data Flow:**\nExternal Client -> MCP Server Trigger -> call_context7_ai_agent (Tool) -> AI Agent Sub-Workflow -> (Response Stream) -> MCP Server Trigger -> External Client\n\n3. ** There is a video I've made on youtube that explains this workflow a bit and why I created it: https://youtu.be/dudvmyp7Pyg?si=N59DYTe2WE6GPlvf"
},
"typeVersion": 1
},
{
"id": "79bf63c1-b325-4a3c-950e-30df5aef15e4",
"name": "Sticky Note4",
"type": "n8n-nodes-base.stickyNote",
"position": [
-20,
-200
],
"parameters": {
"color": 5,
"width": 360,
"height": 200,
"content": "This is the entry point for the AI Agent sub-workflow.\n- Receives:\n - 'query': The user's natural language question from the main workflow.\n- Passes data to: The IF node (for API key check, if implemented) or directly to the AI Agent."
},
"typeVersion": 1
},
{
"id": "2486cacc-b06a-4e30-b363-456930cf0ea5",
"name": "Sticky Note5",
"type": "n8n-nodes-base.stickyNote",
"position": [
760,
-280
],
"parameters": {
"color": 5,
"width": 560,
"height": 440,
"content": "The core AI logic unit.\n- Model: Uses Google Gemini.\n- System Prompt: Instructs the AI on how to use the Context7 tools.\n- Tools: Has access to 'context7-resolve-library-id' and 'context7-get-library-docs'.\n- Memory: Connected to 'Simple Memory' using the current execution ID ({{ $execution.id }}) as the session key for conversation context.\n- Input: Receives the user's 'query'.\n- Output: Generates a response by orchestrating calls to its tools based on the query."
},
"typeVersion": 1
},
{
"id": "f5f12aa6-d90a-419c-9baa-2c7c451a71d1",
"name": "Sticky Note6",
"type": "n8n-nodes-base.stickyNote",
"position": [
580,
640
],
"parameters": {
"color": 6,
"width": 360,
"height": 200,
"content": "Provides conversational memory for the AI Agent.\n- Type: Window Buffer Memory (keeps a sliding window of recent messages).\n- Session Key: Uses `{{ $execution.id }}`. This ensures that memory is unique to each individual execution of this sub-workflow, preventing cross-talk between different user requests."
},
"typeVersion": 1
},
{
"id": "d2598e8e-3931-436f-a480-b4bef659bccb",
"name": "Sticky Note7",
"type": "n8n-nodes-base.stickyNote",
"position": [
1100,
640
],
"parameters": {
"color": 6,
"width": 480,
"height": 500,
"content": "This node allows the AI Agent to use the external 'resolve-library-id' Context7 MCP tool.\n\n- Connection: Uses the 'smithery_context7' credential to communicate with the Context7 MCP service.\n- Action: When invoked by the AI Agent, this node makes a call to the Context7 'resolve-library-id' tool.\n- Purpose of External Tool: To convert a common library name (e.g., \"react\") into a Context7-specific ID (e.g., \"/facebook/react\").\n- AI Agent Interaction:\n - The AI Agent's system prompt guides it to use this tool first to get a library ID.\n - The \"Tool Parameters\" for this node are configured to use an AI-generated JSON object. The AI is prompted with: \"Generate a JSON object with the parameters for this tool. The tool expects a JSON object with one **required** key: - \"libraryName\": (string) The common name of the software library or framework. Example: {\\\"libraryName\\\": \\\"react\\\"}\"\n - The AI Agent will construct this JSON (e.g., `{\"libraryName\": \"nextjs\"}`) and pass it to this node.\n- Output: Returns the Context7-compatible library ID to the AI Agent."
},
"typeVersion": 1
},
{
"id": "7bebd852-75e5-427e-a3ee-781cd85f20e6",
"name": "Sticky Note8",
"type": "n8n-nodes-base.stickyNote",
"position": [
1620,
640
],
"parameters": {
"color": 6,
"width": 520,
"height": 500,
"content": "This node allows the AI Agent to use the external 'get-library-docs' Context7 MCP tool.\n\n- Connection: Uses the 'smithery_context7' credential to communicate with the Context7 MCP service.\n- Action: When invoked by the AI Agent, this node makes a call to the Context7 'get-library-docs' tool.\n- Purpose of External Tool: To fetch documentation for a library, given its Context7-specific ID.\n- AI Agent Interaction:\n - The AI Agent's system prompt guides it to use this tool after obtaining a `context7CompatibleLibraryID` (usually from the 'resolve-library-id' tool).\n - The \"Tool Parameters\" for this node are configured to use an AI-generated JSON object. The AI is prompted with details about expected keys like `context7CompatibleLibraryID` (required), `topic` (optional), and `tokens` (optional).\n - Example JSON the AI might construct: `{\"context7CompatibleLibraryID\": \"/vercel/next.js\", \"topic\": \"routing\"}`.\n- Output: Returns the fetched library documentation (or relevant snippets) to the AI Agent."
},
"typeVersion": 1
},
{
"id": "f4749f9a-6f9e-45c5-b6a9-a89d4dc77dec",
"name": "Sticky Note9",
"type": "n8n-nodes-base.stickyNote",
"position": [
500,
-1380
],
"parameters": {
"color": 7,
"width": 1140,
"height": 1040,
"content": "## n8n AI Agent as an MCP Service (Context7 Docs Example)\n\n**What This Workflow Does:**\nThis n8n setup transforms a complex AI agent into a single, callable tool exposed over an MCP (Multi-Agent Collaboration Protocol) SSE (Server-Sent Events) endpoint. It allows external clients (like development environments, other AI agents, or custom applications) to leverage the power of an n8n-hosted AI agent with a simple natural language query.\n\n**How It Works:**\n1. **`Context7 MCP Server Trigger` (This Workflow):** Creates a public SSE endpoint. Its path is kept long and random for basic security by obscurity. This trigger exposes the `call_context7_ai_agent` tool.\n2. **`call_context7_ai_agent` (Tool Workflow Node, This Workflow):** This node is the \"tool\" offered by the MCP server. When called by a client, it doesn't do the AI work itself; instead, it invokes a separate sub-workflow.\n3. **AI Agent Sub-Workflow (e.g., \"Context7 Smithery AI Agent MCP Server\" - ID: vqujIu65SFbZUz0n):** This is where the magic happens!\n * It receives the `query` from the main workflow.\n * A `Context7 AI Agent` node, powered by a model like Google Gemini, orchestrates the task.\n * The agent uses connected tools:\n * `context7-resolve-library-id`: To find the correct ID for a software library.\n * `context7-get-library-docs`: To fetch documentation for that library.\n * It uses `Simple Memory` (scoped by execution ID) to maintain conversational context if needed for multi-turn interactions (though this example is primarily single-shot).\n\n**Why This Pattern is Useful & The Smithery Connection:**\n\n* **Simplifies Client-Side Logic:** Clients (like **Smithery's Roo code**, VS Code extensions, or other AI systems) don't need to manage complex multi-step reasoning, tool selection, or API calls to different services. They just call *one tool* (`call_context7_ai_agent`) with a plain language request (e.g., \"What is flexbox in Tailwind CSS?\").\n* **Reduces Client-Side Token Usage:** All the detailed system prompts, tool descriptions for the sub-tools (resolve-id, get-docs), and the actual execution of these tools happen *within n8n*. The client only sends a short query and receives the processed result. This is incredibly efficient, especially if the client is another LLM.\n* **Centralized Agent Capabilities:** You can update, enhance, or swap out models and tools within your n8n AI agent sub-workflow without needing to update any of the client applications that consume it.\n* **Ideal for Agentic Systems:** This pattern is a cornerstone of building robust **agentic systems**. By encapsulating specialized AI capabilities within n8n and exposing them as callable MCP tools:\n * You can create a \"team\" of specialized n8n agents.\n * A primary agent (in Smithery, or another n8n workflow) can delegate tasks to these specialized n8n agent-tools.\n * It promotes modularity, reusability, and scalability in AI application development.\n* **Leverage n8n's Ecosystem:** Build your agent with n8n's visual interface, manage credentials securely, and easily integrate with hundreds of other services if your agent needs them.\n\n**Client Configuration Example (for Roo Code/Cline):**\nTo connect to this n8n agent from a client like Roo code, you'd configure it with the MCP server details:\n```json\n\"n8n-context7-agent\": { // A descriptive name for your client\n \"url\": \"https://YOUR_N8N_INSTANCE/mcp/YOUR_LONG_RANDOM_PATH/sse\", // Path from MCP Server Trigger\n \"alwaysAllow\": [\n \"call_context7_ai_agent\" // Name of the Tool Workflow node\n ]\n}"
},
"typeVersion": 1
}
],
"active": true,
"settings": {
"executionOrder": "v1"
},
"versionId": "785e4ebf-bb80-48e3-872c-40d1b9a8d14b",
"connections": {
"Simple Memory": {
"ai_memory": [
[
{
"node": "Context7 AI Agent",
"type": "ai_memory",
"index": 0
}
]
]
},
"call_context7_ai_agent": {
"ai_tool": [
[
{
"node": "Context7 MCP Server Trigger",
"type": "ai_tool",
"index": 0
}
]
]
},
"Context7 Workflow Start": {
"main": [
[
{
"node": "Context7 AI Agent",
"type": "main",
"index": 0
}
]
]
},
"Google Gemini Chat Model": {
"ai_languageModel": [
[
{
"node": "Context7 AI Agent",
"type": "ai_languageModel",
"index": 0
}
]
]
},
"context7-get-library-docs": {
"ai_tool": [
[
{
"node": "Context7 AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
},
"context7-resolve-library-id": {
"ai_tool": [
[
{
"node": "Context7 AI Agent",
"type": "ai_tool",
"index": 0
}
]
]
}
}
}
Credentials you'll need
Each integration node will prompt for credentials when you import. We strip credential IDs before publishing — you'll add your own.
googlePalmApimcpClientHttpApi
Pro
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About this workflow
This n8n workflow template uses community nodes and is only compatible with the self-hosted version of n8n.
Source: https://n8n.io/workflows/4547/ — original creator credit. Request a take-down →
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