n8n-mcp
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Ani Björkström
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4 days ago
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Pricing
Pay per usage
Pricing
Pay per usage
Rating
0.0
(0)
Developer
Ani Björkström
Actor stats
1
Bookmarked
2
Total users
1
Monthly active users
4 days ago
Last modified
Categories
Share
You can access the n8n-mcp programmatically from your own applications by using the Apify API. You can also choose the language preference from below. To use the Apify API, you’ll need an Apify account and your API token, found in Integrations settings in Apify Console.
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Used for automated QA testing. Leave disabled for normal operation.", "default": false }, "apifyToken": { "title": "Apify API Token", "type": "string", "description": "Your Apify API token. Leave empty when running on Apify platform (auto-detected). Only needed for external deployments." }, "defaultActors": { "title": "Pre-loaded Actors", "type": "array", "description": "Actors to load on startup for faster first execution. Format: username/actor-name", "default": [], "items": { "type": "string" } }, "enableDynamicDiscovery": { "title": "Auto-discover Actors", "type": "boolean", "description": "Automatically discover and load Actors when first requested via MCP. Recommended to leave enabled.", "default": true }, "maxConcurrentRuns": { "title": "Max Parallel Executions", "minimum": 1, "maximum": 20, "type": "integer", "description": "Maximum number of Actors that can run simultaneously across all MCP sessions.", "default": 5 }, "defaultTimeout": { "title": "Default Timeout (seconds)", "minimum": 30, "maximum": 3600, "type": "integer", "description": "How long to wait for Actor execution before timing out. Can be overridden per MCP tool call.", "default": 300 }, "maxSessions": { "title": "Max MCP Sessions", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Maximum number of concurrent MCP client sessions. Each n8n workflow or AI client gets one session.", "default": 100 }, "sessionTimeout": { "title": "Session Timeout (seconds)", "minimum": 60, "maximum": 86400, "type": "integer", "description": "How long an MCP session stays active without activity before being cleaned up.", "default": 3600 }, "webhookSecret": { "title": "Webhook Secret", "type": "string", "description": "Optional secret to protect your MCP endpoint. If set, all requests must include X-Webhook-Secret header." }, "enableLogging": { "title": "Debug Logging", "type": "boolean", "description": "Enable verbose logging for troubleshooting MCP protocol issues. Not recommended for production.", "default": false } } }, "runsResponseSchema": { "type": "object", "properties": { "data": { "type": "object", "properties": { "id": { "type": "string" }, "actId": { "type": "string" }, "userId": { "type": "string" }, "startedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "finishedAt": { "type": "string", "format": "date-time", "example": "2025-01-08T00:00:00.000Z" }, "status": { "type": "string", "example": "READY" }, "meta": { "type": "object", "properties": { "origin": { "type": "string", "example": "API" }, "userAgent": { "type": "string" } } }, "stats": { "type": "object", "properties": { "inputBodyLen": { "type": "integer", "example": 2000 }, "rebootCount": { "type": "integer", "example": 0 }, "restartCount": { "type": "integer", "example": 0 }, "resurrectCount": { "type": "integer", "example": 0 }, "computeUnits": { "type": "integer", "example": 0 } } }, "options": { "type": "object", "properties": { "build": { "type": "string", "example": "latest" }, "timeoutSecs": { "type": "integer", "example": 300 }, "memoryMbytes": { "type": "integer", "example": 1024 }, "diskMbytes": { "type": "integer", "example": 2048 } } }, "buildId": { "type": "string" }, "defaultKeyValueStoreId": { "type": "string" }, "defaultDatasetId": { "type": "string" }, "defaultRequestQueueId": { "type": "string" }, "buildNumber": { "type": "string", "example": "1.0.0" }, "containerUrl": { "type": "string" }, "usage": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "integer", "example": 1 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } }, "usageTotalUsd": { "type": "number", "example": 0.00005 }, "usageUsd": { "type": "object", "properties": { "ACTOR_COMPUTE_UNITS": { "type": "integer", "example": 0 }, "DATASET_READS": { "type": "integer", "example": 0 }, "DATASET_WRITES": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_READS": { "type": "integer", "example": 0 }, "KEY_VALUE_STORE_WRITES": { "type": "number", "example": 0.00005 }, "KEY_VALUE_STORE_LISTS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_READS": { "type": "integer", "example": 0 }, "REQUEST_QUEUE_WRITES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_INTERNAL_GBYTES": { "type": "integer", "example": 0 }, "DATA_TRANSFER_EXTERNAL_GBYTES": { "type": "integer", "example": 0 }, "PROXY_RESIDENTIAL_TRANSFER_GBYTES": { "type": "integer", "example": 0 }, "PROXY_SERPS": { "type": "integer", "example": 0 } } } } } } } } }}OpenAPI is a standard for designing and describing RESTful APIs, allowing developers to define API structure, endpoints, and data formats in a machine-readable way. It simplifies API development, integration, and documentation.
OpenAPI is effective when used with AI agents and GPTs by standardizing how these systems interact with various APIs, for reliable integrations and efficient communication.
By defining machine-readable API specifications, OpenAPI allows AI models like GPTs to understand and use varied data sources, improving accuracy. This accelerates development, reduces errors, and provides context-aware responses, making OpenAPI a core component for AI applications.
You can download the OpenAPI definitions for n8n-mcp from the options below:
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