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File hash verification for AI agents with hashlookup CIRCL API

David AshbyDavid Ashby
277 views
2/3/2026
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Complete MCP server exposing 11 hashlookup CIRCL API operations to AI agents.

⚑ Quick Setup

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  1. Import this workflow into your n8n instance
  2. Credentials Add hashlookup CIRCL API credentials
  3. Activate the workflow to start your MCP server
  4. Copy the webhook URL from the MCP trigger node
  5. Connect AI agents using the MCP URL

πŸ”§ How it Works

This workflow converts the hashlookup CIRCL API into an MCP-compatible interface for AI agents.

β€’ MCP Trigger: Serves as your server endpoint for AI agent requests β€’ HTTP Request Nodes: Handle API calls to https://hashlookup.circl.lu β€’ AI Expressions: Automatically populate parameters via $fromAI() placeholders β€’ Native Integration: Returns responses directly to the AI agent

πŸ“‹ Available Operations (11 total)

πŸ”§ Bulk (2 endpoints)

β€’ POST /bulk/md5: Bulk Search MD5 Hashes β€’ POST /bulk/sha1: Bulk Search SHA1 Hashes

πŸ”§ Children (1 endpoints)

β€’ GET /children/{sha1}/{count}/{cursor}: Return children from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

πŸ”§ Info (1 endpoints)

β€’ GET /info: Get Database Info

πŸ”§ Lookup (3 endpoints)

β€’ GET /lookup/md5/{md5}: Lookup MD5. β€’ GET /lookup/sha1/{sha1}: Lookup SHA-1. β€’ GET /lookup/sha256/{sha256}: Lookup SHA-256.

πŸ”§ Parents (1 endpoints)

β€’ GET /parents/{sha1}/{count}/{cursor}: Return parents from a given SHA1. A number of element to return and an offset must be given. If not set it will be the 100 first elements. A cursor must be given to paginate over. The starting cursor is 0.

πŸ”§ Session (2 endpoints)

β€’ GET /session/create/{name}: Create a session key to keep search context. The session is attached to a name. After the session is created, the header hashlookup_session can be set to the session name. β€’ GET /session/get/{name}: Return set of matching and non-matching hashes from a session.

πŸ”§ Stats (1 endpoints)

β€’ GET /stats/top: Get Top Queries

πŸ€– AI Integration

Parameter Handling: AI agents automatically provide values for: β€’ Path parameters and identifiers β€’ Query parameters and filters β€’ Request body data β€’ Headers and authentication

Response Format: Native hashlookup CIRCL API responses with full data structure

Error Handling: Built-in n8n HTTP request error management

πŸ’‘ Usage Examples

Connect this MCP server to any AI agent or workflow:

β€’ Claude Desktop: Add MCP server URL to configuration β€’ Cursor: Add MCP server SSE URL to configuration β€’ Custom AI Apps: Use MCP URL as tool endpoint β€’ API Integration: Direct HTTP calls to MCP endpoints

✨ Benefits

β€’ Zero Setup: No parameter mapping or configuration needed β€’ AI-Ready: Built-in $fromAI() expressions for all parameters β€’ Production Ready: Native n8n HTTP request handling and logging β€’ Extensible: Easily modify or add custom logic

> πŸ†“ Free for community use! Ready to deploy in under 2 minutes.

n8n Workflow: Basic MCP Server Trigger

This n8n workflow demonstrates a simple setup for receiving triggers from an MCP (Model Context Protocol) Server. It serves as a foundational template for building more complex AI agent interactions.

What it does

This workflow performs a single, fundamental action:

  1. Listens for MCP Triggers: It uses the "MCP Server Trigger" node to listen for incoming requests from an MCP server. When a request is received, the workflow is activated.

Prerequisites/Requirements

  • n8n Instance: An active n8n instance where this workflow can be imported and run.
  • MCP Server: An external Model Context Protocol (MCP) server or application that will send requests to this n8n workflow.

Setup/Usage

  1. Import the Workflow:
    • Download the workflow JSON provided.
    • In your n8n instance, go to "Workflows" and click "New".
    • Click the "Import from JSON" button and paste the workflow JSON.
    • Click "Import".
  2. Activate the Workflow:
    • Once imported, ensure the workflow is active by toggling the "Active" switch in the top right corner of the workflow editor.
  3. Configure MCP Server (External):
    • You will need to configure your external MCP server or application to send requests to the webhook URL provided by the "MCP Server Trigger" node.
    • To find the webhook URL, click on the "MCP Server Trigger" node in the workflow editor. The URL will be displayed in the node's configuration panel.
  4. Test the Workflow:
    • Send a request from your MCP server to the webhook URL.
    • Observe the execution history in n8n to confirm that the workflow was triggered successfully.

This workflow is a starting point. You can expand it by adding more nodes after the "MCP Server Trigger" to process the incoming data, interact with other services, or perform complex AI-related tasks.

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