Hashing MCP Server

What does “Hashing” MCP Server do?

The Hashing MCP Server is a Model Context Protocol (MCP) server designed for calculating cryptographic hashes, specifically MD5 and SHA-256. By exposing these hashing functionalities as tools, it enables AI assistants and LLM-based development environments (such as Claude Desktop and VSCode) to process cryptographic requests efficiently. The server streamlines tasks that require quick hash generation, which is essential for verifying data integrity, securing content, and supporting workflows that involve cryptographic operations. Its integration with MCP clients allows developers and AI agents to seamlessly request and receive hash computations, simplifying workflows that depend on cryptography.

List of Prompts

No information found regarding prompt templates in the repository.

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List of Resources

No explicit MCP resource definitions found in the repository.

List of Tools

• calculate_md5: Computes the MD5 hash of a given text input, enabling quick verification and fingerprinting of content.
• calculate_sha256: Computes the SHA-256 hash of a given text input, useful for more secure hash requirements in modern cryptographic workflows.

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Use Cases of this MCP Server

• File Integrity Verification: Developers can quickly generate and compare hashes to verify file integrity or detect tampering.
• Password Hashing Demonstrations: Useful for educational purposes, demonstrating how plain-text passwords are transformed into hashes.
• Data Deduplication: Identify duplicate files or content by comparing their hashes directly within supported AI-enhanced development environments.
• API Response Validation: Automate the process of hashing and validating API payloads or data exchanges in development workflows.

How to set it up

Windsurf

1. Ensure Docker is installed and running, or Python 3.13+ with venv/uv is available.
2. Open your Windsurf configuration file.
3. Add the Hashing MCP server to the mcpServers section:

   {
     "mcpServers": {
       "hashing-mcp-server": {
         "command": "docker",
         "args": ["run", "-p", "7090:7090", "kunalpathak13/hashing-mcp-server:latest"]
       }
     }
   }
   

4. Save the configuration and restart Windsurf.
5. Verify the server is running by checking the MCP client integrations.

Claude

1. Install Docker or Python 3.13+.
2. Open the Claude Desktop or supported client’s configuration.
3. Add the Hashing MCP server:

   {
     "mcpServers": {
       "hashing-mcp-server": {
         "command": "docker",
         "args": ["run", "-p", "7090:7090", "kunalpathak13/hashing-mcp-server:latest"]
       }
     }
   }
   

4. Restart Claude Desktop.
5. Confirm that the hashing tools appear in the AI assistant interface.

Cursor

1. Prerequisite: Python 3.13+ or Docker installed.
2. Access the Cursor configuration file.
3. Insert the MCP server block:

   {
     "mcpServers": {
       "hashing-mcp-server": {
         "command": "docker",
         "args": ["run", "-p", "7090:7090", "kunalpathak13/hashing-mcp-server:latest"]
       }
     }
   }
   

4. Save and restart Cursor.
5. Test hash computations within Cursor.

Cline

1. Make sure Docker or Python 3.13+ is available.
2. Edit Cline’s MCP server configuration.
3. Register the Hashing MCP server:

   {
     "mcpServers": {
       "hashing-mcp-server": {
         "command": "docker",
         "args": ["run", "-p", "7090:7090", "kunalpathak13/hashing-mcp-server:latest"]
       }
     }
   }
   

4. Restart Cline.
5. Check for the server’s availability in Cline’s tool list.

Securing API Keys Example:
While this server does not require API keys, if you add one, use:

{
  "env": {
    "HASHING_MCP_API_KEY": "your_api_key_here"
  },
  "inputs": {}
}

Replace HASHING_MCP_API_KEY with your variable as required.

How to use this MCP inside flows

Using MCP in FlowHunt

To integrate MCP servers into your FlowHunt workflow, start by adding the MCP component to your flow and connecting it to your AI agent:

Click on the MCP component to open the configuration panel. In the system MCP configuration section, insert your MCP server details using this JSON format:

{
  "hashing-mcp-server": {
    "transport": "streamable_http",
    "url": "https://yourmcpserver.example/pathtothemcp/url"
  }
}

Once configured, the AI agent is now able to use this MCP as a tool with access to all its functions and capabilities. Remember to change “hashing-mcp-server” to your actual MCP server name and replace the URL.

Overview

Our opinion

This MCP server is very focused and simple, providing basic but valuable crypto-hashing tools. Its documentation is clear, but lacks advanced features like prompt templates or explicit MCP resources. The lack of stars and forks suggests it’s early-stage, but the presence of a license and Docker/PyPI support is good. Score: 5/10 for clarity and utility, but limited scope and adoption.

MCP Score