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openapi-diff-mcp-server

openapi-diff-mcp-server

Local
github.com/DrSatyrVersion Control0Updated 82w ago

About openapi-diff-mcp-server

openapi-diff-mcp-server provides an MCP interface for integrating the openapi-diff tool into AI workflows that compare OpenAPI specifications. Its README documents building the Java JAR and configuring it in Claude Desktop.

Key features

  1. 01MCP interface for openapi-diff
  2. 02Comparison of OpenAPI specifications
  3. 03Buildable as a Java JAR

Use cases

  1. 01Compare OpenAPI specifications through an AI integration
  2. 02Configure the server for Claude Desktop

MCP support

It provides an MCP server interface for AI integration with openapi-diff, a tool for comparing OpenAPI specifications.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

openapi-diff-mcp-server

MCP server interface for AI integration with openapi-diff tool for comparing OpenAPI specifications.

Features

  • TBD

Usage

Using MCP Server with Cloude Desktop
  1. Build MCP server Jar using:
mvn clean install
  1. To use the MCP server with Cloude Desktop, you need to configure it using a claude_desktop_config.json file. Below is an example configuration:
{
  "mcpServers": {
    "openapi-diff-mcp-server": {
      "command": "java",
      "args": [
        "-jar",
        "-Dmcp.transport=stdio",
        "/path/to/jar/openapi-diff-mcp-server-0.0.1-SNAPSHOT.jar"
      ]
    }
  }
}

Building and Running

mvn clean install
java -jar target/openapi-diff-mcp-server-<VERSION>.jar

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.