Swagger/OpenAPI▌
by amrsa1
Integrate Swagger/OpenAPI with your REST API to explore endpoints, fetch docs, and execute authenticated requests easily
Integrates with REST APIs through OpenAPI specifications to fetch documentation, explore endpoints, execute authenticated requests, and validate responses with support for multiple authentication methods.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / API developers testing endpoints during development
- / QA engineers validating API behavior
- / Integration developers exploring third-party APIs
- / Backend developers documenting API interactions
capabilities
- / Fetch OpenAPI/Swagger documentation from URLs
- / Test API endpoints with authentication
- / Explore API schemas and data structures
- / Execute authenticated HTTP requests
- / Validate API responses against schemas
- / Auto-discover documentation URLs
what it does
Loads OpenAPI/Swagger documentation from any URL and lets you test API endpoints directly through the MCP interface. Supports multiple authentication methods and automatically detects IDE configurations.
about
Swagger/OpenAPI is a community-built MCP server published by amrsa1 that provides AI assistants with tools and capabilities via the Model Context Protocol. Integrate Swagger/OpenAPI with your REST API to explore endpoints, fetch docs, and execute authenticated requests easily It is categorized under developer tools.
how to install
You can install Swagger/OpenAPI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
license
MIT
Swagger/OpenAPI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Swagger MCP Server
A Model Context Protocol (MCP) server that provides tools for exploring and testing APIs through Swagger/OpenAPI documentation. This server automatically detects configuration files from multiple IDEs and provides comprehensive API interaction capabilities.
Features
- 🔍 Fetch and parse Swagger/OpenAPI documentation from any URL
- 🧪 Test API endpoints directly through the MCP interface
- 📊 Explore API schemas and understand data structures
- 🔧 Multi-IDE support - automatically detects config from VS Code, Cursor, Windsurf, and more
- 🌐 Flexible authentication - supports API keys, basic auth, and bearer tokens
- ⚡ Auto-discovery - can find documentation URLs automatically
Configuration
IDE Setup
Create an MCP configuration file in your IDE's configuration directory:
- VS Code:
~/.vscode/mcp.jsonor.vscode/mcp.json(in your project) - Cursor:
~/.cursor/mcp.jsonor.cursor/mcp.json(in your project) - Windsurf:
~/.windsurf/mcp.jsonor.windsurf/mcp.json(in your project) - Any IDE:
mcp.json(in your project root) or.mcp/config.json
Authentication Options
Option 1: Using API Key
"swagger-mcp": {
"command": "npx",
"args": [
"-y",
"swagger-mcp@latest"
],
"env": {
"API_BASE_URL": "https://api.example.com",
"API_DOCS_URL": "https://api.example.com/swagger.json",
"API_KEY": "your-api-key-here"
}
}
Option 2: Using Username and Password
"swagger-mcp": {
"command": "npx",
"args": [
"-y",
"swagger-mcp@latest"
],
"env": {
"API_BASE_URL": "https://api.example.com",
"API_DOCS_URL": "https://api.example.com/swagger.json",
"API_USERNAME": "your-username",
"API_PASSWORD": "your-password"
}
}
Configuration Options
API_BASE_URL- Base URL for your API (e.g.,https://api.example.com) [Required]API_DOCS_URL- Direct URL to Swagger/OpenAPI JSON/YAML (optional, will be auto-discovered)API_KEY- API key for authentication (used as Bearer token)API_USERNAME- Username for basic authenticationAPI_PASSWORD- Password for basic authentication
Authentication Flow
The server intelligently handles authentication:
- For API requests: Uses API_KEY as Bearer token, falls back to Basic auth
- For authentication endpoints: Auto-injects username/password credentials
- Token management: Automatically stores and reuses tokens from login responses
- Auto-refresh: Attempts to refresh tokens on 401 Unauthorized responses
Available Tools
fetch_swagger_info
Fetches and parses Swagger/OpenAPI documentation from a given URL to discover available API endpoints.
list_endpoints
Lists all available API endpoints after fetching Swagger documentation, showing methods, paths, and summaries.
get_endpoint_details
Gets detailed information about a specific API endpoint including parameters, request/response schemas, and examples.
execute_api_request
Executes an API request to a specific endpoint with authentication, parameters, headers, and body handling.
validate_api_response
Validates an API response against the schema definitions from Swagger documentation to ensure compliance.
Usage Examples
Once configured, you can use the MCP server in your AI-powered editor to:
- Explore APIs: "Show me the available endpoints in this API"
- Test endpoints: "Test the POST /users endpoint with this data"
- Understand schemas: "Explain the User model structure"
- Debug API calls: "Help me troubleshoot this API request"
- Validate responses: "Check if this response matches the API schema"
Supported IDEs
The server automatically detects configuration files from:
- VS Code (
.vscode/mcp.json) - Cursor (
.cursor/mcp.json) - Windsurf (
.windsurf/mcp.json) - Root directory (
mcp.json) - Alternative location (
.mcp/config.json)
Development
# Clone the repository
git clone https://github.com/amrsa1/SwaggerMCP.git
cd SwaggerMCP
# Install dependencies
npm install
# Run in development mode
npm run dev
# Build for production
npm run build
License
MIT License - see LICENSE file for details.
Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
FAQ
- What is the Swagger/OpenAPI MCP server?
- Swagger/OpenAPI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Swagger/OpenAPI?
- This profile displays 64 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★64 reviews- ★★★★★Arjun Khanna· Dec 24, 2024
I recommend Swagger/OpenAPI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Ganesh Mohane· Dec 16, 2024
Swagger/OpenAPI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Mateo Bhatia· Dec 12, 2024
Swagger/OpenAPI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Sofia Malhotra· Dec 12, 2024
We evaluated Swagger/OpenAPI against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Dev Yang· Dec 12, 2024
Swagger/OpenAPI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Aarav Kim· Nov 19, 2024
Strong directory entry: Swagger/OpenAPI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Maya Liu· Nov 11, 2024
Swagger/OpenAPI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Mateo Mehta· Nov 3, 2024
We wired Swagger/OpenAPI into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.
- ★★★★★Camila Jain· Nov 3, 2024
I recommend Swagger/OpenAPI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Camila Kapoor· Nov 3, 2024
Useful MCP listing: Swagger/OpenAPI is the kind of server we cite when onboarding engineers to host + tool permissions.
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