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OpenAPI Documentation▌

zkytech

by zkytech

Transform OpenAPI specifications into dynamic tools for integrating external services, supporting JSON web token authent

Transforms OpenAPI specifications into dynamic tools for interacting with external services, handling authentication, validation, and request routing without custom code.

github stars

★ 2

GitHubnpm
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Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.

Works with remote URLs or local filesAuto-refreshes documentationNo manual setup required

best for

  • / API developers exploring service documentation
  • / Testing and debugging REST APIs
  • / Learning unfamiliar API structures

capabilities

  • / List API groups from OpenAPI specs
  • / Browse APIs within specific groups
  • / Get detailed endpoint information
  • / Search APIs by keyword
  • / Load remote or local OpenAPI files
  • / Auto-refresh documentation on requests

what it does

Converts OpenAPI/Swagger documentation into interactive tools, letting you browse, search, and explore API endpoints without writing code.

about

OpenAPI Documentation is a community-built MCP server published by zkytech that provides AI assistants with tools and capabilities via the Model Context Protocol. Transform OpenAPI specifications into dynamic tools for integrating external services, supporting JSON web token authent It is categorized under developer tools.

how to install

You can install OpenAPI Documentation 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

OpenAPI Documentation is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.

readme

api-doc-mcp

This is a set of MCP tools for managing and retrieving OpenAPI documentation.

Features

This toolkit provides the following features:

  1. List all API groups
  2. List APIs in a specified group
  3. Get API details
  4. Search APIs
  5. Support both remote API docs and local JSON files
  6. Auto-refresh API documentation on each request

Usage

For cursor

# remote api
npx -y api-doc-mcp http://localhost:8000/swagger.json

# local file
npx -y api-doc-mcp ./swagger.json

Build

npm run build

Usage

Command Format

npx api-doc-mcp <API_DOC_URL_OR_FILE_PATH>

Examples

  1. View help:
npx api-doc-mcp
  1. List all API groups (Remote API):
npx api-doc-mcp https://api.example.com/swagger.json
  1. List all API groups (Local file):
npx api-doc-mcp ./swagger.json

Development

npm run dev

Tool Description

listApiGroups

List all available API groups.

Returns:

  • name: Group name
  • description: Group description
  • apiCount: API count

listGroupApis

List all APIs in a specified group.

Parameters:

  • groupName: API group name

Returns:

  • path: API path
  • method: HTTP method
  • summary: API summary

getApiDetail

Get detailed information about a specified API.

Parameters:

  • path: API path
  • method: HTTP method

Returns:

  • Complete API details, including parameters, request body, and response definition

searchApis

Search APIs.

Parameters:

  • keyword: Search keyword

Returns:

  • path: API path
  • method: HTTP method
  • summary: API summary

FAQ

What is the OpenAPI Documentation MCP server?
OpenAPI Documentation 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 OpenAPI Documentation?
This profile displays 30 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 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. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 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

4.5★★★★★30 reviews
  • ★★★★★Mia Ndlovu· Dec 28, 2024

    OpenAPI Documentation has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • ★★★★★Mia Garcia· Dec 4, 2024

    OpenAPI Documentation reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • ★★★★★Soo Bansal· Nov 23, 2024

    I recommend OpenAPI Documentation for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • ★★★★★Mia Thompson· Nov 19, 2024

    OpenAPI Documentation is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.

  • ★★★★★Rahul Santra· Nov 11, 2024

    OpenAPI Documentation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • ★★★★★Mia Malhotra· Nov 3, 2024

    OpenAPI Documentation is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.

  • ★★★★★Mia Lopez· Oct 22, 2024

    We evaluated OpenAPI Documentation against two servers with overlapping tools; this profile had the clearer scope statement.

  • ★★★★★Hana Kapoor· Oct 14, 2024

    Strong directory entry: OpenAPI Documentation surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • ★★★★★Mia Chen· Oct 10, 2024

    We wired OpenAPI Documentation into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • ★★★★★Pratham Ware· Oct 2, 2024

    We evaluated OpenAPI Documentation against two servers with overlapping tools; this profile had the clearer scope statement.

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