MCPBuilder
Generate Model Context Protocol (MCP) servers from API specifications (OpenAPI/Postman) with web UI
Install / Use
claude mcp add UnitOneAI -- npx -y github:UnitOneAI/MCPBuilderIf the server publishes to npm under a different name, use that package instead ā check the repo README.
MCP Server
Model Context Protocol server
Quality Score
Category
AutomationSupported Platforms
Tags
Our assessment of MCPBuilder
MCPBuilder scores 75/100 on our quality scale, 2610th of 2,895 Automation skills we index.
Its MCP Server is 16 KB long, well organised into 47 sections with 6 code examples: a thorough specification that gives an agent plenty to work with.
It has 3 GitHub stars, so there is little community track record yet; judge it on its content.
Maintenance, license and trust
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
- Our last check on 2026-09-12 found the source still online.
- It is released under the MIT license, a permissive license that allows use, modification and commercial use with attribution.
- Its trust signals score 85/100, with 2 cautions from licensing, adoption, age or documentation. These come from repository metadata, not a code audit ā read the skill file before letting an agent act on it.
MCPBuilder compared with similar skills
All 4 of these similar skills score higher than MCPBuilder; compare them before choosing.
| Skill | Score | Stars | Updated | Format |
|---|---|---|---|---|
| MCPBuilder (this skill)by UnitOneAI | 75 | 3 | 6mo ago | MCP Server |
| Agent-Reachby Panniantong | 100 | 95.7k | 3d ago | CLAUDE.md |
| headroomby headroomlabs-ai | 100 | 75.0k | today | CLAUDE.md |
| CowAgentby zhayujie | 100 | 47.3k | today | CLAUDE.md |
| Scraplingby D4Vinci | 100 | 86.8k | today | MCP Server |
Frequently asked questions
- How do I install MCPBuilder?
- Run
claude mcp add UnitOneAI -- npx -y github:UnitOneAI/MCPBuilder. The install tabs above show the steps for each supported agent. - Which AI agents does MCPBuilder work with?
- It is written for Claude Code and Claude Desktop, as a MCP Server file. Other agents that read the same format can often use it too.
- Is MCPBuilder safe to use?
- It is MIT-licensed and scores 85/100 on trust signals. Skills are instructions an agent will follow, so read the file before installing it and do not approve commands you do not understand.
- Is MCPBuilder still maintained?
- The repository was last updated about 6 months ago. That is recent enough to be usable, but agent tooling moves fast, so check the instructions against your agent's current version.
Skill content
View source on GitHubUNITONE MCP Builder
Transform any API into a Model Context Protocol (MCP) server with an intuitive web portal.
Generate MCP servers from OpenAPI/Swagger specs, Postman collections, or API documentation. Make your APIs accessible to AI assistants through natural language interactions.
Built With
- Backend: Node.js + TypeScript + Express.js
- Frontend: React 18 + Material-UI + Vite
- Database: better-sqlite3
- MCP SDK: @modelcontextprotocol/sdk v1.20+
- Template Engine: Handlebars
- Type Safety: TypeScript + Zod
ā ļø Important: Local Development Tool
UNITONE MCP Builder is designed for local development use only.
This is the open source version intended for single-user local development environments, while we welcome contributors!
If you require:
- Hosted/managed MCP server generation
- Public server deployment with authentication
- Extended security features
- Enterprise support
- Team collaboration features
Please contact the UNITONE team at https://unitone.ai for our enterprise solutions.
Table of Contents
- Features
- Quick Start
- Admin Portal Guide
- Usage
- Using Generated Servers
- Best Practices
- Project Structure
- API Endpoints
- Development
- Tech Stack
- Troubleshooting
- Contributing
- License
- Resources
- Support
Features
- š Multiple Input Formats - OpenAPI/Swagger (JSON/YAML), Postman collections, or plain text API docs
- šØ Admin Dashboard - Beautiful Material-UI interface for managing API configurations and MCP servers
- š§ STDIO Transport - Generate servers compatible with any MCP-compatible client
- š Authentication Support - Bearer tokens, API keys, and OAuth2 client credentials
- š ļø Auto-generate Tools - Automatically create MCP tools from API endpoints
- š¦ One-Click Deployment - Deploy and manage servers directly from the dashboard
- ā Type Safe - Full TypeScript support with auto-generated types
- š Path Parameters - Automatic path parameter substitution (e.g.,
/users/{id}) - š§© OpenAPI $ref Resolution - Resolves component references in specifications
- š Large-Scale APIs - Tested with 200+ endpoint APIs (Microsoft Graph)
Quick Start
Prerequisites
- Node.js >= 20.0.0
- npm >= 9.0.0
Installation
# Clone the repository
git clone https://github.com/UnitOneAI/MCPBuilder.git
cd MCPBuilder
# Install dependencies
npm install
# Start the application
npm run dev
The dashboard will be available at http://localhost:5173 and the API server at http://localhost:3000.
Admin Portal Guide
Visual walkthrough of creating and deploying an MCP server using the admin portal.
1. MCP Servers Dashboard
Start from the MCP Servers page where you can view all your servers and create new ones.

2. Parse Your API
Provide your API specification using OpenAPI/Swagger, API Documentation, or Postman Collection. The parser will automatically extract endpoints and generate MCP tools.

3. Select Endpoints
Choose which endpoints to expose as MCP tools. The portal shows endpoint details including method, path, description, and parameters. Use the search and filter features for large APIs.

4. Server Generated
Once generation is complete, you'll see the server location and configuration details. The portal provides ready-to-use configuration for your MCP client.

5. Deploy and Run
Deploy your server with one click. The portal handles installation, building, and startup automatically. Monitor server status and manage deployments from the dashboard.

Usage
Option 1: Quick Start with OpenAPI Spec
The fastest way to get started is with an OpenAPI/Swagger specification.
Example - Microsoft Graph Users API:
- Navigate to "Create Server" in the dashboard
- Click "OpenAPI/Swagger" tab
- Paste the spec URL or upload file:
https://raw.githubusercontent.com/microsoftgraph/msgraph-sdk-powershell/dev/openApiDocs/v1.0/Users.yml - Click "Parse & Create Configuration"
- Select the endpoints you want (e.g., GET /users, GET /users/{id})
- Configure server:
- Name:
msgraph-users - Description: Microsoft Graph Users API
- Auth: OAuth 2
- Name:
- Click "Generate"
Your MCP server will be generated in generated-servers/msgraph-users/.
Available Microsoft Graph APIs:
- Users:
https://raw.githubusercontent.com/microsoftgraph/msgraph-sdk-powershell/dev/openApiDocs/v1.0/Users.yml - Groups:
https://raw.githubusercontent.com/microsoftgraph/msgraph-sdk-powershell/dev/openApiDocs/v1.0/Groups.yml - Mail:
https://raw.githubusercontent.com/microsoftgraph/msgraph-sdk-powershell/dev/openApiDocs/v1.0/Mail.yml - Calendar:
https://raw.githubusercontent.com/microsoftgraph/msgraph-sdk-powershell/dev/openApiDocs/v1.0/Calendar.yml - View all specs
Option 2: Using Plain Text Documentation
If you don't have an OpenAPI spec, you can paste API documentation or upload file:
GET /users - Get all users
- page (number) - Page number (optional)
- limit (number) - Items per page (optional)
POST /users - Create a new user
- name (string) - User's name (required)
- email (string) - User's email (required)
GET /users/{id} - Get user by ID
- id (string) - User ID (required)
Option 3: Import Postman Collection
Simply paste your exported Postman collection JSON or upload file.
Build and Deploy Your MCP Server
After generation is complete:
- Navigate to the "MCP Servers" section in the admin portal
- Your server will now be shown with status "Ready"
- Click "Deploy" to start the server
- If deployment is successful, the status will change from "Ready" to "Running"
The server is now ready to use with any MCP client. Environment variables (API_BASE_URL, API_TOKEN, etc.) are automatically passed from your MCP client configuration - no manual export needed!
Using Generated Servers
All generated servers use STDIO transport, compatible with any MCP-enabled application.
MCP Client Configuration
Configure your MCP-compatible client to use the generated server. Most clients follow a similar configuration pattern:
Example configuration:
{
"mcpServers": {
"my-api-server": {
"command": "node",
"args": ["/absolute/path/to/generated-servers/my-api-server/dist/index.js"],
"env": {
"API_BASE_URL": "https://api.example.com",
"API_TOKEN": "your_bearer_token_here"
}
}
}
}
Key configuration elements:
- command:
node - args: Absolute path to
dist/index.jsin your generated server - env: Environment variables for API configuration
Refer to your MCP client's documentation for specific configuration file locations and format. Generated servers include a .vscode/mcp.json file as a reference.
Note: When using with an MCP client (like Claude Desktop), environment variables are automatically passed from the client configuration. Manual export is only needed for local testing.
Best Practices
Tool Count Recommendations
When creating MCP servers, the number of tools you expose significantly impacts performance and accuracy:
Optimal Range (Recommended): Under 40 tools
- Best performance and tool selection accuracy
- AI assistants can effectively navigate and choose the right tools
- Faster response times and lower resource usage
Warning Range: 40-100 tools
- Server will function but may experience reduced performance
- Tool selection accuracy may decrease as the AI has more options to evaluate
- Consider splitting into multiple focused servers if possible
Critical Range: 100+ tools
- Significant performance degradation likely
- Poor tool selection accuracy due to overwhelming number of options
- Strongly recommended to refactor into multiple servers
Creating Multiple Focused Servers
For large APIs (100+ endpoints), we recommend:
-
Group by functionality - Organize endpoints into logical domains
- Example:
msgraph-users,msgraph-mail,msgraph-calendar
- Example:
-
Split by use case - Create servers for specific workflows
- Example:
api-read-operations,api-write-operations
- Example:
-
Separate by resource type - Divide by primary entities
- Example:
api-customers,api-orders,api-inventory
- Example:
Benefits of multiple servers:
- Each server stays under the 40-tool optimal threshold
- Improved tool selection accuracy and performance
- Users can enable only the servers they need
- Easier to maintain and update individual servers
- Better error isolation and debugging
Large-Scale API Example
For the Microsoft Graph API (200+ endpoints), instead of one massive server:
# ā Avoid: Single server with 200+ tools
msgraph-all-api
# ā
Recommended: Multiple focused servers
msgraph-users # 15 tools - User management
msgraph-mail # 22 tools - Email operations
msgraph-calendar # 18 tools - Calendar operations
msgraph-groups # 12 tools - Group management
msgraph-files # 25 tools - OneDrive operations
The portal will automatically warn you when selecting more than 40 endpoints and provide guidance on creating multiple focused servers.
Project Structure
MCPBuilder/
āāā src/
ā āāā server/ # Express backend API
ā ā āāā routes/ # API route handlers
ā ā ā āāā apiConfigs.ts # API configuration endpoints
ā ā ā āāā mcpServers.ts # MCP server management
ā ā ā āāā deployments.ts # Server deployment/build
ā ā ā āāā parser.ts # OpenAPI/Postman parsers
ā ā āāā utils/ # Server utilities
ā ā ā āāā logger.ts # File-based logging
ā ā āāā database.ts # SQLite database operations
ā ā āāā index.ts # Server entry point
ā āāā client/ # React frontend
ā ā āāā components/ # Reusable UI components
ā ā āāā pages/ # Page components
ā ā āāā services/ # API service layer
ā ā āāā assets/ # Static assets (images, etc.)
ā ā āāā theme.ts # Material-UI theme config
ā ā āāā App.tsx # Main app component
ā āāā generator/ # MCP generator core
ā ā āāā McpGenerator.ts # Code generator
ā ā āāā ApiParser.ts # API specification parser
ā
Truncated for display ā read the full file on GitHub.
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Trust signals
From repository metadata: license, adoption, age and documentation. Not a code audit ā see the Safety scan above for what the skill file itself contains.
