Models Plus

Comprehensive AI Model Directory & MCP Server

Unified REST API and Model Context Protocol (MCP) server for AI model metadata, built on models.dev data.

Features • Quick Start • API Docs • MCP • Contributing

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Features

Models PLUS provides a comprehensive AI model catalog with modern tooling:

Core Features

• Unified REST API - Advanced search and filtering for 100+ AI models
• Model Context Protocol (MCP) - Native MCP support with 4 powerful tools
• Real-time Data - Fresh data from models.dev database
• Lightning Fast - Built with Bun runtime and SST v3

Developer Experience

• Zero Config - Biome + Ultracite for ultra-fast formatting and linting
• TypeScript - Full type safety with strict TypeScript configuration
• Cloudflare Workers - Global edge deployment with SST

Rich Metadata

• Comprehensive Model Info - Pricing, limits, capabilities, modalities
• Provider Details - Environment variables, documentation, integrations
• Advanced Filtering - Search by cost, context length, features, and more

Public API: https://modelsplus.quivr.tech

Quick Start

Try the Public API

# List latest models
curl "https://modelsplus.quivr.tech/v1/models?limit=5"

# Find reasoning-capable models
curl "https://modelsplus.quivr.tech/v1/models?reasoning=true"

# Get specific model details
curl "https://modelsplus.quivr.tech/v1/models/openai:gpt-4o"

Local Development

# Install dependencies
bun install

# Start development server
bun run dev

# Build for production
bun run build

Installation

📋 Requirements

• Bun 1.2.21 - Runtime and package manager
• Node.js types - For tooling compatibility (bundled via SST)

Quick Install

# Install dependencies
bun install

# Generate JSON assets from vendor data
cd packages/api && bun run generate && bun run build

Development

Useful Scripts

• bun run build — Build all workspaces
• bun run dev — SST Dev with Cloudflare Worker locally
• bun run dev:api — Direct Worker dev for API only
• bun run deploy — Deploy via SST to Cloudflare Workers
• bun run sync:upstream — Sync vendor subtree

Development Setup

1. Generate JSON assets from vendor TOML files:

   cd packages/api
   bun run generate
   bun run build
   

2. Run development servers:

   # SST Dev (recommended)
   bun run dev
   
   # Direct Worker dev
   cd packages/api && bun run dev
   

Note: SST config (sst.config.ts) auto-builds @modelsplus/api and exposes the Worker URL.

API Guide

Authentication

No authentication required. The API is publicly accessible.

Base URL

https://modelsplus.quivr.tech

Response Format

All API responses return JSON. Error responses include:

{
  "error": "Error message",
  "status": 400
}

Rate Limits

Currently no rate limiting is enforced, but please be respectful.

Query Parameters

Models API (/v1/models)

Parameter	Type	Description	Example
q	string	Search query (model name, provider, etc.)	q=gpt
provider	string	Filter by provider	provider=openai
tool_call	boolean	Filter by tool calling support	tool_call=true
attachment	boolean	Filter by attachment support	attachment=true
reasoning	boolean	Filter by reasoning capabilities	reasoning=true
temperature	boolean	Filter by temperature support	temperature=true
open_weights	boolean	Filter by open weights availability	open_weights=true
min_input_cost	number	Minimum input cost filter	min_input_cost=0.001
max_input_cost	number	Maximum input cost filter	max_input_cost=0.01
min_output_cost	number	Minimum output cost filter	min_output_cost=0.002
max_output_cost	number	Maximum output cost filter	max_output_cost=0.05
min_context	number	Minimum context length	min_context=32000
max_context	number	Maximum context length	max_context=128000
min_output_limit	number	Minimum output limit	min_output_limit=4000
max_output_limit	number	Maximum output limit	max_output_limit=8000
modalities	string	Comma-separated modalities	modalities=image,text
release_after	string	Released after date (ISO)	release_after=2024-01-01
release_before	string	Released before date (ISO)	release_before=2024-12-31
updated_after	string	Updated after date (ISO)	updated_after=2024-06-01
updated_before	string	Updated before date (ISO)	updated_before=2024-12-31
sort	string	Sort field	sort=name or sort=cost_input
order	string	Sort order	order=asc or order=desc
limit	number	Maximum results (default: unlimited)	limit=10
offset	number	Skip number of results	offset=20
fields	string	Comma-separated fields to return	fields=id,name,provider

Providers API (/v1/providers)

Parameter	Type	Description	Example
q	string	Search query (provider name)	q=openai
env	string	Filter by environment variable	env=API_KEY
npm	string	Filter by npm package	npm=openai
limit	number	Maximum results	limit=10
offset	number	Skip number of results	offset=5

Model Object Schema

{
  "id": "openai:gpt-4o",
  "provider": "openai",
  "name": "GPT-4o",
  "release_date": "2024-05-13",
  "last_updated": "2024-08-06",
  "attachment": true,
  "reasoning": false,
  "temperature": true,
  "tool_call": true,
  "open_weights": false,
  "knowledge": "2023-10",
  "cost": {
    "input": 0.0025,
    "output": 0.01,
    "cache_read": 0.00125,
    "cache_write": 0.00625
  },
  "limit": {
    "context": 128000,
    "output": 16384
  },
  "modalities": {
    "input": ["text", "image"],
    "output": ["text"]
  }
}

Provider Object Schema

{
  "id": "openai",
  "name": "OpenAI",
  "env": ["OPENAI_API_KEY"],
  "npm": "openai",
  "api": "https://api.openai.com/v1",
  "doc": "https://platform.openai.com/docs"
}

🔗 API Endpoints

Base URL: https://modelsplus.quivr.tech

Method	Endpoint	Description
GET	/health	Health/status check
GET	/.well-known/mcp	MCP discovery
GET	/v1/models	List/search models
GET	/v1/models/count	Count models after filters
GET	/v1/models/:id	Get specific model details
GET	/v1/providers	List/search providers
GET	/v1/providers/count	Count providers after filters
GET/POST	/mcp	MCP over HTTP (JSON-RPC)
GET/POST	/mcp/http	Alternate MCP endpoint

Code Examples

JavaScript/TypeScript:

// Search models
const models = await fetch('https://modelsplus.quivr.tech/v1/models?reasoning=true&limit=5')
  .then(res => res.json());

// Get specific model
const model = await fetch('https://modelsplus.quivr.tech/v1/models/openai:gpt-4o')
  .then(res => res.json());

Python:

import requests

# Find vision-capable models
response = requests.get('https://modelsplus.quivr.tech/v1/models',
                       params={'modalities': 'image', 'limit': 5})
models = response.json()

MCP Integration

Models PLUS provides native Model Context Protocol (MCP) support for seamless integration with AI assistants.

Available Tools

• search_models - Advanced search and filtering for AI models
• get_model - Detailed information about specific models
• search_providers - Search and filter AI providers
• get_provider - Detailed provider information

Quick Setup

Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "models-plus": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/sdk", "server", "https://modelsplus.quivr.tech/mcp"]
    }
  }
}

Cursor

Configure MCP server with URL: https://modelsplus.quivr.tech/mcp

Other MCP Clients

For any MCP-compatible client, use: https://modelsplus.quivr.tech/mcp

Usage Examples

Once integrated, use natural language:

• "Find all GPT-4 models from OpenAI"
• "Show me reasoning-capable models under $1 per million tokens"
• "What are the specs for Claude 3 Opus?"
• "Which providers support tool calling?"

Direct HTTP API

# Discover capabilities
curl "https://modelsplus.quivr.tech/mcp"

# List available tools
curl -s "https://modelsplus.quivr.tech/mcp" \
  -X POST \
  -H 'Content-Type: application/json' \
  -d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}'

Data Source

Model and provider metadata sourced from models.dev TOML files. The build process (packages/api/src/generate.ts) converts these into optimized JSON artifacts for the API and MCP handlers.

Deployment

Deploys via SST to Cloudflare Workers:

bun run deploy

SST config creates a sst.cloudflare.Worker with global edge deployment.

Contributing

We welcome contributions! Here's how to get started:

1. Fork and create a feature branch
2. Install dependencies: bun install
3. Build and ensure tests pass: bun run build
4. Format code: npx ultracite format && npx ultracite lint
5. Test your changes thoroughly
6. Submit a pull request with a clear description

Acknowledgments

Built on top of models.dev - a comprehensive open-source database of AI model specifications, pricing, and capabilities maintained by the SST team.

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