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

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

Generate JSON assets from vendor TOML files:

cd packages/api
bun run generate
bun run build

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:

Fork and create a feature branch
Install dependencies: bun install
Build and ensure tests pass: bun run build
Format code: npx ultracite format && npx ultracite lint
Test your changes thoroughly
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.

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

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

Generate JSON assets from vendor TOML files:

cd packages/api
bun run generate
bun run build

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:

Fork and create a feature branch
Install dependencies: bun install
Build and ensure tests pass: bun run build
Format code: npx ultracite format && npx ultracite lint
Test your changes thoroughly
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.

Features

Core Features

Developer Experience

Rich Metadata

Quick Start

Try the Public API

Local Development

Installation

📋 Requirements

Quick Install

Development

Useful Scripts

Development Setup

API Guide

Authentication

Base URL

Response Format

Rate Limits

Query Parameters

Models API (/v1/models)

Providers API (/v1/providers)

Model Object Schema

Provider Object Schema

🔗 API Endpoints

Code Examples

MCP Integration

Available Tools

Quick Setup

Claude Desktop

Cursor

Other MCP Clients

Usage Examples

Direct HTTP API

Data Source

Deployment

Contributing

Acknowledgments

Features

Core Features

Developer Experience

Rich Metadata

Quick Start

Try the Public API

Local Development

Installation

📋 Requirements

Quick Install

Development

Useful Scripts

Development Setup

API Guide

Authentication

Base URL

Response Format

Rate Limits

Query Parameters

Models API (/v1/models)

Providers API (/v1/providers)

Model Object Schema

Provider Object Schema

🔗 API Endpoints

Code Examples

MCP Integration

Available Tools

Quick Setup

Claude Desktop

Cursor

Other MCP Clients

Usage Examples

Direct HTTP API

Data Source

Deployment

Contributing

Acknowledgments

Models API (`/v1/models`)

Providers API (`/v1/providers`)

Models API (`/v1/models`)

Providers API (`/v1/providers`)