Skip to main content
Glama
ajentsor

LLM Radar

by ajentsor
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

⚠️ Project Retired (2026-05-01)

LLM Radar is no longer maintained. The daily data pipeline has been disabled and the repository is archived.

The live dashboard and MCP server may stop working as upstream APIs change. The model data in data/ reflects the last update before retirement.

Code remains here for reference only. Forks are welcome.

LLM Radar

Real-time AI Model Intelligence via MCP

MIT License Python 3.10+ MCP Server Updated Daily

Skip the search. Your AI already has current model info.

Live Dashboard · Model Reference · MCP Setup · Contributing

What is LLM Radar?

LLM Radar is an MCP server that gives your AI assistant current information about AI models from OpenAI, Anthropic, and Google.

The problem: AI assistants have training cutoffs. Ask about models and you get outdated recommendations, deprecated APIs, or hallucinated pricing.

The solution: Connect LLM Radar and your AI already knows what's available today:

  • Fetching fresh data from provider APIs daily

  • Enriching it with Claude for better descriptions

  • Exposing it via MCP for any compatible client

Related MCP server: Model Hub MCP

MCP Server Setup

Install via pip

# Install
pip install llm-radar-mcp

# Or run directly
pip install llm-radar-mcp && llm-radar-mcp

Claude Desktop config (local stdio):

{
  "mcpServers": {
    "llm-radar": {
      "command": "llm-radar-mcp"
    }
  }
}

Option 3: Docker

docker run -p 8000:8000 ghcr.io/ajentsor/llm-radar:latest

Then connect to http://localhost:8000/sse

Available MCP Tools

Once connected, you can use these tools:

Tool

Description

query_models

Search/filter models by provider, type, or modality support

compare_models

Side-by-side comparison of specific models

get_model

Get detailed info about a specific model by API ID

list_model_ids

List all available model IDs for a provider

Example Queries

"What models support vision input?"
→ Uses query_models with input_modality="image"

"Compare GPT-4o, Claude Sonnet, and Gemini 2.5 Pro"
→ Uses compare_models with those model IDs

"List all OpenAI model IDs"
→ Uses list_model_ids with provider="openai"

Available Resources

The MCP server also exposes resources you can read directly:

Resource URI

Description

llm-radar://models/all

Complete JSON data

llm-radar://models/openai

OpenAI models only

llm-radar://models/anthropic

Anthropic models only

llm-radar://models/google

Google models only

llm-radar://highlights

Curated recommendations

How It Works

┌─────────────────────────────────────────────────────────────────┐
│                    Daily GitHub Action (8am UTC)                │
├─────────────────────────────────────────────────────────────────┤
│                                                                 │
│  ┌──────────┐   ┌──────────┐   ┌──────────┐                    │
│  │  OpenAI  │   │Anthropic │   │  Google  │   ← Fetch APIs     │
│  │   API    │   │   API    │   │   API    │                    │
│  └────┬─────┘   └────┬─────┘   └────┬─────┘                    │
│       │              │              │                           │
│       └──────────────┼──────────────┘                           │
│                      ▼                                          │
│              ┌──────────────┐                                   │
│              │    Claude    │   ← Enrich & Format               │
│              │   (Sonnet)   │                                   │
│              └──────┬───────┘                                   │
│                     │                                           │
│       ┌─────────────┼─────────────┐                            │
│       ▼             ▼             ▼                            │
│  ┌─────────┐  ┌──────────┐  ┌───────────┐                      │
│  │models.  │  │ MCP      │  │  GitHub   │   ← Deploy           │
│  │  json   │  │ Server   │  │  Pages    │                      │
│  └─────────┘  └──────────┘  └───────────┘                      │
│                                                                 │
└─────────────────────────────────────────────────────────────────┘

Data Format

Each model includes:

Field

Description

id

API model identifier

name

Human-friendly name

provider

openai, anthropic, or google

description

What the model is best for

context_window

Max input tokens

pricing

Input/output cost per 1M tokens

capabilities

vision, function_calling, reasoning, etc.

status

active, preview, or deprecated

released

Release date

recommended_for

Use case suggestions

Local Development

# Clone
git clone https://github.com/ajentsor/llm-radar.git
cd llm-radar

# Install
python3 -m venv venv
source venv/bin/activate
pip install -e ".[dev]"

# Run MCP server (stdio mode)
llm-radar-mcp

# Run MCP server (HTTP mode for testing)
llm-radar-mcp --http --port 8000

# Fetch fresh data (requires API keys)
cp .env.example .env
# Edit .env with your API keys
python3 -m llm_radar.fetch_models
python3 -m llm_radar.aggregate_with_claude

Project Structure

llm-radar/
├── src/llm_radar/              # Main package
│   ├── __init__.py
│   ├── mcp_server.py           # MCP server implementation
│   ├── fetch_models.py         # API fetchers
│   └── aggregate_with_claude.py # Claude enrichment
├── data/
│   ├── models.json             # Structured model data
│   ├── MODELS.md               # Human-readable reference
│   └── raw/                    # Raw API responses
├── docs/                       # Landing page (Cloudflare)
├── Dockerfile                  # Container build
├── docker-compose.yml          # Local container setup
├── pyproject.toml              # Python package config
└── .github/workflows/
    └── update-models.yml       # Daily cron job

Configuration

To run the data fetcher yourself:

# .env file
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...
GOOGLE_API_KEY=AI...

For GitHub Actions, add these as repository secrets.

Self-Hosting

Docker Compose

version: '3.8'
services:
  llm-radar:
    image: ghcr.io/ajentsor/llm-radar:latest
    ports:
      - "8000:8000"
    restart: unless-stopped

Cloudflare Workers / Fly.io / Railway

The MCP server supports HTTP/SSE transport, making it deployable to any platform that supports long-running HTTP connections.

Contributing

See CONTRIBUTING.md for guidelines.

Key areas for contribution:

  • Additional providers (Cohere, Mistral, etc.)

  • More MCP tools

  • Better data enrichment prompts

  • Documentation improvements

License

MIT License - see LICENSE

Built for developers who want accurate AI model info

Star this repo · Report Issue · View Dashboard

Install Server
A
license - permissive license
A
quality
F
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity
Issues opened vs closed

Resources

Unclaimed servers have limited discoverability.

Looking for Admin?

If you are the server author, to access and configure the admin panel.

Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/ajentsor/llm-radar'

If you have feedback or need assistance with the MCP directory API, please join our Discord server