LLM Radar
Skip the search. MCP Server that gives your AI current info about OpenAI, Anthropic, and Google models. Updated daily.
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β οΈ 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LLM Radar is no longer maintained.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
Skip the search. Your AI already has current model info.
Live Dashboard Β· Model Reference Β· MCP Setup Β· 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 Ask about models and you get outdated recommendations, deprecated APIs, or hallucinated pricing.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
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?"
β Uses query_models with input_modality="image"
"Compare GPT-4o, Claude Sonnet, and Gemini 2.5 Pro"
β Uses compare_models with those model IDs
"List all OpenAI model IDs"
β 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
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β β
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β β OpenAI β βAnthropic β β Google β β Fetch APIs β
β β API β β API β β API β β
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β βΌ β
β ββββββββββββββββ β
β β Claude β β Enrich & Format β
β β (Sonnet) β β
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β β β
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β βββββββββββ ββββββββββββ βββββββββββββ β
β βmodels. β β MCP β β GitHub β β Deploy β
β β json β β Server β β Pages β β
β βββββββββββ ββββββββββββ βββββββββββββ β
β β
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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/
βββ src/llm_radar/ # Main package
β βββ __init__.py
β βββ mcp_server.py # MCP server implementation
β βββ fetch_models.py # API fetchers
β βββ aggregate_with_claude.py # Claude enrichment
βββ data/
β βββ models.json # Structured model data
β βββ MODELS.md # Human-readable reference
β βββ raw/ # Raw API responses
βββ docs/ # Landing page (Cloudflare)
βββ Dockerfile # Container build
βββ docker-compose.yml # Local container setup
βββ pyproject.toml # Python package config
βββ .github/workflows/
βββ 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
