How Polydev Works
Technical architecture behind multi-model consultation, CLI-as-API tunneling, and crowdsourced model rankings. Built on MCP, runs in your IDE.
Multi-Model Architecture
Polydev registers as an MCP (Model Context Protocol) server in your IDE. When you ask for perspectives, your code context is sent to multiple frontier models simultaneously. Each model responds independently, giving you diverse approaches to the same problem.
CLI Detection
Polydev detects which CLI tools are available on your machine using a three-tier strategy:
Response Merging
Responses from all models are collected, formatted, and returned as a single MCP tool result. The merge strategy:
MCP Configuration
{
"mcpServers": {
"polydev": {
"command": "npx",
"args": ["--yes", "--package=polydev-ai@latest", "--", "polydev-stdio"],
"env": {
"POLYDEV_USER_TOKEN": "pd_your_token_here"
}
}
}
}CLI-as-API Tunnel
Turn your $20/month CLI subscription into a REST API. Polydev maintains an authenticated session with your CLI tool and exposes it as a standard HTTP endpoint. Same subscription, programmatic access.
codex auth login
API Usage
# Query your Claude Pro subscription via REST API
curl https://api.polydev.ai/v1/chat \
-H "Authorization: Bearer pd_your_token" \
-H "Content-Type: application/json" \
-d '{
"model": "claude-opus-4-6",
"messages": [
{"role": "user", "content": "Review this PR for security issues"}
]
}'
# Response routes through your authenticated CLI session
# No API key needed — uses your $20/mo subscriptionSecurity Model
Supported Providers
Crowdsourced Rankings
Every Polydev query generates pairwise comparisons between model responses. These comparisons feed into an Elo rating system that ranks models across 6 dimensions — using real developer code, not synthetic benchmarks.
6-Dimension Prompt Classification
Every prompt is automatically classified across 6 dimensions, enabling filtered leaderboard views. Find the best model for your specific stack and task type.
Elo Rating System
Ratings use the Bradley-Terry model with an adaptive K-factor that decreases as more comparisons accumulate. New models start at 1500 Elo and converge quickly with high K, then stabilize.
Each dimension maintains independent Elo ratings, so a model can rank #1 for Python debugging but #3 for Rust architecture.
Privacy Model
Prompts are classified but never stored. The classification (e.g., "Python + Debugging + Complex") is retained for dimension filtering, but the actual code and prompt text are discarded after ranking.
Only pairwise comparison results (model A beat model B) are persisted.
Open Source
Polydev's MCP server is published as an open-source npm package. The cloud service adds authentication, leaderboard data aggregation, and CLI-as-API tunneling.
Open Source (npm)
Cloud Service
Ready to Try It?
Add Polydev to your IDE in 30 seconds. Free tier includes 500 messages/month.