aideveloper-toolsproject-intelligencesemantic-searchquality-scoring
AI project intelligence MCP server tracking 220,000+ AI repos with quality scores and LLM summaries.
Quick Install
Automated & IDE SetupCopy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.
Manual Client & Custom JSON ConfigExpand JSON ▾
Client Config & Setup
Remote HTTPChoose your client or environment
Target File:
💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.
Overview
Use cases
Discover trending AI projects and models across multiple registries
Compare AI projects by quality scores and community metrics
Perform semantic search over AI repositories and datasets
Analyze trends and demand signals from AI ecosystem bot traffic
Integrate AI infrastructure intelligence into agent workflows
Key features
Daily ingestion and scoring of 220,000+ AI projects
Composite quality scores across four dimensions
LLM-generated technical summaries and comparisons
Structured, front-loaded pages for AI agent consumption
Demand Radar analyzing bot traffic for ecosystem insights
Static site generation with internal linking and freshness signals
Capabilities & Tool Schemas
Inspect callable tools, capabilities, and parameters exposed to AI agents by Pt Edge.
Extracted Tool Capabilities
Daily ingestion and scoring of 220,000+ AI projects
Composite quality scores across four dimensions
LLM-generated technical summaries and comparisons
Structured, front-loaded pages for AI agent consumption
Demand Radar analyzing bot traffic for ecosystem insights
Static site generation with internal linking and freshness signals
Documentation Overview
PT-Edge — AI Infrastructure Intelligence
PT-Edge is a precomputed reasoning cache for AI infrastructure decisions. It tracks 220,000+ AI repos across GitHub, PyPI, npm, Docker Hub, HuggingFace, and Hacker News, scores them daily on quality, and publishes the results as a 220,000+ page directory site.
The site serves two audiences: AI agents reading pages on behalf of humans (structured, front-loaded, machine-readable) and humans reading directly (navigable, trustworthy, original analysis). Every page is designed so an AI agent can land on it and walk away with a confident, citable recommendation in one pass.
Every major AI lab's crawl infrastructure treats the site as a primary data source. The access logs are themselves an intelligence layer — see Demand Radar below.
Directory site: mcp.phasetransitions.ai — 220,000+ pages across 17 domains with 2,400+ categories, updated daily.
Built by Graham Rowe
How It Works
- Ingest — daily pipeline pulls GitHub stats, package downloads, releases, HN posts, HuggingFace models/datasets, and registry data
- Score — composite quality score (0-100) from four dimensions: maintenance, adoption, maturity, community
- Enrich — LLM-generated technical summaries, practitioner-focused assessments, and comparison analyses from READMEs
- Publish — static site generation across 17 domains with structured data, internal linking, and freshness signals
- Observe — bot traffic analysis reveals what the AI ecosystem values (Demand Radar)
The entire system runs on a single server instance for under $300/month.
Quality Scoring
| Dimension | Max | Signals |
|---|---|---|
| Maintenance | 25 | Commit activity (30d), push recency |
| Adoption | 25 | Stars (log scale), monthly downloads, reverse dependents |
| Maturity | 25 | License, PyPI/npm packaging, repo age |
| Community | 25 | Forks (log scale), fork-to-star ratio |
Tiers: Verified (70-100), Established (50-69), Emerging (30-49), Experimental (10-29)
Demand Radar
Every bot hit on the site is latent intelligence. The access logs carry three layers of signal:
- Indexing bots (Meta, Anthropic, Amazon, Google, Perplexity, OpenAI) — what AI companies think will be valuable in future model weights. Each bot has a distinct crawl strategy that reveals its parent company's priorities.
- User-action bots (ChatGPT-User, OAI-SearchBot, Perplexity-User) — what real humans are asking AI right now. Each hit represents a practitioner making a technology decision through an AI intermediary.
- Human visitors — what people find through search engines directly.
The Demand Radar extracts these signals and feeds them into content prioritisation — eventually via trained ML models rather than hand-tuned weights. See scratch/demand-radar/ for the full analysis.
Stack
Python, FastAPI, PostgreSQL + pgvector, LLM enrichment (multiple providers), static site generation via Jinja2 + Tailwind CSS. Hosted on Render. MCP tools and REST API for programmatic access.
Development
This is a production system with no staging environment. The database is a live 1GB+ PostgreSQL instance — queries hit real data. See docs/development.md for setup notes and safety rules.
Documentation
docs/strategy.md— strategic positioning and reasoningdocs/roadmap.md— phased build plandocs/design/worker-architecture.md— task queue and worker designdocs/development.md— development setup and database safetyscratch/demand-radar/— access log intelligence analysis
License
MIT — see LICENSE.
Related MCP Servers
Adoption & maintenance
Factual signals from GitHub, npm, and our automated checks — not a rating.
GitHub stars
7
Stargazers on the source repository.
Last commit
4mo ago
Most recent push to the default branch.
Directory activity
2 views
Config copies, upvotes, and views on AllMCPs.
Reviews
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Frequently Asked Questions about Pt Edge
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CategoryCloud Platforms
PricingBring your own API key (usage-based cost)
51Quality signal: Good · 51/100How this signal is calculated ▾
Server availabilityNot measured
Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.
Verified ownership10/20
Documentation & tools25/30
Adoption & activity3/15
Community engagement0/10
A guidance signal from public completeness & health data — not a user rating. New listings start lower and rise as they add docs, get verified, and grow adoption. Signals we can't observe for a listing are skipped, not counted against it.
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