MCP Server Catalog + Quality Score

Scrape top awesome-mcp-server lists, score every MCP server by stars / recency / license / activity. Find production-ready MCP servers without clicking through 500+ READMEs.

What does this Actor do?

The MCP (Model Context Protocol) ecosystem exploded in 2025 — there are now hundreds of MCP servers across multiple "awesome-mcp" lists, but no quality bar. Half are abandoned, half lack a license, and the best ones are buried under repo-spam.

This Actor:

1. Scrapes the top awesome-mcp-server lists on GitHub (default: 3 of the largest)
2. Extracts every GitHub repo linked in those lists
3. Fetches stats for each (stars, forks, last commit, license, open issues, language)
4. Scores each on 6 quality dimensions (max 100 points)
5. Returns a ranked dataset + Markdown report

You get a single, current, decision-ready view of "which MCP server should I actually use?"

Why use this Actor?

• Pre-install due diligence: before adding an MCP server to your Claude / Cursor / Cline / Aider setup, check if it's maintained
• Curate your own list: drop the JSON into your README to spec-build a higher-signal "awesome" list
• Security teams: find MCP servers without licenses (legal risk) or with abandoned maintainers (supply-chain risk)
• Investors / market analysts: track MCP ecosystem growth over time

How to use

1. (Optional) Filter by keyword (e.g. github, slack, database) to narrow scope
2. Set Max servers (default 100, max 500)
3. (Optional) Add additional Source lists beyond the 3 defaults
4. Click Start
5. Download the dataset or mcp-catalog.md from the Storage tab

Input

• keywordFilter — only include repos whose name contains this keyword (case-insensitive)
• maxServers — stop after N candidates (default 100)
• sourceLists — array of GitHub repos with curated MCP lists (defaults to the 3 largest)

Output

Each row in the dataset:

{
  "fullName": "github/github-mcp-server",
  "name": "github-mcp-server",
  "url": "https://github.com/github/github-mcp-server",
  "sourceList": "punkpeye/awesome-mcp-servers",
  "stars": 12500,
  "forks": 890,
  "openIssues": 45,
  "lastCommit": "2026-05-18T14:23:11Z",
  "license": "MIT",
  "language": "TypeScript",
  "description": "Official MCP server for GitHub API",
  "qualityScore": 95,
  "scoreBreakdown": {
    "stars": 30,
    "recency": 20,
    "license": 15,
    "description": 10,
    "docs": 10,
    "activity": 15
  }
}

Quality scoring (6 dimensions, max 100 points)

Dimension	Max	What it measures
Stars	30	Log-scaled (avoids over-rewarding mega-repos)
Recency	20	Pushed in last 90d = 20, 180d = 10, 365d = 5
License	15	MIT/Apache/BSD = 15, GPL = 10, none = 0
Description	10	Has substantive repo description
Docs	10	README size proxy (size_kb > 50)
Activity	15	5-200 open issues = healthy; 0 = abandoned

Pricing

Pay-per-event model:

• First run: free trial (up to 50 servers scored)
• Per-server rate: $0.02 per server scored
• Per-catalog (100 servers): $2
• Monthly refresh subscription: $5/month for daily refreshed catalog

Tips

• Set keywordFilter to narrow scope and save API quota — GitHub anonymous API is rate-limited to 60 req/hour
• Run during off-peak (US night) if you hit rate-limits on the GitHub API
• For ongoing tracking, schedule the Actor weekly and diff the dataset to catch new high-quality MCP servers as they appear

FAQ

Why not just look at stars?

Stars are gamed and lag adoption. A repo at 1k stars from 2024 with no commits since is worse than a 200-star repo with 50 PRs in the last month. The composite score corrects for that.

What if a list has 1000+ repos?

The Actor stops at maxServers. You can raise it to 500 (the GitHub API rate-limit ceiling for anonymous calls per hour).

Is the data current?

Each run fetches live GitHub data. The dataset is a snapshot at run time — schedule daily/weekly if you need fresh data.

Support

Issues / feature requests: open in the Issues tab on the Apify console.

Built by Ian Mu — github.com/ianymu — author of verify-before-stop.