Covidence

An MCP connector that lets Claude screen studies in Covidence using its own intelligence — no brittle CSS selectors, no hardcoded click paths.

How it works

Instead of a static Playwright script, Claude navigates Covidence directly using Claude in Chrome. It reads the live page, finds the right buttons by understanding what it sees, and casts votes — the same way a human would. When Covidence updates their UI, nothing breaks.

The MCP server itself is intentionally thin: it stores your inclusion/exclusion criteria per review and keeps a session vote log. All actual browser interaction is handled by Claude.

You ──► Claude ──► covidence_screen (MCP)
                        │
                        ▼
              Returns screening prompt
                        │
                        ▼
         Claude navigates Chrome directly
         (read_page → reason → find → click)
                        │
                        ▼
              Votes cast in Covidence

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Setup

There are two ways to connect, depending on whether you're using Claude Desktop or the claude.ai web app.

---

Option A — Claude Desktop (local)

Requirements: Node.js ≥ 18, Claude Desktop app

git clone <this repo>
cd covidence-mcp
npm install
npm run build

Add to your Claude Desktop config:

macOS: ~/Library/Application Support/Claude/claude_desktop_config.json Windows: %APPDATA%\Claude\claude_desktop_config.json

{
  "mcpServers": {
    "covidence": {
      "command": "node",
      "args": ["/absolute/path/to/covidence-mcp/dist/index.js"]
    }
  }
}

Restart Claude Desktop. Done.

---

Option B — claude.ai web (remote hosting)

Claude.ai supports remote MCP servers over SSE. You deploy this server somewhere public and give Claude the URL — no desktop app required.

Requirements: A free account on Railway, Render, or any host that can run Node.js

1. Deploy to Railway (easiest)

Deploy on Railway

Or manually:

# Push this folder to a GitHub repo, then:
# 1. Create a new Railway project from that repo
# 2. Railway auto-detects Node.js and runs `npm run build && npm start`
# 3. Set the PORT environment variable (Railway sets this automatically)

The server switches to HTTP mode automatically when PORT is set. Your public URL will look like:

https://covidence-mcp-production.up.railway.app

2. Connect to claude.ai

1. Go to claude.ai → Settings → Integrations
2. Click Add custom connector
3. Enter your server URL: https://your-deployment.up.railway.app/sse
4. Save — Claude will confirm the connection

Deploy to Render (alternative)

1. Create a new Web Service from your GitHub repo
2. Build command: npm install && npm run build
3. Start command: node dist/index.js
4. Render sets PORT automatically

Deploy to Fly.io (alternative)

fly launch
fly deploy

Then connect https://your-app.fly.dev/sse in Claude's integrations settings.

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Usage

Once connected (either way), the workflow is the same.

First time — tell Claude your login and criteria:

Log in to Covidence with [email protected], then save these criteria for review 12345:
Include: RCTs and quasi-experimental studies in adults with type 2 diabetes.
Exclude: animal studies, systematic reviews, non-English publications, studies before 2000.

Screen a batch:

Screen the next 20 studies in review 12345.

Claude calls covidence_screen, opens Covidence in Chrome, reads a batch of abstracts, applies your criteria, and votes on all of them.

Check progress:

How many studies have we screened today?

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Tools

Tool	What it does
covidence_login	Starts a session and returns Chrome navigation steps for login
covidence_set_criteria	Saves inclusion/exclusion criteria for a review ID
covidence_screen	Builds a full screening prompt — Claude uses this to drive Chrome
covidence_log_vote	Records a vote in the session log
covidence_get_session_log	Returns all votes cast this session with totals
covidence_nav	Returns plain-English navigation steps for any specific action

---

License

MIT