GitHub Traffic Analytics MCP server

GitHub Traffic Analytics & Historical Data Tracker

GitHub-Traffic-Analytics is a local-first Python automation tool, FastAPI web dashboard, and Model Context Protocol (MCP) server engineered to bypass GitHub's strict 14-day history limit. It automatically backs up and analyzes your repository's traffic metrics into a local SQLite database.

Visit the official GitHub Traffic Analytics Repository for full documentation and code.

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Table of Contents

• GitHub Traffic Analytics & Historical Data Tracker
  • Table of Contents
  • 🚀 Key Features
  • Architecture
  • Features
  • Prerequisites
  • Quick Install
  • Usage
  • Roadmap
  • License

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🚀 Key Features

• Bypass the 14-Day Limit: Permanently save historical repository metrics via automated data ingestion.
• Web UI & Visualization: Filter and analyze traffic trends with an interactive dashboard and external event logging.
• Built-in MCP Server: Allow AI agents to query your analytics database directly.

GitHub-Traffic-Analytics gives you:

• Persistent local history: daily views, clones, referrers, and paths stored in SQLite
• Event correlation: annotate your traffic timeline with the external events that moved it
• Staleness detection: skips writes when the GitHub API returns an identical frozen snapshot, so bad data never enters the historical record
• Two interfaces: a web dashboard for humans and an MCP server for IDE agents

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Architecture

GitHub API
    │
    ▼
ingester.py  ──── staleness check ────► skip + warn (upstream_stale)
    │
    ▼
SQLite (app.db)
    │
    ├──► FastAPI (main.py) ──► Jinja2 templates ──► Web UI
    │         │
    │         └──► REST API (/api/metrics, /api/events, /api/sync)
    │
    └──► MCP Server (mcp_server.py) ──► IDE agents (Cursor, VS Code)

• ingester.py: Coordinates GitHub API requests and manages database persistence.
• main.py & templates/: Powers the local FastAPI server and web dashboard.
• mcp_server.py: Exposes structured analytic tools to connected LLMs.

Key files:

File	Role
main.py	FastAPI app, REST endpoints, Pydantic models
ingester.py	GitHub CLI data fetch, staleness detection, sync logic
database.py	SQLite init, DB_PATH resolution, schema setup
mcp_server.py	MCP tools for LLM-driven traffic analysis
templates/index.html	Main dashboard: Alpine.js + Chart.js
templates/source_detail.html	Per-referrer/path historical chart

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Features

Traffic ingestion:

• Fetches views, clones, unique visitors, and unique cloners via the GitHub CLI
• Detects and skips upstream-stale snapshots (identical payload = no write)
• UNIQUE constraint on (repo_id, logged_date, source_type, source_or_path) prevents duplicate records
• Background task execution: sync requests return immediately, ingestion runs async

Web dashboard:

• Multi-repo support: add and switch between repos from the UI
• Date range filtering on all charts
• Four traffic charts: views, unique visitors, clones, unique cloners
• Referrers and top paths with per-source historical drill-down
• Event annotations rendered as dashed vertical lines on the charts
• Tooltip footer shows matching event titles per date

External event tracking:

• Log Reddit posts, DEV.to articles, releases, and any external event with full CRUD
• Events are stored with title, category, url, description, and event_date
• Correlate traffic spikes with the content or release that caused them

MCP server:

• get_repo_metrics: daily traffic for a date range
• list_events: all logged external events with IDs
• log_external_event: create a new event (all fields required)
• update_external_event: edit an existing event by ID
• delete_external_event: remove an event by ID
• query_sql: raw SELECT access to the SQLite database
• trigger_ingester: on-demand sync from any MCP-connected IDE

Claude Desktop: GitHub repository traffic analysis with the MCP server connected.

Analytics skill (gh-analytics-skill/SKILL.md):

An agent skill that turns the raw MCP tools into a guided analysis workflow. When exposed to an MCP-connected agent, it:

• Always calls trigger_ingester first, so answers come from fresh data — never cached
• Separates organic clones from CI/bot loops (clone_ratio ≥ 8.0)
• Maps deep-funnel path conversion (issues, PRs, discussions, dependency pages) and normalized referrer buckets (Reddit, Hacker News, Google, GitHub)
• Correlates traffic and star spikes with logged external events, validating unknowns via Tavily
• Synthesizes KPIs, anomalies, star velocity, and code churn — optionally as a D3.js HTML dashboard

How to use it: register the github-analytics MCP server in your client, then make the skill file available to your agent (e.g., drop it into your skills directory). The skill triggers automatically on requests like "how's my repo traffic this month?" or "why did stars spike last week?". See gh-analytics-skill/SKILL.md for the full query templates, schema reference, and rules.

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Prerequisites

1. Python 3.10+:

2. GitHub CLI: required for all data ingestion

# macOS
brew install gh

# Windows
winget install --id GitHub.cli

# Linux
# https://cli.github.com/

3. Authenticate the GitHub CLI:

gh auth login

Follow the prompts. Select HTTPS and authenticate via browser. Verify with:

gh auth status

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Quick Install

# 1. Clone the repo
git clone https://github.com/KikeVen/GitHub-Traffic-Analytics.git
cd GitHub-Traffic-Analytics

# 2. Create and activate a virtual environment (Python 3.10+)
python -m venv .venv
source .venv/bin/activate      # Windows: .venv\Scripts\activate

# 3. Install dependencies
pip install -r requirements.txt

# 4. Configure your default repository
cp .env.example .env            # Windows: copy .env.example .env
# Edit .env and set GITHUB_REPO=owner/repo

# 5. Initialize the database and run the first sync
python ingester.py

# 6. Start the web server
# uvicorn main:app --reload
python main.py

Open http://localhost:8000 in your browser.

Configuration (.env):

Copy .env.example to .env and set your default repository:

GITHUB_REPO=owner/repo

GITHUB_REPO is only the default used when running python ingester.py directly. The web UI and the MCP trigger_ingester(repo="owner/repo") tool pass their own repo and override it, so you can track multiple repositories without editing .env. .env is gitignored — commit .env.example instead.

MCP server (for IDE agents):

python mcp_server.py

Add to your MCP client config (e.g., mcp.json in Claude Desktop):

{
  "mcpServers": {
    "github-analytics": {
      "command": "D:\\Users\\<username>\\<file_path>\\GitHub-Traffic-Analytics\\venv\\Scripts\\python.exe",
      "args": [
        "D:\\Users\\<username>\\<file_path>\\GitHub-Traffic-Analytics\\mcp_server.py"
      ]
    }
  }
}

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Usage

Add a repository via the web UI:

Open the dashboard, enter owner/repo (e.g., <user>/<repo>) and click Add Repo. The ingester runs in the background and populates the charts.

Sync on demand:

Click Sync in the dashboard, or trigger via MCP:

trigger_ingester(repo="<user>/<repo>")

Log an external event:

Use the dashboard form or the MCP tool:

log_external_event(
    repo="<user>/<repo>",
    date="2026-09-22",
    title="Reddit post: RAG can rank but it can't judge",
    category="reddit",
    url="https://reddit.com/r/Rag/...",
    description="Posted to r/Rag, 14 upvotes, 13 comments."
)

The event appears as an annotated vertical line on all traffic charts.

Query via MCP (IDE agent):

get_repo_metrics(repo="<user>/<repo>", start_date="2026-09-10", end_date="2026-09-27")
query_sql(query="SELECT date, views_count, clones_count FROM daily_traffic WHERE repo_id = 1 ORDER BY date DESC LIMIT 14")

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Roadmap

• Chart images returned directly from MCP tools (ImageContent / base64 PNG) for inline rendering in Cursor and VS Code
• get_traffic_sources MCP tool: referrers and paths without raw SQL
• Automatic release ingestion from GitHub Releases API into releases_history
• PostgreSQL backend option for multi-user or higher-volume setups
• Stars history chart on the dashboard

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License

MIT: see LICENSE for details.

Maintainer: @KikeVen