@seofanboy/babbar-mcp-server

v1.0.4

Published

5 months ago

Model Context Protocol server for Babbar.tech SEO API

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seofanboy

mcp babbar seo api modelcontextprotocol

Babbar MCP Server

A Model Context Protocol (MCP) server that integrates with the Babbar.tech SEO API, enabling AI assistants (Claude, ChatGPT with MCP, etc.) to perform advanced SEO analysis, competitor research, backlink auditing, and content gap identification.

Features

Comprehensive SEO Analysis

Host/Domain/URL Overview: Popularity (Value), Trust, Semantic Value, Babbar Authority Score (BAS)
Backlink Analysis: URL, host, and domain backlink profiles
Anchor Text Analysis: Anchor distribution and risk detection
Page Analysis: Identify top pages by Page Value, Trust, Semantic Value, Internal Page Value

Competitor Discovery & Analysis

Similar Hosts: Detect semantically close competitors
Batch Analysis: Compare multiple entities (hosts/domains/URLs)
Historical Data: Track performance over time

Advanced SEO Metrics

Induced Strength (fi): Unique Babbar metric measuring real link value (popularity + topicality)
Internal PageRank: Internal linking analysis
Duplication Analysis: RollingHash (Rabin–Karp) detection of duplicate content (87%+ threshold)

Keywords & SERP Analysis

Keyword Positions: Track Google rankings (currently supports fr_FR, en_GB, en_US, es_ES)
SERP Data: Full SERP with features and positions
Semantic Explorer: Related searches, People Also Ask, suggestions

Performance & Reliability

Smart Caching: 10-day cache for heavy endpoints
Rate Limit Management: Auto-handling + retry after 60s
Usage Logging: Full request/response logging
Error Handling: Clear messages for invalid key, rate limits, or API issues

🚀 Installation & Setup

Prerequisites

Node.js 18 or higher (check with node -v)
npm (comes with Node.js, check with npm -v)
Babbar API key (get it from your Babbar settings)

1. Clone the repository

git clone https://github.com/BabbarTech/BabbarMCP.git
cd BabbarMCP

2. Install dependencies

npm install

3. Set up environment variables

You must export your Babbar API key: Create a .env file from the provided .env.example:

cp .env.example .env

Edit .env:

BABBAR_API_KEY=your_api_key_here
LOG_LEVEL=info

⚠️ Without a valid key, the server will not return real data (the API substitutes with dummy hosts/URLs).

4. Build the project

npm run build

This compiles TypeScript sources into the dist/ directory.

▶️ Usage

Local development

Run in watch mode:

npm run dev

Run built server:

node dist/index.js

With Claude Desktop

Add this block to your claude_desktop_config.json:

{
  "mcpServers": {
    "babbar": {
      "command": "node",
      "args": ["/absolute/path/to/babbar-mcp/dist/index.js"],
      "env": {
        "BABBAR_API_KEY": "your_api_key_here",
        "LOG_LEVEL": "info"
      }
    }
  }
}

With ChatGPT Desktop (MCP support)

Go to Settings > MCP Servers, and add a new server with: Command: node Args: /absolute/path/to/babbar-mcp/dist/index.js Env: at least BABBAR_API_KEY

🛠️ Available Tools (highlights)

Host Analysis

babbar_host_overview → Metrics for a host
babbar_host_backlinks_url|host|domain → Backlink analysis
babbar_host_anchors → Anchor distribution
babbar_host_pages_top_sv|pt|pv|iev → Top pages by different metrics
babbar_host_similar → Competitor discovery
babbar_host_keywords → Keywords and rankings
babbar_host_duplicate → Duplicate content clusters

Domain Analysis

babbar_domain_overview
babbar_domain_backlinks_*
babbar_domain_anchors

URL Analysis

babbar_url_overview
babbar_url_induced_strength (fi)
babbar_url_semantic_similarity
babbar_url_links_internal|external

Keyword & SERP

babbar_keyword_serp
babbar_semantic_questions
babbar_semantic_related
babbar_semantic_suggests

Advanced Analyses

babbar_content_gap
babbar_competitive_analysis
babbar_anchor_profile_risk
babbar_backlink_opportunities_spotfinder (uses Induced Strength only)

📖 Example Queries (works in French)

Do a full metrics analysis of whisky.glass
Give me backlink opportunities for www.recette-americaine.com
Tell me about the semantic neighborhood of www.clubmed.fr
Can you run a content gap analysis for whisky.glass?
Show me the internal duplication issues within whisky.glass
Provide me with a health analysis of whisky.glass

📊 Understanding Babbar Metrics

Page/Host/Domain Value (0–100) → Popularity (Reasonable Surfer model)
Page/Host/Domain Trust (0–100) → Trust score (not a direct Google authority factor)
Semantic Value (0–100) → Popularity adjusted by topical coherence
BAS (0–100) → Combined authority score with anti-spam (best correlated to Google rankings)
Induced Strength (fi) → Only valid metric for backlink value, computed by Babbar API

💡 Tip: Always benchmark your metrics against your direct competitors, not the whole web.

Rate Limiting

Each plan has a per-minute API quota.
Remaining calls are tracked via headers (x-ratelimit-remaining).
The MCP server auto-waits 60 seconds when the limit is exhausted.
Errors are explicit if multiple processes compete for the limit.

Caching

Cache enabled on POST requests (main data endpoints)
Cache lifetime: 10 days
Cache key = endpoint + parameters
Reduces redundant queries and saves API credits

🐞 Error Handling

401 Unauthorized → Invalid or missing BABBAR_API_KEY
429 Too Many Requests → Rate limit exceeded, auto-retry after 60s
400/404/Other → Detailed error, check parameters

Logs include: endpoint, timestamp, status, and remaining quota.

📜 Logging

Default: info level
Levels: debug, info, warn, error
Configure via LOG_LEVEL environment variable
Logs include endpoint calls, rate limit status, success/failure

🛠️ Development