MCP GEO Server (mcp-geo-server)

A production-grade Model Context Protocol (MCP) Server that exposes the SAGE (Search & AI-Engine Guided Evaluation) auditing and measurement engine to AI agents (Claude Desktop, Cursor, Antigravity, custom agents).

Developed by Taqi Molavi (Molavi R&D Think Tank) as the official tool-exposure and protocol adapter layer for the SAGE Core framework.

🏛️ Architecture: Thin SAGE Adapter

mcp-geo-server implements a strict separation of concerns:

AI Agent / Claude Desktop / Cursor / Antigravity
                     │
                     ▼ (MCP JSON-RPC Protocol)
           mcp-geo-server (Protocol & Transport Layer)
                     │
                     ▼ (Python API / SageAdapter)
            SAGE Core (`sage-audit` Engine)
         ┌───────────┼───────────┐
         ▼           ▼           ▼
      Pillar 1    Pillar 2    Pillar 3
    Technical SEO  Entity AEO   GEO & RAG

• mcp-geo-server: Responsible exclusively for MCP protocol negotiation, tool definitions, transport error handling, and standard JSON shaping.

• sage-audit: The single source of truth for auditing, semantic embedding, RAG simulation, Citation Survival Proxy (CSP), and the SAGE Evidence Taxonomy (E0–E5).

Related MCP server: maxaeo-ai-visibility-mcp

🛠️ MCP Tools Reference

Full Audits

• audit_url(url, embedding_backend="auto")
  Performs a complete 3-pillar audit (Technical SEO, Entity AEO, GEO Readiness) against a live URL.

• audit_html(html_content, url="https://example.local/", embedding_backend="auto")
  Performs a complete 3-pillar audit on raw HTML strings (ideal for local development, CI/CD, or offline snapshots).

Pillar-Specific Tools

• technical_seo(url=None, html_content=None)
  Pillar 1: Audits DOM hygiene, clean heading outlines, canonical tags, AI crawler directives (GPTBot, ClaudeBot, PerplexityBot in robots.txt), and header policies.

• aeo_readiness(url=None, html_content=None)
  Pillar 2: Evaluates Answer Engine Optimization including JSON-LD schema depth, sameAs authority graph connections, and direct-answer density.

• geo_readiness(url=None, html_content=None, embedding_backend="auto")
  Pillar 3: Generative Engine Optimization with RAG chunking simulation, semantic density, and Citation Survival Proxy (CSP).

• entity_analysis(url=None, html_content=None)
  Deep analysis of linked-data schemas (Organization, Person, Product, Article), entity disambiguation, and knowledge graph signals.

• citation_readiness(url=None, html_content=None, embedding_backend="auto")
  Evaluates fact density, source attribution, numeric verification anchors, and Citation Survival Proxy diagnostic factors.

Utilities & Capabilities

• generate_llms_txt(domain, title, description, key_sections=None)
  Generates a clean, spec-compliant /llms.txt file for generative engines and AI crawlers.

• get_capabilities()
  Returns supported pillars, available vector embedding backends, SAGE engine version, and active evidence taxonomy definitions.

Legacy & Backward Compatibility

• audit_geo_url(url, html_content) — Forwards to audit_html with deprecation notice.

• calculate_mavi_score(...) — Legacy manual-input MAVI calculator (marked mode: manual_override).

• measure_mavi(...) — 5-layer MAVI measurement engine with SAGE L1–L4 automation.

📊 Evidence & Agent Tool Integration

• MCP Client Demonstration Script: examples/mcp_client_example.py

• Standalone Offline JSON-RPC Demo: examples/public_demo/

• 5-Layer MAVI Integration Specification: docs/mavi-methodology.md

• Ecosystem Data Flow & Contracts: docs/BENCHMARK_ECOSYSTEM.md

• Cross-Repository Evidence Map: Ecosystem Evidence Flow

Evidence Taxonomy & CSP Diagnostics

Every finding and metric exposed by mcp-geo-server carries SAGE Evidence Taxonomy levels to guarantee transparency:

Citation Survival Proxy (CSP) findings are clearly exposed as diagnostic indices and proxy metrics—never misrepresented as uncalibrated citation probabilities.

🚀 Installation

# Install with uv (Recommended)
uv pip install mcp-geo-server

# Or with pip
pip install mcp-geo-server

⚙️ MCP Client Configuration

Claude Desktop

Add to claude_desktop_config.json:

{
  "mcpServers": {
    "geo-auditor": {
      "command": "uv",
      "args": [
        "run",
        "--package",
        "mcp-geo-server",
        "mcp-geo-server"
      ]
    }
  }
}

Cursor IDE

1. Open Settings -> Features -> MCP.

2. Click + Add New MCP Server.

3. Command: uv run --package mcp-geo-server mcp-geo-server

🐍 Python Usage

from mcp_geo_server.adapter import SageAdapter

adapter = SageAdapter()

# Full audit
result = adapter.audit_full(url="https://example.com")
print(f"Overall Score: {result['score']}/100 ({result['grade']})")

# Pillar 3: GEO & RAG Readiness
geo = adapter.geo_readiness(url="https://example.com")
print(f"CSP Diagnostic: {geo['metrics']['citation_survival_proxy']}")

🧪 Testing

uv run pytest -v

🏛️ Ecosystem

MCP GEO Server operates as the protocol integration component of the Molavi AI Visibility Stack:

• Discovery: AnswerPath GEO

• Measurement: GEO-Scope

• Diagnostics: SAGE Audit

• Action: SiteProbe

• Protocol: MCP GEO Server

📖 Runnable Python Client Example

Run the bundled MCP adapter example script:

python examples/mcp_client_example.py

💬 Community & External Collaboration

We welcome contributions to MCP tool definitions, AI agent bindings, and transport protocols:

• Discussions: GitHub Discussions

• First Contribution Guide: docs/FIRST_CONTRIBUTION.md

• MAVI Integration Specs: docs/mavi-methodology.md

• Report Issues: GitHub Issues

• Contribution Standards: CONTRIBUTING.md and SECURITY.md

📄 License & Author

Developed by Taghi Molavi — molavi.pro
MIT License — Copyright (c) 2026 Taghi Molavi