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Open-Source AI Visibility Measurement Engine & Provenance-Backed Benchmark Platform for Generative Engine Optimization (GEO). Enables Claude Desktop, Cursor, Antigravity, and AI Agents to run GEO audits & benchmarks directly.
GEO-Scope MCP server has significant definition quality gaps. Tool names lack clear action verbs (audit_ai_visibility, reverse_engineer_ranking_factors are vague). Descriptions are present but generic, many lack specificity about WHEN to use each tool or what distinguishes them. Input schemas are partially defined but incomplete: audit_ai_visibility and reverse_engineer_ranking_factors lack 'mode', 'models', 'seed' in visible schema (added dynamically in code). measure_mavi, verify_benchmark, and reproduce_benchmark schemas are incomplete or missing required field documentation. No output schemas documented. Error handling guidance absent. Parameters lack validation constraints (e.g., niche enum values not enforced in schema, prompt_count unbounded). Tool descriptions mention 'seeded simulation' and 'research priors' but do not explain when to call each tool vs. alternatives.
Audits a brand's visibility and Share of Model (SoM) across ChatGPT Search, Perplexity, Gemini, and Claude. Defaults to seeded simulation. Select live and explicit providers for actual inference. Factor weights are research priors, not fitted findings.
Generates a concrete, prioritized 5-pillar GEO optimization roadmap (BLUF method, tables, Reddit strategy, schema). Defaults to seeded simulation. Select live and explicit providers for actual inference. Factor weights are research priors, not fitted findings.
Measures the Molavi AI Visibility Index (MAVI) across L1-L5 layers (Technical Accessibility, Semantic Extractability, Entity Clarity, Citation Readiness, Observed AI Visibility). Computes measured multi-layer scores with partial normalization and zero score fabrication.
Cryptographically verifies a benchmark dataset and recomputes all metrics from raw observations.
Returns labeled platform-specific research priors (Reddit UGC, G2 reviews, PR, Schema, Freshness) for a target niche (does not fit proprietary neural weights). Defaults to seeded simulation. Select live and explicit providers for actual inference. Factor weights are research priors, not fitted findings.
Tool names lack clear action verbs. 'audit_ai_visibility' and 'reverse_engineer_ranking_factors' are vague and do not clearly signal what action occurs. LLMs struggle to infer intent from these names alone.
Input schemas incomplete or inferred. audit_ai_visibility and reverse_engineer_ranking_factors schemas visible in code lack 'mode', 'models', 'seed' parameters, these are added dynamically in a loop. measure_mavi schema is incomplete (missing required field indicators). No output schemas documented for any tool.
Parameter constraints missing. 'niche' accepts free-form strings but should be an enum (crm_sales, seo_marketing, project_management, ai_copywriting, ecommerce_platform). 'prompt_count' is unbounded (no min/max). 'models' array lacks validation. LLMs will hallucinate invalid values.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 39 | 2026-07-28+ | v2 |
Verifies SHA-256 cryptographic integrity and bit-level authenticity of a GEO-Scope benchmark dataset.
Descriptions lack differentiation and recovery guidance. All three main tools mention 'seeded simulation' and 'research priors' but do not explain when to call audit_ai_visibility vs. reverse_engineer_ranking_factors vs. generate_geo_playbook. No error handling guidance (e.g., 'If live mode fails, retry with simulate mode').
measure_mavi schema is incomplete. Parameters 'experiment_data', 'custom_weights', 'manual_layers' are objects with no property definitions. LLMs cannot infer what fields these objects should contain.