What SEO Metrics Does Open‑SEO Track? A Complete Guide to Every Data Point

Open‑SEO tracks eight categories of SEO metrics spanning keyword research, site audits, backlink analysis, domain overview, rank tracking, saved keywords, AI brand visibility, and AI search prompt exploration—all powered by DataForSEO's API via an extensible MCP tool architecture.

The open-source Open‑SEO platform (every-app/open-seo) aggregates a comprehensive suite of search-engine-optimization metrics across its six core features. These metrics are defined centrally in the UI configuration and populated at runtime by a collection of DataForSEO MCP (Model Context Protocol) tools that standardize data retrieval across the stack.

Keyword Research Metrics

Open‑SEO's keyword research feature surfaces traditional search-intelligence data. According to the source code in web/src/lib/feature-pages.ts (lines 81-86), the metrics include:

• Search volume – monthly query frequency
• Keyword difficulty (KD) – competitiveness score
• CPC – cost-per-click advertising data
• SERP results – competitive landscape overview

The get_keyword_metrics MCP tool hydrates up to 700 keywords per call with volume, KD, intent, CPC, competition, and optional monthly trending data. The implementation lives in src/server/mcp/tools/dataforseo-research-tools.ts, which delegates to fetchKeywordMetricsForList in src/server/lib/dataforseo/keyword-metrics.ts (lines 71-86).

// Example: ask the MCP for metrics on a list of keywords
import { callMcpTool } from '@/client/mcp';

async function getMetrics() {
  const response = await callMcpTool('get_keyword_metrics', {
    projectId: 'proj_123',
    keywords: ['open source SEO', 'keyword research tool'],
    includeClickstreamData: true,
    includeMonthlyTrends: false,
    sortBy: 'search_volume',
  });

  // Structured rows available via response.structuredContent.keywords
  console.log('Metrics table:\n', response.text);
}

Site Audit Metrics

The site audit feature captures technical SEO health signals. Per feature-pages.ts (lines 92-97), tracked metrics include:

• Crawled URLs – total pages discovered
• Page fields – titles, meta descriptions, headings
• Affected pages – URLs with identified issues
• Audit history – temporal change tracking

Data flows through two paths: crawl jobs store page-level signals in the database, while Lighthouse performance data is injected via the lighthouse server function (see src/server/lib/lighthouseStoredPayload.ts).

Backlink Profile Metrics

Backlinks are tracked via feature-pages.ts (lines 64-70) with these dimensions:

• Backlinks – total inbound link count
• Referring domains – unique source sites
• Target URLs – specific destination pages
• Rank & spam signals – quality indicators

The get_backlinks_profile and get_backlinks_overview MCP tools call DataForSEO's backlink endpoints. Implementation resides in src/server/mcp/tools/dataforseo-research-tools.ts.

Domain Overview Metrics

For competitive intelligence, domain overview (lines 41-46 in feature-pages.ts) tracks:

• Organic traffic – estimated monthly visits
• Organic keywords – ranking term count
• Top keywords – highest-value ranking queries
• Top pages – most visible URLs

The get_domain_overview MCP tool queries DataForSEO Labs for these estimates.

Rank Tracking Metrics

The rank tracking feature (lines 50-55) monitors positional performance:

• Desktop rank – search position on desktop
• Mobile rank – search position on mobile
• SERP features – presence in rich results
• Position change – historical delta

get_rank_tracker stores periodic checks, with refresh logic in src/server/functions/rank-tracking.ts and full service implementation in src/server/features/rank-tracking/services/RankTrackingService.ts.

Saved Keywords Metrics

For saved keywords (lines 98-103), the UI displays:

• Saved ideas – bookmarked research terms
• Tags – user-defined categorization
• Volume – last known search frequency
• Difficulty – last known KD score

These metrics persist from the initial get_keyword_metrics hydration call.

AI Brand Visibility Metrics

Open‑SEO uniquely tracks AI brand visibility (lines 70-75) with:

• Mentions – brand appearances in AI responses
• Citations – referenced sources
• Platforms – which AI systems surface the brand
• Cited domains – competitor citation analysis

The get_ai_brand_visibility MCP tool queries DataForSEO's AI-optimization endpoints.

AI Search Prompt Explorer Metrics

The AI search prompt explorer (lines 76-81) captures:

• Prompts – tested query variations
• Web context – sources cited in responses
• Search-country – geographic settings
• Brand mentions – visibility in AI answers

The ai_search_prompts MCP tool executes identical prompts across supported models and returns citation URLs for comparison.

Architectural Flow: How Metrics Propagate

Understanding how Open‑SEO metrics flow from definition to display requires tracing three layers:

1. Metric definitions – Static labels and UI grouping in web/src/lib/feature-pages.ts; each feature's metrics array drives components/feature-page.tsx

2. MCP tool layer – Server-side tools expose raw DataForSEO calls with standardized transforms (snake_case via toMcpKeywordMetricRow)

3. Data retrieval – Client creation through createDataforseoClient, with optional clickstream enrichment and configurable sortBy parameters

For direct server-side access without the MCP abstraction:

import { createDataforseoClient } from '@/server/lib/dataforseo';
import { fetchKeywordMetricsForList } from '@/server/lib/dataforseo/keyword-metrics';

async function fetchDirectly() {
  const client = createDataforseoClient({ /* billing context */ });
  const metrics = await fetchKeywordMetricsForList(client, {
    keywords: ['site audit', 'backlink analysis'],
    locationCode: 2840,           // US
    languageCode: 'en',
    creditFeature: 'keyword_research',
  });
  console.log(metrics); // array of MetricRow objects
}

Summary

• Eight metric categories cover traditional SEO (keywords, backlinks, ranks), technical health (site audits), competitive intelligence (domain overview), and emerging AI-search visibility
• Centralized configuration in web/src/lib/feature-pages.ts defines all metric labels and groupings
• MCP tool architecture standardizes DataForSEO API access across dataforseo-research-tools.ts and related server modules
• Dual output format returns both human-readable tables and structured JSON for programmatic consumption
• Extensible design allows new metric dimensions by adding feature definitions and corresponding MCP tools

Frequently Asked Questions

What data provider powers Open‑SEO's metrics?

All metrics originate from DataForSEO's API, accessed through a custom MCP tool layer that standardizes authentication, transforms, and caching. The createDataforseoClient factory manages credential context and rate limiting.

Can I export raw metric data from Open‑SEO?

Yes. MCP tool responses include structuredContent with typed arrays (e.g., keywords: MetricRow[]). For direct programmatic access, use server functions like fetchKeywordMetricsForList which return native TypeScript objects.

How does Open‑SEO track AI-specific SEO metrics?

Two dedicated features monitor AI-search visibility: get_ai_brand_visibility tracks brand mentions across AI platforms, while ai_search_prompts executes controlled prompt testing across multiple models and captures their citation behavior.

Where are metric definitions stored if I want to customize the dashboard?

Edit web/src/lib/feature-pages.ts to modify metric labels, groupings, or add new features. The feature-page.tsx component automatically renders any feature with a valid metrics array declaration.

{
  "mcpServers": {
    "instagit": {
      "command": "npx",
      "args": ["-y", "instagit@latest"]
    }
  }
}