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SEO Research MCP

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Python
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DataSEO MCP

DataSEO MCP ist ein Model Context Protocol-Server für praktische SEO-Recherche in KI-Assistenten. Er kombiniert kostenlose SEO-Daten von Ahrefs, ein Fallback für CAPTCHA-Anbieter und optional eine über OpenRouter betriebene Abfrageplanung hinter einer kleinen Auswahl an MCP-Tools.

[!CAUTION] Dieses Projekt dient ausschließlich zu Bildungs- und Forschungszwecken. Es interagiert mit Drittanbietern, darunter Ahrefs, CapSolver, Anti-Captcha und OpenRouter. Die Benutzer sind dafür verantwortlich, alle geltenden Nutzungsbedingungen einzuhalten.

Funktionen

Tool

Zweck

Beispiel-Prompt

get_backlinks_list

Backlink-Übersicht und Top-Backlink-Zeilen

"Zeige Backlinks für suparank.io"

keyword_generator

Keyword- und Fragen-Ideen

"Finde SaaS-SEO-Keywords für Onboarding"

get_traffic

Schätzungen zum organischen Traffic

"Schätze Traffic für ege.md"

keyword_difficulty

KD und SERP-Snapshot

"Prüfe Schwierigkeit für AI SEO tools"

ai_search_queries

KI-Suchanfragen-Ideen nach Suchintention

"Generiere KI-Suchanfragen für SaaS-SEO"

domain_overview

Backlink- + Traffic-Zusammenfassung für eine Domain

"Fasse suparank.io zusammen"

compare_domains

Vergleich von 2-5 Domains

"Vergleiche suparank.io und ahrefs.com"

backlink_opportunities

Backlink-Quellenlücken bei Wettbewerbern

"Finde Backlink-Lücken für meine Domain"

seo_content_brief

SERP + KI-gestütztes Content-Briefing

"Erstelle ein Content-Briefing für AI SEO Audit"

DataSEO MCP wird von Ege Bese gepflegt. Für KI-SEO- und Rank-Tracking-Workflows siehe Suparank.

Related MCP server: SEO Insights MCP Server

Installation

uvx --python 3.10 dataseo-mcp

Für die lokale Entwicklung:

git clone https://github.com/egebese/dataseo-mcp.git
cd dataseo-mcp
uv sync
uv run dataseo-mcp

Der Legacy-Befehl seo-mcp ist weiterhin als Kompatibilitäts-Alias verfügbar.

Konfiguration

Für Ahrefs-basierte Tools ist mindestens ein CAPTCHA-Anbieter erforderlich:

export CAPSOLVER_API_KEY="your-capsolver-key"
# or
export ANTICAPTCHA_API_KEY="your-anticaptcha-key"

Wenn beide konfiguriert sind, wird zuerst CapSolver versucht und Anti-Captcha als Fallback verwendet. KI-Tools sind optional:

export OPENROUTER_API_KEY="your-openrouter-key"
export OPENROUTER_MODEL="openai/gpt-4o-mini"  # optional

Nützliche Runtime-Overrides:

Variable

Standard

Zweck

DATASEO_CACHE_DIR

~/.cache/dataseo-mcp

Speicherort des Signatur-Caches

DATASEO_REQUEST_TIMEOUT

30

HTTP-Timeout in Sekunden

DATASEO_MAX_POLLING_ATTEMPTS

120

Limit für CAPTCHA-Polling

OPENROUTER_BASE_URL

https://openrouter.ai/api/v1

OpenAI-kompatibler KI-Endpunkt

MCP-Einrichtung

Konfiguration für Claude Desktop / Cursor:

{
  "mcpServers": {
    "dataseo": {
      "command": "uvx",
      "args": ["--python", "3.10", "dataseo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_CAPSOLVER_KEY",
        "ANTICAPTCHA_API_KEY": "YOUR_ANTICAPTCHA_KEY",
        "OPENROUTER_API_KEY": "YOUR_OPENROUTER_KEY"
      }
    }
  }
}

Claude Code:

claude mcp add dataseo --scope user -- uvx --python 3.10 dataseo-mcp

VS Code MCP-Konfiguration:

{
  "servers": {
    "dataseo": {
      "command": "uvx",
      "args": ["--python", "3.10", "dataseo-mcp"],
      "env": {
        "CAPSOLVER_API_KEY": "YOUR_CAPSOLVER_KEY"
      }
    }
  }
}

Es ist nur ein API-Schlüssel für einen CAPTCHA-Anbieter erforderlich. Fügen Sie OPENROUTER_API_KEY nur hinzu, wenn Sie KI-gestützte Abfragegenerierung oder Content-Briefings wünschen.

API-Referenz

get_backlinks_list(domain)

Gibt zurück:

{
  "overview": {
    "domainRating": 76,
    "backlinks": 1500,
    "refDomains": 300
  },
  "backlinks": [
    {
      "anchor": "DataSEO MCP",
      "domainRating": 76,
      "title": "Example page",
      "urlFrom": "https://source.example/page",
      "urlTo": "https://example.com/page",
      "edu": false,
      "gov": false
    }
  ]
}

keyword_generator(keyword, country="us", search_engine="Google")

Gibt Keyword- und Fragen-Ideen im bestehenden label / value-Format zurück.

get_traffic(domain_or_url, country="None", mode="subdomains")

Gibt Traffic-Historie, Traffic-Zusammenfassung, Top-Seiten, Länder und Keywords zurück. Das Legacy-Feld costMontlyAvg bleibt erhalten und costMonthlyAvg ist ebenfalls enthalten.

keyword_difficulty(keyword, country="us")

Gibt einen Keyword-Difficulty-Score und organische SERP-Zeilen mit verfügbaren Metriken zurück.

ai_search_queries(keyword, count=10, model="openai/gpt-4o-mini", language="en")

Gibt deduplizierte, KI-generierte Suchanfragen zurück:

{
  "keyword": "ai seo audit",
  "queries": [
    {"query": "what is an AI SEO audit", "intent": "informational"},
    {"query": "best AI SEO audit tools", "intent": "commercial"}
  ],
  "model_used": "openai/gpt-4o-mini",
  "total_queries": 2
}

count wird von 1 bis 50 validiert. Gültige Intentionen sind informational, commercial, transactional und navigational.

Zusätzliche Tools

  • domain_overview(domain, country="None"): Backlink-Übersicht plus Traffic-Zusammenfassung für eine Domain.

  • compare_domains(domains, country="None"): vergleicht 2-5 eindeutige Domains.

  • backlink_opportunities(domain, competitors): listet Backlink-Quellen von Wettbewerbern auf, die in der Zielstichprobe nicht vorhanden sind.

  • seo_content_brief(keyword, country="us", count=12, model, language): kombiniert Keyword-Difficulty, SERP-Zeilen, KI-Suchanfragen und empfohlene Content-Ansätze.

Architektur

server.py ist bewusst schlank gehalten. Die Implementierung ist unterteilt in:

  • services.py: MCP-Tool-Orchestrierung und öffentliche Rückgabeformate.

  • schemas.py: Pydantic-Validierung und Normalisierung.

  • captcha.py: CapSolver / Anti-Captcha-Fallback mit begrenztem Polling.

  • backlinks.py, keywords.py, traffic.py: Ahrefs-Endpunkt-Adapter.

  • ai.py: OpenRouter/OpenAI-kompatible Abfragegenerierung.

  • cache.py: JSON-Signatur-Cache standardmäßig unter ~/.cache/dataseo-mcp.

Alle externen HTTP-Grenzen werden in Tests gemockt.

Entwicklung

uv sync
uv run pytest -q
uv run ruff check .
uv run python -m compileall -q src
uv run python -c "from seo_mcp.server import main"

Fehlerbehebung

Problem

Lösung

Kein CAPTCHA-Anbieter konfiguriert

Setze CAPSOLVER_API_KEY oder ANTICAPTCHA_API_KEY

CAPTCHA-Lösung fehlgeschlagen

Prüfe Guthaben des Anbieters, Gültigkeit des Schlüssels und Ratenlimits

KI-Tool gibt Fehler wegen fehlendem Schlüssel zurück

Setze OPENROUTER_API_KEY

Leere SEO-Antwort

Domain oder Keyword ist möglicherweise nicht in der Upstream-Quelle indexiert

Alter Befehl nicht mehr dokumentiert

Verwende dataseo-mcp; seo-mcp funktioniert weiterhin als Alias

Lizenz

MIT mit Hinweis zur Bildungsnutzung. Die ursprüngliche Fork-Attributierung bleibt in LICENSE erhalten.

Available Tools

4 tools
get_trafficC
Check the estimated search traffic for any website. 

Args:
    domain_or_url (str): The domain or URL to query
    country (str): The country to query, default is "None"
    mode (["subdomains", "exact"]): The mode to use for the query
Returns:
    Traffic data for the specified domain or URL
ParametersJSON Schema
NameRequiredDescriptionDefault
domain_or_urlYes
countryNoNone
modeNosubdomains

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are present, so the description carries the full burden. It does not disclose whether the tool is read-only, destructive, requires authentication, or has rate limits. For a data query tool, this is a significant gap.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is concise at two sentences plus an Args/Returns list. It is front-loaded with the purpose and structured for easy scanning. No wasted words.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no output schema, the description only vaguely states 'Traffic data' without specifying structure. Combined with missing behavioral context, the description is incomplete for a 3-parameter tool.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description includes an Args section that lists parameters and their types, adding some meaning beyond the schema (e.g., domain_or_url is 'The domain or URL to query'). However, it does not explain the difference between 'subdomains' and 'exact' modes, nor the effect of the country parameter, beyond default values.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'Check the estimated search traffic for any website', which clearly identifies the verb and resource. It distinguishes from sibling tools like get_backlinks_list and keyword_difficulty by focusing on traffic estimation, but does not explicitly differentiate.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides no guidance on when to use this tool versus alternatives, nor does it specify prerequisites or exclusions. It simply states the tool's function without context for decision-making.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

keyword_difficultyC

Get keyword difficulty for the specified keyword

ParametersJSON Schema
NameRequiredDescriptionDefault
keywordYes
countryNous

TDQS

C2.6/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description must carry full burden. It does not disclose how difficulty is calculated, what unit/scale is used, or any side effects. Bare minimum.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Single sentence of 8 words is concise but lacks substance. Appropriate length for the content, but more detail would improve informativeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and 2 params requiring explanation, the description is inadequate. Leaves agent guessing about return values, scope, and usage nuances.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema has 0% description coverage. Description only mentions 'specified keyword' without explaining its meaning or format. Country parameter and default are not explained. Adds minimal value to schema.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description 'Get keyword difficulty for the specified keyword' clearly states the action and resource. It is distinct from sibling tools like get_backlinks_list and get_traffic, though sibling differentiation is implicit rather than explicit.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool compared to alternatives. Lacks context about prerequisites or typical use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

keyword_generatorC

Get keyword ideas for the specified keyword

ParametersJSON Schema
NameRequiredDescriptionDefault
keywordYes
countryNous
search_engineNoGoogle

TDQS

C2.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided. Description only says 'get ideas' with no disclosure of behavior (e.g., return format, rate limits, data source).

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely short, no fluff, but lacks essential details. Conciseness is achieved at the expense of completeness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness1/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With 3 parameters, no output schema, and no annotations, the description is severely incomplete. No information on return values or side effects.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters1/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 0% and description only mentions the keyword parameter, ignoring country and search_engine. Fails to compensate for missing parameter descriptions.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states it returns keyword ideas for a given keyword. It distinguishes from sibling tools (backlinks, traffic, difficulty) but lacks specificity about the output format or scope.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool vs. alternatives, no prerequisites, no context about typical use cases.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 4 tool updatesv1.0.0
    • First observedget_backlinks_list
    • First observedget_traffic
    • First observedkeyword_difficulty
    • First observedkeyword_generator
Deploy Server
Shareable, User-Owned Context BackendPeople, applications, and agents read, write, and coordinate together in one context lake. Reach it over MCP, a CLI, a Python SDK, or REST.fulcradynamics.comAd

TDQS

B3.1/5.0

Scored across 4 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: get_backlinks_list retrieves backlink data, get_traffic provides traffic estimates, keyword_difficulty assesses keyword competitiveness, and keyword_generator suggests related keywords. There is no overlap in functionality, making tool selection unambiguous.

Naming Consistency5/5

All tools follow a consistent verb_noun pattern with snake_case: get_backlinks_list, get_traffic, keyword_difficulty, keyword_generator. The naming is predictable and readable, with no deviations in style or convention.

Tool Count4/5

With 4 tools, the count is reasonable for an SEO research server, covering key areas like backlinks, traffic, and keywords. It is slightly lean but not insufficient, as these tools address core SEO analysis needs without being overwhelming.

Completeness3/5

The toolset covers essential SEO research functions but has notable gaps. For example, there are no tools for competitor analysis, content suggestions, or SERP features, which are common in SEO workflows. Agents can perform basic research but may lack comprehensive coverage for advanced tasks.

Maintenance

ActivityInactive
ResponsivenessSlow

Resources

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