Skip to main content
Glama
deenesjakoruzh
egebese

SEO Research MCP

by egebese
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

DataSEO MCP

DataSEO MCP — это сервер протокола контекста модели (MCP) для практических SEO-исследований в ИИ-ассистентах. Он объединяет бесплатные SEO-данные Ahrefs, резервный механизм для провайдеров CAPTCHA и опциональное планирование запросов на базе OpenRouter в рамках небольшого набора инструментов MCP.

[!CAUTION] Этот проект предназначен только для образовательных и исследовательских целей. Он взаимодействует со сторонними сервисами, включая Ahrefs, CapSolver, Anti-Captcha и OpenRouter. Пользователи несут ответственность за соблюдение всех применимых условий использования.

Функции

Инструмент

Назначение

Пример запроса

get_backlinks_list

Обзор обратных ссылок и основные строки обратных ссылок

"Покажи обратные ссылки для suparank.io"

keyword_generator

Идеи ключевых слов и вопросов

"Найди SaaS SEO ключевые слова для онбординга"

get_traffic

Оценки органического трафика

"Оцени трафик для ege.md"

keyword_difficulty

KD и снимок SERP

"Проверь сложность для AI SEO tools"

ai_search_queries

Идеи ИИ-запросов по поисковому интенту

"Сгенерируй ИИ-запросы для SaaS SEO"

domain_overview

Сводка по обратным ссылкам и трафику для одного домена

"Сделай сводку по suparank.io"

compare_domains

Сравнение 2-5 доменов

"Сравни suparank.io и ahrefs.com"

backlink_opportunities

Пробелы в источниках обратных ссылок конкурентов

"Найди пробелы в обратных ссылках для моего домена"

seo_content_brief

SERP + ИИ-ассистированный бриф контента

"Создай контент-бриф для AI SEO аудита"

DataSEO MCP поддерживается Ege Bese. Для рабочих процессов AI SEO и отслеживания позиций см. Suparank.

Related MCP server: SEO Insights MCP Server

Установка

uvx --python 3.10 dataseo-mcp

Для локальной разработки:

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

Устаревшая команда seo-mcp по-прежнему доступна как псевдоним для совместимости.

Конфигурация

Для инструментов на базе Ahrefs требуется как минимум один провайдер CAPTCHA:

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

Если настроены оба, сначала используется CapSolver, а Anti-Captcha применяется в качестве резервного варианта. ИИ-инструменты являются опциональными:

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

Полезные переменные среды:

Переменная

По умолчанию

Назначение

DATASEO_CACHE_DIR

~/.cache/dataseo-mcp

Расположение кэша подписей

DATASEO_REQUEST_TIMEOUT

30

Тайм-аут HTTP в секундах

DATASEO_MAX_POLLING_ATTEMPTS

120

Лимит опроса CAPTCHA

OPENROUTER_BASE_URL

https://openrouter.ai/api/v1

ИИ-эндпоинт, совместимый с OpenAI

Настройка MCP

Конфигурация для 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

Конфигурация MCP для VS Code:

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

Требуется ключ только одного провайдера CAPTCHA. Добавляйте OPENROUTER_API_KEY только если вам нужна генерация запросов с помощью ИИ или контент-брифы.

Справочник API

get_backlinks_list(domain)

Возвращает:

{
  "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")

Возвращает идеи ключевых слов и вопросов в существующем формате label / value.

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

Возвращает историю трафика, сводку трафика, топовые страницы, страны и ключевые слова. Устаревшее поле costMontlyAvg сохранено, также включено costMonthlyAvg.

keyword_difficulty(keyword, country="us")

Возвращает оценку сложности ключевого слова и строки органической выдачи (SERP) с доступными метриками.

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

Возвращает дедуплицированные запросы, сгенерированные ИИ:

{
  "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 проверяется в диапазоне от 1 до 50. Допустимые интенты: informational, commercial, transactional и navigational.

Дополнительные инструменты

  • domain_overview(domain, country="None"): обзор обратных ссылок плюс сводка трафика для одного домена.

  • compare_domains(domains, country="None"): сравнивает 2-5 уникальных доменов.

  • backlink_opportunities(domain, competitors): перечисляет источники обратных ссылок конкурентов, отсутствующие в целевой выборке.

  • seo_content_brief(keyword, country="us", count=12, model, language): объединяет сложность ключевых слов, строки SERP, ИИ-запросы и рекомендуемые углы подачи контента.

Архитектура

server.py намеренно сделан легким. Реализация разделена на:

  • services.py: оркестрация инструментов MCP и публичные форматы возвращаемых данных.

  • schemas.py: валидация и нормализация Pydantic.

  • captcha.py: резервный механизм CapSolver / Anti-Captcha с ограниченным опросом.

  • backlinks.py, keywords.py, traffic.py: адаптеры эндпоинтов Ahrefs.

  • ai.py: генерация запросов, совместимая с OpenRouter/OpenAI.

  • cache.py: кэш JSON-подписей по умолчанию в ~/.cache/dataseo-mcp.

Все внешние HTTP-границы замоканы в тестах.

Разработка

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"

Устранение неполадок

Проблема

Решение

Не настроен провайдер CAPTCHA

Установите CAPSOLVER_API_KEY или ANTICAPTCHA_API_KEY

Ошибка решения CAPTCHA

Проверьте баланс провайдера, валидность ключа и лимиты

ИИ-инструмент возвращает ошибку отсутствующего ключа

Установите OPENROUTER_API_KEY

Пустой SEO-ответ

Домен или ключевое слово могут не индексироваться вышестоящим источником

Старая команда больше не документирована

Используйте dataseo-mcp; seo-mcp все еще работает как псевдоним

Лицензия

MIT с уведомлением об образовательном использовании. Атрибуция оригинального форка сохранена в LICENSE.

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

Related MCP Connectors

Related MCP Servers

  • SEO Research MCP

    iflow-mcp
    A
    license
    B
    quality
    D
    maintenance
    Enables AI-powered IDEs to perform SEO research including backlink analysis, keyword generation, traffic estimation, and keyword difficulty using Ahrefs data and CapSolver for CAPTCHA solving.
    4
    MIT

  • SEO Insights MCP Server

    mrgoonie
    A
    license
    Not graded
    quality
    F
    maintenance
    Connects AI assistants to SEO APIs for backlinks analysis, keyword research, and traffic analysis.
    9 npm
    28
    MIT

  • advanced-seo-mcp

    halilertekin
    A
    license
    A
    quality
    D
    maintenance
    Provides AI agents with professional-grade SEO capabilities including on-page analysis, technical audits, PageSpeed insights, and Ahrefs data integration.
    13
    11 npm
    5
    MIT

  • SEO Toolkit MCP

    QexleOSS
    A
    license
    Not graded
    quality
    B
    maintenance
    Enables SEO data retrieval including backlinks, keyword research, keyword difficulty, and traffic estimation using Ahrefs free tools.
    4 npm
    MIT