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context-awesome

Name: context-awesome
Author: bh-rat

by bh-rat

Overview Schema Related Servers Score Discussions

TypeScript

Hybrid

context-awesome : awesome-ссылки для ваших агентов

MCP Server

Сервер протокола контекста модели (MCP), предоставляющий доступ ко всем курируемым спискам awesome и их элементам. Он может предоставить вашему агенту лучшие ресурсы из разделов более чем 8500 списков awesome на GitHub и более 1 миллиона (число растет) элементов awesome.

Что такое списки Awesome? Списки Awesome — это курируемые сообществом коллекции лучших инструментов, библиотек и ресурсов по любой теме — от фреймворков машинного обучения до инструментов дизайна. Добавив этот MCP-сервер, ваши ИИ-агенты получают мгновенный доступ к этим высококачественным, проверенным ресурсам вместо того, чтобы полагаться на случайные веб-поиски.

Идеально подходит для:

Агентов интеллектуальных работников для получения наиболее релевантных ссылок для их работы
Источника лучших учебных ресурсов
Глубоких исследований, позволяющих быстро собрать множество высококачественных ресурсов по любой теме.
Поисковых агентов

https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11

Два способа использования Context Awesome

Режим	Установка	Подходит для
MCP Server	укажите агенту размещенный URL или запустите `context-awesome-mcp`	Claude Desktop, Cursor, Windsurf, VS Code — агенты, которые нативно поддерживают MCP
CLI	`npm install -g context-awesome`	Скриптов, рабочих процессов в оболочке, редакторов без поддержки MCP, CI-задач

Оба режима поставляются из одного и того же npm-пакета (context-awesome) и обращаются к одному и тому же размещенному бэкенду.

Related MCP server: agent101-mcp

Инструменты MCP

Каждый инструмент MCP имеет соответствующую подкоманду CLI — сервер и CLI предоставляют одни и те же операции.

Инструмент	Эквивалент CLI	Что он делает
`find_awesome_section`	`context-awesome sections <query...>`	Поиск разделов/категорий в списках awesome, соответствующих запросу
`search_awesome_items`	`context-awesome search <query...>`	Полнотекстовый поиск по отдельным элементам (инструменты/библиотеки/ресурсы)
`get_awesome_items`	`context-awesome items <target>`	Получение элементов из известного списка + раздела, с учетом лимита токенов

Команды CLI

CLI (context-awesome) напрямую взаимодействует с размещенным бэкендом. Для MCP-сервера используйте отдельный бинарный файл context-awesome-mcp (см. Установка — MCP-клиенты ниже).

context-awesome <command> [options]

Commands:
  sections <query...>        Find sections matching a query
  search <query...>          Search items (e.g., context-awesome search "postgres orm")
  items <target>             Fetch items from a list (by owner/repo or listId)

Globals:
  --api-host <url>           Backend API host (env: CONTEXT_AWESOME_API_HOST)
  --api-key <key>            API key (env: CONTEXT_AWESOME_API_KEY)
  --json                     Emit raw JSON (for scripts)

Установка CLI

npm install -g context-awesome
context-awesome --help
context-awesome search "rate limiter"
context-awesome sections "graph databases"

Использование CLI без установки

npx context-awesome search "vector database"

Установка — MCP-клиенты

Удаленный сервер (рекомендуется)

Context Awesome доступен как размещенный MCP-сервер. Установка не требуется.

Перейдите в: Settings → Cursor Settings → MCP → Add new global MCP server

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

claude mcp add --transport http context-awesome https://www.context-awesome.com/api/mcp

Settings → Connectors → Add Custom Connector.

Имя: Context Awesome
URL: https://www.context-awesome.com/api/mcp

Используйте тот же URL (https://www.context-awesome.com/api/mcp) в интерфейсе "add remote MCP" каждого клиента. См. соответствующие разделы ниже для получения точных фрагментов кода.

Локальный stdio (Claude Desktop, с поддержкой офлайн-режима)

{
  "mcpServers": {
    "context-awesome": {
      "command": "npx",
      "args": ["-y", "context-awesome-mcp", "serve", "--transport", "stdio"],
      "env": {
        "CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com"
      }
    }
  }
}

Локальный HTTP-транспорт (для пользовательских интеграций)

npx context-awesome-mcp serve --transport http --port 3001
# then point your client at http://localhost:3001/mcp

Локальная разработка

git clone https://github.com/bh-rat/context-awesome.git
cd context-awesome
npm install
npm run build

# CLI
./build/cli.js search "graph databases"

# MCP server (stdio)
./build/index.js --transport stdio

# MCP Inspector
npm run inspector

Бэкенд-сервис

Этот MCP-сервер и CLI подключаются к бэкенд-сервису API, который берет на себя всю тяжелую работу по обработке списков awesome.

Бэкенд-сервис скоро будет переведен в открытый доступ, что позволит сообществу вносить свой вклад и пользоваться преимуществами всей экосистемы context-awesome.

Дополнительные методы установки

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

{
  "context_servers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

Нажмите на меню-гамбургер
Выберите Settings
Перейдите в Tools
Нажмите + Add MCP
Введите URL: https://www.context-awesome.com/api/mcp
Имя: Context Awesome

{
  "mcpServers": {
    "context-awesome": {
      "type": "streamable-http",
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

{
  "mcpServers": {
    "context-awesome": {
      "httpUrl": "https://www.context-awesome.com/api/mcp"
    }
  }
}

"mcp": {
  "context-awesome": {
    "type": "remote",
    "url": "https://www.context-awesome.com/api/mcp",
    "enabled": true
  }
}

Перейдите в Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
Нажмите + Add
Настройте URL: https://www.context-awesome.com/api/mcp
Нажмите OK и Apply

Перейдите в Kiro > MCP Servers
Нажмите + Add
Настройте URL: https://www.context-awesome.com/api/mcp
Нажмите Save

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

Перейдите в Settings > AI > Manage MCP servers
Нажмите + Add
Настройте URL: https://www.context-awesome.com/api/mcp
Нажмите Save

{
  "mcpServers": {
    "context-awesome": {
      "type": "http",
      "url": "https://www.context-awesome.com/api/mcp",
      "tools": ["find_awesome_section", "search_awesome_items", "get_awesome_items"]
    }
  }
}

Перейдите в Program > Install > Edit mcp.json
Добавьте:

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

Перейдите в Perplexity > Settings
Выберите Connectors
Нажмите Add Connector
Выберите Advanced
Введите имя: Context Awesome
Введите URL: https://www.context-awesome.com/api/mcp

{
  "inputs": [],
  "servers": {
    "context-awesome": {
      "type": "http",
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

{
  "$schema": "https://charm.land/crush.json",
  "mcp": {
    "context-awesome": {
      "type": "http",
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

acli rovodev mcp

Затем добавьте:

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

Перейдите в меню Zencoder (...)
Выберите Agent tools
Нажмите Add custom MCP
Имя: Context Awesome
URL: https://www.context-awesome.com/api/mcp

Откройте панель чата Qodo Gen
Нажмите Connect more tools
Нажмите + Add new MCP
Добавьте:

{
  "mcpServers": {
    "context-awesome": {
      "url": "https://www.context-awesome.com/api/mcp"
    }
  }
}

Лицензия

MIT

Вклад в проект

Вклад приветствуется! Пожалуйста:

Сделайте форк репозитория
Создайте ветку для новой функции
Добавьте тесты для новой функциональности
Убедитесь, что все тесты проходят
Отправьте pull request

Поддержка

По вопросам и проблемам:

GitHub Issues: https://github.com/bh-rat/context-awesome/issues

Атрибуция

Этот проект использует данные из более чем 8500 списков awesome на GitHub. См. ATTRIBUTION.md для получения полного списка всех репозиториев, данные которых включены.

Авторы

Создано с использованием:

Model Context Protocol SDK
Awesome Lists
Вдохновлено паттернами MCP-сервера context7

Available Tools

2 tools

find_awesome_sectionFind Awesome List SectionAInspect

Discovers sections/categories across awesome lists matching a search query and returns matching sections from awesome lists.

You MUST call this function before 'get_awesome_items' to discover available sections UNLESS the user explicitly provides a githubRepo or listId.

Selection Process:

Analyze the query to understand what type of resources the user is looking for
Return the most relevant matches based on:
- Name similarity to the query and the awesome lists section
- Category/section relevance of the awesome lists
- Number of items in the section
- Confidence score

Response Format:

Returns matching sections of the awesome lists with metadata
Includes repository information, item counts, and confidence score
Use the githubRepo or listId with relevant sections from results for get_awesome_items

For ambiguous queries, multiple relevant sections will be returned for the user to choose from.

ParametersJSON Schema

Name	Required	Description
`query`	Yes	Search terms for finding sections across awesome lists
`confidence`	No	Minimum confidence score (0-1)
`limit`	No	Maximum sections to return

TDQS

A4.3/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes the tool's behavior: the selection process (4 criteria), response format (metadata included), and handling of ambiguous queries (returns multiple sections). It doesn't mention rate limits, authentication needs, or error conditions, but provides substantial operational context.

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 well-structured with clear sections: purpose statement, usage requirement, selection process, response format, and handling of ambiguous queries. While comprehensive, some sentences could be more concise (e.g., the selection process could be bulleted more efficiently). Overall, it's appropriately sized for the tool's complexity.

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

Completeness4/5

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

Given no annotations and no output schema, the description provides substantial context: purpose, usage rules, selection algorithm, response format, and relationship to sibling tool. It doesn't explicitly describe the exact structure of returned metadata or error cases, but covers most essential aspects for a search/discovery 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?

Schema description coverage is 100%, so the schema already documents all parameters thoroughly. The description doesn't add any parameter-specific information beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting for parameter documentation.

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

Purpose5/5

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

The description clearly states the tool's purpose: 'Discovers sections/categories across awesome lists matching a search query and returns matching sections from awesome lists.' It specifies the verb ('discovers'), resource ('sections/categories across awesome lists'), and distinguishes it from its sibling 'get_awesome_items' by explaining this tool is for discovering sections before retrieving items.

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

Usage Guidelines5/5

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

The description provides explicit usage guidance: 'You MUST call this function before 'get_awesome_items' to discover available sections UNLESS the user explicitly provides a githubRepo or listId.' It clearly states when to use this tool versus its sibling and includes conditions for when it's not needed.

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

get_awesome_itemsGet Awesome List ItemsAInspect

Retrieves items from a specific awesome list or section with token limiting. You must call 'find_awesome_section' first to discover available sections, UNLESS the user explicitly provides a githubRepo or listId.

ParametersJSON Schema

Name	Required	Description
`listId`	No	UUID of the list (from find_awesome_section results)
`githubRepo`	No	GitHub repo path (e.g., 'sindresorhus/awesome') from find_awesome_section results
`section`	No	Category/section name to filter
`subcategory`	No	Subcategory to filter
`tokens`	No	Maximum number of tokens to return (default: 10000). Higher values provide more items but consume more tokens.
`offset`	No	Pagination offset for retrieving more items

TDQS

A4.4/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: the token limiting mechanism ('with token limiting'), the dependency on another tool ('call 'find_awesome_section' first'), and the conditional logic for parameters. However, it doesn't mention error handling, rate limits, or authentication needs, which are common gaps for retrieval tools.

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

Conciseness5/5

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

The description is perfectly concise with two sentences that each serve distinct purposes: the first states the core functionality with a key constraint, and the second provides essential usage guidance. There is no wasted language, and information is front-loaded effectively.

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

Completeness4/5

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

Given the complexity (6 parameters, dependency on another tool) and lack of annotations/output schema, the description does well by covering purpose, usage guidelines, and key behavioral aspects. However, it doesn't describe the return format (e.g., structure of items, pagination details), which would be helpful since there's no output schema, leaving some gaps in completeness.

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?

Schema description coverage is 100%, so the schema already documents all 6 parameters thoroughly. The description adds minimal parameter semantics beyond the schema, only implying that 'githubRepo' and 'listId' come from 'find_awesome_section' results. This meets the baseline of 3 when schema coverage is high, but doesn't provide significant additional value.

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

Purpose5/5

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

The description clearly states the verb 'retrieves' and the resource 'items from a specific awesome list or section', specifying the action and target. It distinguishes from the sibling tool 'find_awesome_section' by indicating this tool is for retrieving items after sections are identified, establishing a clear functional relationship.

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

Usage Guidelines5/5

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

The description explicitly states when to use this tool: 'You must call 'find_awesome_section' first to discover available sections, UNLESS the user explicitly provides a githubRepo or listId.' This provides clear prerequisites and alternatives, directly addressing the sibling tool relationship and user input scenarios.

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

Install Server

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TDQS

A4.2/5.0

Disambiguation5/5

The two tools have clearly distinct purposes: 'find_awesome_section' discovers sections/categories across awesome lists based on a search query, while 'get_awesome_items' retrieves actual items from a specific list or section. There is no overlap in functionality—one is for discovery and the other for retrieval, making them perfectly distinguishable.

Naming Consistency5/5

Both tools follow a consistent verb_noun pattern with snake_case: 'find_awesome_section' and 'get_awesome_items'. The naming is predictable and readable, with 'find' and 'get' as appropriate verbs for their respective actions, maintaining uniformity throughout the set.

Tool Count3/5

With only 2 tools, the server feels thin for its apparent purpose of interacting with awesome lists. While the tools cover discovery and retrieval, typical operations like creating, updating, or deleting items are missing, suggesting the scope might be limited or incomplete. A count of 2 is borderline for a functional server in this domain.

Completeness2/5

The tool surface is significantly incomplete for the domain of awesome list management. It only supports discovery and retrieval, lacking any CRUD operations such as adding, updating, or removing items or sections. This will likely cause agent failures when full lifecycle management is needed, as there are obvious gaps in coverage.

Maintenance

ActivityInactive

ResponsivenessSyncing

Resources

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context-awesome : awesome-ссылки для ваших агентов

Два способа использования Context Awesome

Инструменты MCP

Команды CLI

Установка CLI

Использование CLI без установки

Установка — MCP-клиенты

Удаленный сервер (рекомендуется)

Локальный stdio (Claude Desktop, с поддержкой офлайн-режима)

Локальный HTTP-транспорт (для пользовательских интеграций)

Локальная разработка

Бэкенд-сервис

Дополнительные методы установки

Лицензия

Вклад в проект

Поддержка

Атрибуция

Авторы

Available Tools

TDQS

TDQS

TDQS

Maintenance

Resources

Looking for Admin?

Related MCP Connectors

Related MCP Servers

Unified Docs Hub

agent101-mcp

toolradar-mcp

ES Hub Signals MCP

Latest Blog Posts

MCP directory API