Un servidor del Protocolo de Contexto de Modelo (MCP) que proporciona acceso a todas las listas "awesome" curadas y sus elementos. Puede proporcionar los mejores recursos para tu agente a partir de secciones de las más de 8500 listas "awesome" en GitHub y más de 1 millón (y creciendo) de elementos "awesome".
¿Qué son las listas "Awesome"? Las listas "awesome" son colecciones seleccionadas por la comunidad de las mejores herramientas, bibliotecas y recursos sobre cualquier tema, desde marcos de trabajo de aprendizaje automático hasta herramientas de diseño. Al añadir este servidor MCP, tus agentes de IA obtienen acceso instantáneo a estos recursos de alta calidad y verificados, en lugar de depender de búsquedas web aleatorias.
Perfecto para:
Agentes de trabajadores del conocimiento para obtener las referencias más relevantes para su trabajo.
La fuente de los mejores recursos de aprendizaje.
La investigación profunda puede recopilar rápidamente una gran cantidad de recursos de alta calidad para cualquier tema.
Agentes de búsqueda.
https://github.com/user-attachments/assets/babab991-e4ff-4433-bdb7-eb7032e9cd11
Modo | Instalación | Ideal para |
Servidor MCP | apunta tu agente a la URL alojada o ejecuta | Claude Desktop, Cursor, Windsurf, VS Code — agentes que hablan MCP de forma nativa |
CLI |
| Scripts, flujos de trabajo de shell, editores sin soporte MCP, trabajos de CI |
Ambos modos se distribuyen desde el mismo paquete npm (context-awesome) y acceden al mismo backend alojado.
Related MCP server: agent101-mcp
Cada herramienta MCP tiene un subcomando CLI 1:1; el servidor y la CLI exponen las mismas operaciones.
Herramienta | Equivalente CLI | Qué hace |
|
| Descubre secciones/categorías en listas "awesome" que coincidan con una consulta |
|
| Búsqueda de texto completo en elementos individuales (herramientas/bibliotecas/recursos) |
|
| Obtiene elementos de una lista + sección conocida, con presupuesto de tokens |
La CLI (context-awesome) se comunica directamente con el backend alojado. Para el servidor MCP, utiliza el binario independiente context-awesome-mcp (consulta Instalación — Clientes MCP a continuación).
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)npm install -g context-awesome
context-awesome --help
context-awesome search "rate limiter"
context-awesome sections "graph databases"npx context-awesome search "vector database"Context Awesome está disponible como un servidor MCP alojado. No requiere instalación.
Ve a: 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/mcpSettings → Connectors → Add Custom Connector.
Nombre: Context Awesome
URL: https://www.context-awesome.com/api/mcp
Utiliza la misma URL (https://www.context-awesome.com/api/mcp) con la interfaz de "add remote MCP" de cada cliente. Consulta las secciones dedicadas a continuación para obtener los fragmentos exactos.
{
"mcpServers": {
"context-awesome": {
"command": "npx",
"args": ["-y", "context-awesome-mcp", "serve", "--transport", "stdio"],
"env": {
"CONTEXT_AWESOME_API_HOST": "https://api.context-awesome.com"
}
}
}
}npx context-awesome-mcp serve --transport http --port 3001
# then point your client at http://localhost:3001/mcpgit 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 inspectorEste servidor MCP y la CLI se conectan a un servicio de API backend que maneja el trabajo pesado del procesamiento de listas "awesome".
El servicio backend será de código abierto pronto, permitiendo a la comunidad contribuir y beneficiarse del ecosistema completo de context-awesome.
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}{
"context_servers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}Haz clic en el menú de hamburguesa
Selecciona Settings
Navega a Tools
Haz clic en + Add MCP
Introduce la URL: https://www.context-awesome.com/api/mcp
Nombre: 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
}
}Ve a Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
Haz clic en + Add
Configura la URL: https://www.context-awesome.com/api/mcp
Haz clic en OK y Apply
Navega a Kiro > MCP Servers
Haz clic en + Add
Configura la URL: https://www.context-awesome.com/api/mcp
Haz clic en Save
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}Navega a Settings > AI > Manage MCP servers
Haz clic en + Add
Configura la URL: https://www.context-awesome.com/api/mcp
Haz clic en Save
{
"mcpServers": {
"context-awesome": {
"type": "http",
"url": "https://www.context-awesome.com/api/mcp",
"tools": ["find_awesome_section", "search_awesome_items", "get_awesome_items"]
}
}
}Navega a Program > Install > Edit mcp.json
Añade:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}Navega a Perplexity > Settings
Selecciona Connectors
Haz clic en Add Connector
Selecciona Advanced
Introduce el nombre: Context Awesome
Introduce la 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 mcpLuego añade:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}Ve al menú de Zencoder (...)
Selecciona Agent tools
Haz clic en Add custom MCP
Nombre: Context Awesome
URL: https://www.context-awesome.com/api/mcp
Abre el panel de chat de Qodo Gen
Haz clic en Connect more tools
Haz clic en + Add new MCP
Añade:
{
"mcpServers": {
"context-awesome": {
"url": "https://www.context-awesome.com/api/mcp"
}
}
}MIT
¡Las contribuciones son bienvenidas! Por favor:
Haz un fork del repositorio
Crea una rama de funcionalidad
Añade pruebas para la nueva funcionalidad
Asegúrate de que todas las pruebas pasen
Envía una solicitud de extracción (pull request)
Para problemas y preguntas:
GitHub Issues: https://github.com/bh-rat/context-awesome/issues
Este proyecto utiliza datos de más de 8.500 listas "awesome" en GitHub. Consulta ATTRIBUTION.md para obtener una lista completa de todos los repositorios cuyos datos están incluidos.
Creado con:
Inspirado en los patrones de servidor MCP de context7
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.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | Search terms for finding sections across awesome lists | |
| confidence | No | Minimum confidence score (0-1) | |
| limit | No | Maximum sections to return |
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.
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.
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.
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.
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.
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.
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.
| Name | Required | Description | Default |
|---|---|---|---|
| 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 |
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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