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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-Referenzen für Ihre Agenten

MCP Server

Ein Model Context Protocol (MCP) Server, der Zugriff auf alle kuratierten Awesome-Listen und deren Einträge bietet. Er kann Ihrem Agenten die besten Ressourcen aus Abschnitten der über 8.500 Awesome-Listen auf GitHub und mehr als 1 Million (wachsend) Awesome-Listeneinträgen bereitstellen.

Was sind Awesome-Listen? Awesome-Listen sind von der Community kuratierte Sammlungen der besten Tools, Bibliotheken und Ressourcen zu jedem Thema – von Machine-Learning-Frameworks bis hin zu Design-Tools. Durch das Hinzufügen dieses MCP-Servers erhalten Ihre KI-Agenten sofortigen Zugriff auf diese hochwertigen, geprüften Ressourcen, anstatt sich auf zufällige Websuchen verlassen zu müssen.

Perfekt für:

Wissensarbeiter-Agenten, um die relevantesten Referenzen für ihre Arbeit zu erhalten
Die Quelle für die besten Lernressourcen
Tiefgehende Recherchen, um schnell eine Vielzahl hochwertiger Ressourcen zu jedem Thema zu sammeln.
Such-Agenten

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

Zwei Möglichkeiten, Context Awesome zu nutzen

Modus	Installation	Gut geeignet für
MCP Server	Verweisen Sie Ihren Agenten auf die gehostete URL oder starten Sie `context-awesome-mcp`	Claude Desktop, Cursor, Windsurf, VS Code — Agenten, die nativ MCP sprechen
CLI	`npm install -g context-awesome`	Skripte, Shell-Workflows, Editoren ohne MCP-Unterstützung, CI-Jobs

Beide Modi stammen aus demselben npm-Paket (context-awesome) und greifen auf dasselbe gehostete Backend zu.

Related MCP server: agent101-mcp

MCP-Tools

Jedes MCP-Tool hat ein 1:1 CLI-Unterkommando – der Server und die CLI bieten dieselben Operationen.

Tool	CLI-Äquivalent	Was es tut
`find_awesome_section`	`context-awesome sections <query...>`	Entdecken Sie Abschnitte/Kategorien in Awesome-Listen, die einer Suchanfrage entsprechen
`search_awesome_items`	`context-awesome search <query...>`	Volltextsuche über einzelne Einträge (Tools/Bibliotheken/Ressourcen)
`get_awesome_items`	`context-awesome items <target>`	Abrufen von Einträgen aus einer bekannten Liste + Abschnitt, token-budgetiert

CLI-Befehle

Die CLI (context-awesome) kommuniziert direkt mit dem gehosteten Backend. Für den MCP-Server verwenden Sie das separate context-awesome-mcp-Binary (siehe Installation — MCP-Clients unten).

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)

Installieren der CLI

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

Verwenden der CLI ohne Installation

npx context-awesome search "vector database"

Installation — MCP-Clients

Remote-Server (Empfohlen)

Context Awesome ist als gehosteter MCP-Server verfügbar. Keine Installation erforderlich.

Gehen Sie zu: 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.

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

Verwenden Sie dieselbe URL (https://www.context-awesome.com/api/mcp) mit der Benutzeroberfläche für "Remote-MCP hinzufügen" des jeweiligen Clients. Siehe die dedizierten Abschnitte unten für exakte Snippets.

Lokales stdio (Claude Desktop, offline-fähig)

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

Lokaler HTTP-Transport (für benutzerdefinierte Integrationen)

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

Lokale Entwicklung

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

Backend-Service

Dieser MCP-Server und die CLI verbinden sich mit einem Backend-API-Service, der die Schwerstarbeit der Awesome-Listen-Verarbeitung übernimmt.

Der Backend-Service wird bald als Open Source veröffentlicht, was es der Community ermöglicht, zum vollständigen context-awesome-Ökosystem beizutragen und davon zu profitieren.

Zusätzliche Installationsmethoden

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

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

Klicken Sie auf das Hamburger-Menü
Wählen Sie Settings
Navigieren Sie zu Tools
Klicken Sie auf + Add MCP
Geben Sie die URL ein: https://www.context-awesome.com/api/mcp
Name: 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
  }
}

Gehen Sie zu Settings -> Tools -> AI Assistant -> Model Context Protocol (MCP)
Klicken Sie auf + Add
Konfigurieren Sie die URL: https://www.context-awesome.com/api/mcp
Klicken Sie auf OK und Apply

Navigieren Sie zu Kiro > MCP Servers
Klicken Sie auf + Add
Konfigurieren Sie die URL: https://www.context-awesome.com/api/mcp
Klicken Sie auf Save

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

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

Navigieren Sie zu Settings > AI > Manage MCP servers
Klicken Sie auf + Add
Konfigurieren Sie die URL: https://www.context-awesome.com/api/mcp
Klicken Sie auf Save

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

Navigieren Sie zu Program > Install > Edit mcp.json
Fügen Sie hinzu:

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

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

Navigieren Sie zu Perplexity > Settings
Wählen Sie Connectors
Klicken Sie auf Add Connector
Wählen Sie Advanced
Geben Sie den Namen ein: Context Awesome
Geben Sie die URL ein: 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

Dann fügen Sie hinzu:

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

Gehen Sie zum Zencoder-Menü (...)
Wählen Sie Agent tools
Klicken Sie auf Add custom MCP
Name: Context Awesome
URL: https://www.context-awesome.com/api/mcp

Öffnen Sie das Qodo Gen-Chat-Panel
Klicken Sie auf Connect more tools
Klicken Sie auf + Add new MCP
Fügen Sie hinzu:

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

Lizenz

MIT

Mitwirken

Beiträge sind willkommen! Bitte:

Forken Sie das Repository
Erstellen Sie einen Feature-Branch
Fügen Sie Tests für neue Funktionen hinzu
Stellen Sie sicher, dass alle Tests bestehen
Senden Sie einen Pull Request

Support

Bei Problemen und Fragen:

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

Namensnennung

Dieses Projekt verwendet Daten aus über 8.500 Awesome-Listen auf GitHub. Siehe ATTRIBUTION.md für eine vollständige Liste aller Repositories, deren Daten enthalten sind.

Credits

Erstellt mit:

Model Context Protocol SDK
Awesome Lists
Inspiriert von context7 MCP-Server-Mustern

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-Referenzen für Ihre Agenten

Zwei Möglichkeiten, Context Awesome zu nutzen

MCP-Tools

CLI-Befehle

Installieren der CLI

Verwenden der CLI ohne Installation

Installation — MCP-Clients

Remote-Server (Empfohlen)

Lokales stdio (Claude Desktop, offline-fähig)

Lokaler HTTP-Transport (für benutzerdefinierte Integrationen)

Lokale Entwicklung

Backend-Service

Zusätzliche Installationsmethoden

Lizenz

Mitwirken

Support

Namensnennung

Credits

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