de en es ja ko ru zh

content-core

Name: content-core
Author: lfnovo

by lfnovo

Overview Schema Source Related Servers Discussions Admin

Python

Hybrid

Content Core

License: MIT PyPI version Downloads GitHub stars GitHub forks GitHub issues Ruff

Extraiga, procese y resuma contenido de URLs, archivos y texto a través de una API de Python asíncrona unificada, CLI o servidor MCP.

Formatos admitidos

Categoría	Formatos
Web	URLs, páginas HTML, vídeos de YouTube, publicaciones de Reddit
Documentos	PDF, DOCX, PPTX, XLSX, EPUB, Markdown, texto plano
Multimedia	MP3, WAV, M4A, FLAC, OGG (audio); MP4, AVI, MOV, MKV (vídeo)

Related MCP server: FreeCrawl MCP Server

Inicio rápido

pip install content-core

import content_core

result = await content_core.extract_content(url="https://example.com")
print(result.content)

O sin instalación:

uvx content-core extract "https://example.com"

Uso de la CLI

Content Core proporciona un comando unificado content-core con subcomandos para extracción, resumen y servidor MCP.

Extracción

# From a URL
content-core extract "https://example.com"

# From a file
content-core extract document.pdf

# With JSON output
content-core extract document.pdf --format json

# With a specific engine
content-core extract "https://example.com" --engine firecrawl

# From stdin
echo "some text" | content-core extract

Resumen

# Summarize text
content-core summarize "Long article text here..."

# With context
content-core summarize "Long text" --context "bullet points"

# From stdin
cat article.txt | content-core summarize --context "explain to a child"

Servidor MCP

content-core mcp

Configuración

# Set persistent config
content-core config set llm_provider anthropic
content-core config set llm_model claude-sonnet-4-20250514

# List current config
content-core config list

# Delete a config value
content-core config delete llm_provider

La configuración se almacena en ~/.content-core/config.toml. Prioridad: flags de comando > variables de entorno > archivo de configuración > valores predeterminados.

Instalación cero con uvx

Todos los comandos funcionan sin instalación usando uvx:

uvx content-core extract "https://example.com"
uvx content-core summarize "text" --context "one sentence"
uvx content-core mcp

API de Python

Extracción

import content_core

# From a URL
result = await content_core.extract_content(url="https://example.com")

# From a file
result = await content_core.extract_content(file_path="document.pdf")

# From text
result = await content_core.extract_content(content="some text")

# With engine override
from content_core import ContentCoreConfig
config = ContentCoreConfig(url_engine="firecrawl")
result = await content_core.extract_content(url="https://example.com", config=config)

Resumen

import content_core

summary = await content_core.summarize("long article text", context="bullet points")

Configuración

from content_core import ContentCoreConfig

config = ContentCoreConfig(
    url_engine="firecrawl",
    document_engine="docling",
    audio_concurrency=5,
)
result = await content_core.extract_content(url="https://example.com", config=config)

Integración MCP

Content Core incluye un servidor del Protocolo de Contexto de Modelo (MCP) para su uso con Claude Desktop y otras aplicaciones compatibles con MCP.

Añada a su claude_desktop_config.json:

{
  "mcpServers": {
    "content-core": {
      "command": "uvx",
      "args": ["content-core", "mcp"],
      "env": {
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

El servidor MCP expone dos herramientas: extract_content y summarize_content. Ambas devuelven texto plano.

Para una configuración detallada, consulte la documentación de MCP.

Habilidad de Claude Code

Content Core incluye un SKILL.md que enseña a los agentes de IA cómo usarlo para extraer contenido de fuentes externas. Para que esté disponible en su proyecto de Claude Code, cópielo en su directorio de habilidades:

# Download the skill
curl -o .claude/skills/content-core/SKILL.md --create-dirs \
  https://raw.githubusercontent.com/lfnovo/content-core/main/SKILL.md

Una vez instalado, Claude Code puede usar content-core para extraer contenido de URLs, documentos y archivos multimedia, ya sea a través de la CLI (uvx content-core) o MCP si está configurado.

Proveedores de IA

Content Core utiliza Esperanto para admitir múltiples proveedores de LLM y STT. Cambie de proveedor modificando la configuración; no se requieren cambios en el código:

# Use Anthropic for summarization
content-core config set llm_provider anthropic
content-core config set llm_model claude-sonnet-4-20250514

# Use Groq for transcription
content-core config set stt_provider groq
content-core config set stt_model whisper-large-v3

Los proveedores admitidos incluyen OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama y más. Consulte la documentación de Esperanto para ver la lista completa.

Configuración

Content Core utiliza ContentCoreConfig impulsado por pydantic-settings. Los ajustes se resuelven en orden de prioridad: argumentos del constructor > variables de entorno (CCORE_*) > archivo de configuración (~/.content-core/config.toml) > valores predeterminados.

Variables de entorno

Variable	Descripción	Predeterminado
`CCORE_URL_ENGINE`	Motor de extracción de URL (`auto`, `simple`, `firecrawl`, `jina`, `crawl4ai`)	`auto`
`CCORE_DOCUMENT_ENGINE`	Motor de extracción de documentos (`auto`, `simple`, `docling`)	`auto`
`CCORE_AUDIO_CONCURRENCY`	Transcripciones de audio simultáneas (1-10)	`3`
`CRAWL4AI_API_URL`	URL de la API de Docker de Crawl4AI (omitir para modo de navegador local)	-
`FIRECRAWL_API_URL`	URL de la API personalizada de Firecrawl para instancias autohospedadas	-
`CCORE_FIRECRAWL_PROXY`	Modo proxy de Firecrawl (`auto`, `basic`, `stealth`)	`auto`
`CCORE_FIRECRAWL_WAIT_FOR`	Tiempo de espera en ms antes de la extracción	`3000`
`CCORE_LLM_PROVIDER`	Proveedor de LLM para resumen	-
`CCORE_LLM_MODEL`	Modelo de LLM para resumen	-
`CCORE_STT_PROVIDER`	Proveedor de voz a texto	-
`CCORE_STT_MODEL`	Modelo de voz a texto	-
`CCORE_STT_TIMEOUT`	Tiempo de espera de voz a texto en segundos	-
`CCORE_YOUTUBE_LANGUAGES`	Idiomas preferidos de transcripción de YouTube	-

Las claves de API para servicios externos se establecen a través de sus variables de entorno estándar (p. ej., OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY).

Configuración de proxy

Content Core lee automáticamente las variables de entorno estándar HTTP_PROXY / HTTPS_PROXY / NO_PROXY. No se requiere configuración adicional.

Dependencias opcionales

# Docling for advanced document parsing (PDF, DOCX, PPTX, XLSX)
pip install content-core[docling]

# Crawl4AI for local browser-based URL extraction
pip install content-core[crawl4ai]
python -m playwright install --with-deps

# LangChain tool wrappers
pip install content-core[langchain]

# All optional features
pip install content-core[docling,crawl4ai,langchain]

Uso con LangChain

Cuando se instala con el extra langchain, Content Core proporciona envoltorios de herramientas compatibles con LangChain:

from content_core.tools import extract_content_tool, summarize_content_tool

tools = [extract_content_tool, summarize_content_tool]

Documentación

Guía de uso -- Detalles de la API de Python, configuración y ejemplos
Procesadores -- Cómo funciona la extracción de contenido para cada formato
Servidor MCP -- Integración con Claude Desktop y MCP

Desarrollo

git clone https://github.com/lfnovo/content-core
cd content-core

uv sync --group dev

# Run tests
make test

# Lint
make ruff

Licencia

Este proyecto está bajo la Licencia MIT.

Contribución

¡Las contribuciones son bienvenidas! Consulte nuestra Guía de contribución para obtener más detalles.

Available Tools

2 tools

extract_contentB

Extract content from a URL or file. Does not require an API key for most sources (web pages, PDFs, documents, YouTube transcripts). API key is only needed for audio/video transcription.

Args: url: URL to extract content from (web page, YouTube video, PDF link, etc.) file_path: Local file path to extract content from engine: Optional extraction engine override, routed by input type. With url: auto, simple, firecrawl, jina, crawl4ai. With file_path: auto, simple, docling — docling requires pip install "content-core[docling]" and fails with a configuration error when the extra is missing, in which case use auto or simple. Any other value is rejected with an error naming the accepted ones. formulas: Enable formula extraction via Docling (requires engine=docling) pictures: Enable image description + chart data extraction via Docling (requires engine=docling) no_ocr: Disable OCR in Docling (requires engine=docling)

Returns: Extracted text content

ParametersJSON Schema

Name	Required	Description	Default
`url`	No
`engine`	No
`no_ocr`	No
`formulas`	No
`pictures`	No
`file_path`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

B3.3/5.0

Behavior3/5

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

With no annotations, the description carries the burden. It discloses API key requirements, engine behavior, and the docling extra failure mode, but it does not explicitly state that the operation is read-only or describe the return format beyond 'Extracted text content'. The engine error message is useful but other behavioral aspects remain implicit.

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-organized with an Args/Returns structure and front-loads the purpose. It is detailed but each line earns its place, covering engine specifics and error conditions without excessive verbosity.

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

Completeness3/5

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

For a tool with six optional parameters and no required ones, the description omits guidance on whether at least one of url or file_path must be provided. The return description is minimal, and error handling for missing inputs is not covered. While engine behavior is well documented, these input-requirement gaps reduce completeness.

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

Parameters4/5

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

Schema coverage is 0%, so the description must compensate. It explains url, file_path, engine with valid values and routing, and clarifies that formulas, pictures, and no_ocr require engine=docling. This adds substantial meaning beyond the bare 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 clearly states the tool extracts content from a URL or file and lists common source types. It does not explicitly contrast with the sibling summarize_content, but the verb 'extract' and the scope are unambiguous.

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 is given on when to use this tool versus summarize_content. While it explains engine selection and API key conditions, it never addresses tool-level choice, which is a gap given the sibling exists.

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

summarize_contentA

Summarize content using LLM with optional context. Requires OPENAI_API_KEY (or another LLM provider key) to be configured.

Args: content: The text content to summarize context: Optional context to guide summarization (e.g., "summarize as bullet points")

Returns: Summarized text

ParametersJSON Schema

Name	Required	Description	Default
`content`	Yes
`context`	No

Output Schema

ParametersJSON Schema

Name	Required	Description
`result`	Yes

TDQS

A3.5/5.0

Behavior3/5

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

With no annotations provided, the description bears the full burden of behavioral disclosure. It notes the requirement for an LLM provider API key and indicates that the tool uses an LLM for summarization. However, it does not disclose potential rate limits, costs, or failure modes, leaving some behavioral aspects opaque.

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?

The description uses a docstring format with sections (Args, Returns), making it structured but slightly verbose. It front-loads the core purpose but adds extra formatting that could be trimmed. It is not overly long but could be more concise.

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

Completeness3/5

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

Given the low schema coverage and absence of annotations, the description provides the essential parameter meanings and return type. It also mentions the critical API key dependency. However, it lacks constraints like maximum content length or edge cases, and the output schema existence lightens the burden but doesn't fully compensate for missing details.

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

Parameters4/5

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

Schema coverage is 0% (no descriptions in input schema), but the description adds meaningful explanations for both parameters: 'content' is the text to summarize, and 'context' is optional guidance with an example ('summarize as bullet points'). This compensates well for the missing schema descriptions.

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 function: 'Summarize content using LLM with optional context.' It specifies a specific verb-resource relationship and distinguishes from the sibling tool 'extract_content' which serves a different purpose.

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 mentions a prerequisite (API key configuration) but provides no guidance on when to use this tool versus alternatives, such as the sibling 'extract_content'. No explicit when-to-use or when-not-to-use guidance is given.

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.

2 tool updatesv2.0.4Jul 13, 2026

Changedextract_content9 fields changed

addedInput schema / additionalProperties
```
Added value: +false
```

addedInput schema / properties / engine

Added value: +{
+  "anyOf": [
+    {
+      "type": "string"
+    },
+    {
+      "type": "null"
+    }
+  ],
+  "default": null
+}

addedInput schema / properties / formulas

Added value: +{
+  "default": false,
+  "type": "boolean"
+}

addedInput schema / properties / no_ocr

Added value: +{
+  "default": false,
+  "type": "boolean"
+}

addedInput schema / properties / pictures

Added value: +{
+  "default": false,
+  "type": "boolean"
+}

removedOutput schema / additionalProperties
```
Removed value: -true
```

addedOutput schema / properties

Added value: +{
+  "result": {
+    "type": "string"
+  }
+}

addedOutput schema / required
```
Added value: +[
+  "result"
+]
```
addedOutput schema / x-fastmcp-wrap-result
```
Added value: +true
```

Addedsummarize_content

1 tool updatev1.0.0Jan 3, 2026
- Changedextract_content2 fields changed
  - removedInput schema / properties / file_path / title
```
Removed value: -"File Path"
```
  - removedInput schema / properties / url / title
```
Removed value: -"Url"
```
1 tool updateAug 15, 2025
- First observedextract_content

Deploy Server

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A3.7/5.0

Scored 2026-09-07 06:12 across 2 tools

Disambiguation5/5

The two tools have clearly distinct purposes—extraction vs. summarization—with no functional overlap. Each tool's parameters are also well-differentiated, avoiding ambiguity.

Naming Consistency5/5

Both tool names follow the same verb_noun pattern (extract_content, summarize_content), providing a predictable and consistent naming convention.

Tool Count3/5

With only 2 tools, the set is borderline thin for a content-processing server. While both are useful, the count is minimal and could be expanded with additional content operations.

Completeness4/5

The tools cover the core content workflow of extraction and summarization, but lack other common operations (e.g., translation, keyword extraction) that would round out a comprehensive content toolkit. Minor gaps exist.

Maintenance

ActivityMaintained

ResponsivenessWithin a week

Resources

GitHub Repository

Need Help?

Related Servers

Related MCP Connectors

CoreWise A
io.github.ryanportfolio
Extract structured insights from videos, podcasts, articles, and PDFs with multi-model AI
AI & Machine Learning Multimedia Processing Research & Data
42
arjunkmrm-scrapermcp_elOAuth
ai.smithery
Extract and parse web pages into clean HTML, links, or Markdown. Handle dynamic, complex, or block…
Web Scraping Browser Automation
2
stagenth · 文档解析 A
com.stagenth
Parse PDF/Word/PPT/HTML to Markdown; tables as JSON, image extraction, RAG chunking, page ranges.
RAG Systems File Systems
42
Web Metadata, OpenGraph & Contact Extractor
io.github.JosejuX
Turns any URL into SEO metadata, contacts, tech stack, and AI-ready Markdown, in one call.
Web Scraping Browser Automation Marketing
16

Related MCP Servers

Web-curl MCP Server
Web Scraping Search Browser Automation
rayss868
A
license
C
quality
C
maintenance
A powerful tool for fetching and extracting text content from web pages and APIs, supporting web scraping, REST API requests, and Google Custom Search integration.
Updated 7 days ago (2026-09-06 05:51 UTC)
5
10
MIT
FreeCrawl MCP Server
Web Scraping Browser Automation Documentation Access
dylan-gluck
A
license
Not graded
quality
D
maintenance
Enables web scraping and document processing with JavaScript execution, anti-detection measures, batch processing, and structured data extraction. Supports multiple formats including markdown, HTML, screenshots, and handles PDFs with OCR capabilities.
Updated a year ago (2025-08-20 03:34 UTC)
4
MIT
ParseJet - Universal File &
Documentation Access Multimedia Processing Web Scraping
yooumuu
A
license
B
quality
C
maintenance
Parse any file or URL into structured text. Extract text from PDF, DOCX, YouTube, web pages, images, and 25+ formats via one API. Tools: parse_url, parse_file, get_youtube_transcript.
Updated 6 months ago (2026-03-21 15:18 UTC)
3
8 npm
MIT
readable-mcp
Browser Automation Web Scraping
tommypj
A
license
Not graded
quality
C
maintenance
Converts any URL into clean, LLM-ready Markdown, text, or HTML with production-grade features like SSRF protection, rate limiting, retries, caching, and structured error handling.
Updated 3 months ago (2026-06-23 11:40 UTC)
MIT

Appeared in Searches

A file opener for reading PDF files, images, and other file types

Content Core

Formatos admitidos

Inicio rápido

Uso de la CLI

Extracción

Resumen

Servidor MCP

Configuración

Instalación cero con uvx

API de Python

Extracción

Resumen

Configuración

Integración MCP

Habilidad de Claude Code

Proveedores de IA

Configuración

Variables de entorno

Configuración de proxy

Dependencias opcionales

Uso con LangChain

Documentación

Desarrollo

Licencia

Contribución

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

Related MCP Servers

Web-curl MCP Server

FreeCrawl MCP Server

ParseJet - Universal File &

readable-mcp

Appeared in Searches