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_contentExtract ContentA

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.

Name	Required	Description
`url`	No	URL to extract content from (web page, YouTube video, PDF link, etc.)
`engine`	No	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.
`no_ocr`	No	Disable OCR in Docling (requires engine=docling)
`formulas`	No	Enable formula extraction via Docling (requires engine=docling)
`pictures`	No	Enable image description + chart data extraction via Docling (requires engine=docling)
`file_path`	No	Local file path to extract content from

Output 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, the description carries the full burden; it usefully discloses the auth profile (no key needed for most sources, key needed for audio/video). It is silent on other behaviors: what happens if neither url nor file_path is supplied (0 required params), and whether unsupported sources error or degrade.

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?

Two tight sentences, front-loaded with the core action, and the auth caveat placed immediately after. No filler or repetition.

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?

An output schema exists, so return values need not be described, and the auth context is a genuine addition. Still missing is guidance on the unusual 0-required-parameter shape (must one of url/file_path be given?) and any routing versus the sibling 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 engine routing, docling requirements, and the flag semantics in detail. The description adds only a loose mapping of source types, so the baseline 3 applies.

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?

States a specific verb ('Extract') and resource ('content from a URL or file') plus the supported source types. It does not explicitly differentiate itself from the sibling summarize_content, so an agent must infer that this returns raw content rather than a summary.

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

Usage Guidelines3/5

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

Gives conditional guidance about when an API key is required (audio/video transcription only), which is useful. However, it never says when to choose this over summarize_content, nor does it explain the url-vs-file_path choice, leaving usage largely implied.

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

summarize_contentSummarize ContentB

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

Name	Required	Description	Default
`content`	Yes	The text content to summarize
`context`	No	Optional context to guide summarization (e.g., "summarize as bullet points")

Output Schema

Name	Required	Description
`result`	Yes

TDQS

B3.2/5.0

Behavior3/5

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

No annotations are provided, so the description carries the full burden. It usefully discloses a real prerequisite — an LLM provider key such as OPENAI_API_KEY must be configured — which is not in the schema. However it says nothing about cost, latency, or truncation behavior on long input, leaving gaps for a tool that calls an external LLM.

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?

Two compact sentences with no filler, and the core purpose is front-loaded ahead of the setup requirement. Slightly terse, but every sentence earns its place.

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?

Output schema exists so return values need no explanation, and both parameters are schema-documented, making the definition largely adequate for a simple tool. The gaps are the absence of any routing guidance versus extract_content and the lack of cost/latency context for an external LLM call.

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 both parameters are already fully documented, and the description adds nothing beyond restating that context is optional. Baseline 3 applies when the schema does the heavy lifting.

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?

States a specific verb and resource ('summarize content') plus the mechanism ('using LLM'), which distinguishes it from the sibling extract_content at a high level. It does not explicitly name or contrast with that sibling, so it falls short of a 5.

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?

There is no guidance on when to choose this tool over extract_content, nor any exclusions or preconditions for use beyond the API-key note. The reader must infer that summarizing is the use case from the name alone.

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 updatesv2.1.1Oct 5, 2026

Changedextract_content6 fields changed

addedInput schema / properties / engine / description

Added value: +"Optional extraction engine override, routed by input type.\nWith `url`: auto, simple, firecrawl, jina, crawl4ai.\nWith `file_path`: auto, simple, docling — `docling` requires\n`pip install \"content-core[docling]\"` and fails with a\nconfiguration error when the extra is missing, in which case use\n`auto` or `simple`.\nAny other value is rejected with an error naming the accepted ones."

addedInput schema / properties / file_path / description

Added value: +"Local file path to extract content from"

addedInput schema / properties / formulas / description

Added value: +"Enable formula extraction via Docling (requires engine=docling)"

addedInput schema / properties / no_ocr / description

Added value: +"Disable OCR in Docling (requires engine=docling)"

addedInput schema / properties / pictures / description

Added value: +"Enable image description + chart data extraction via Docling (requires engine=docling)"

addedInput schema / properties / url / description

Added value: +"URL to extract content from (web page, YouTube video, PDF link, etc.)"

Changedsummarize_content2 fields changed
- addedInput schema / properties / content / description
```
Added value: +"The text content to summarize"
```
- addedInput schema / properties / context / description
```
Added value: +"Optional context to guide summarization (e.g., \"summarize as bullet points\")"
```

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

A3.6/5.0

Scored 2026-10-05 10:20 across 2 tools

Disambiguation5/5

The two tools target clearly distinct actions: extract_content pulls raw content from a source, while summarize_content condenses it via an LLM. An agent can easily tell which to call based on whether it needs raw text or a summary.

Naming Consistency5/5

Both names follow the same verb_noun snake_case pattern (extract_content, summarize_content), with no deviations in convention or style.

Tool Count3/5

Two tools is on the thin side for even a focused server, though each earns its place in a natural extract-then-summarize pipeline. A couple of supporting operations (e.g. format conversion or output saving) would round out the surface.

Completeness4/5

The extract and summarize pair covers the core content-processing lifecycle, including multiple input sources (URLs, PDFs, YouTube). Minor gaps exist around batch processing, output formatting, or chunking, but agents can work around these.

Maintenance

ActivityNo data

ResponsivenessSyncing

Resources

Related MCP Connectors

PDF to Text & Markdown with OCR
io.github.monietools
PDF, DOCX and image URLs to clean, LLM-ready Markdown or text, with OCR for scanned pages.
Documentation Access Image & Video Processing RAG Systems
1
https://mcp.apify.com/?tools=poised_beardlichen/pdf-to-text-markdown-ocr
ClearcrawlB
io.github.clearcrawl
Any URL to clean markdown/JSON; PDF, XLSX, CSV, DOCX to text and tables. Free tier for agents.
Browser Automation Web Scraping
21
https://agentreader.onrender.com/clearcrawl/mcp
CoreWiseA
io.github.ryanportfolio
Extract structured insights from videos, podcasts, articles, and PDFs with multi-model AI
AI & Machine Learning Multimedia Processing Research & Data
42
https://corewise.video/mcp
arjunkmrm-scrapermcp_el
ai.smithery
Extract and parse web pages into clean HTML, links, or Markdown. Handle dynamic, complex, or block…
Web Scraping Browser Automation
2
https://server.smithery.ai/@arjunkmrm/scrapermcp_el/mcp

Related MCP Servers

Web-curl MCP Server
Web Scraping Search Browser Automation
rayss868
A
license
C
quality
B
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 a month ago (2026-10-08 12:18 UTC)
5
11
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 8 months ago (2026-03-21 15:18 UTC)
3
7 npm
MIT
readable-mcp
Browser Automation Web Scraping
tommypj
A
license
Not graded
quality
D
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 4 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