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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

Extrahieren, verarbeiten und fassen Sie Inhalte aus URLs, Dateien und Texten über eine einheitliche asynchrone Python-API, CLI oder einen MCP-Server zusammen.

Unterstützte Formate

Kategorie	Formate
Web	URLs, HTML-Seiten, YouTube-Videos, Reddit-Beiträge
Dokumente	PDF, DOCX, PPTX, XLSX, EPUB, Markdown, Klartext
Medien	MP3, WAV, M4A, FLAC, OGG (Audio); MP4, AVI, MOV, MKV (Video)

Related MCP server: FreeCrawl MCP Server

Schnellstart

pip install content-core

import content_core

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

Oder ohne Installation:

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

CLI-Nutzung

Content Core bietet einen einheitlichen content-core-Befehl mit Unterbefehlen für Extraktion, Zusammenfassung und MCP-Server.

Extrahieren

# 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

Zusammenfassen

# 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"

MCP-Server

content-core mcp

Konfiguration

# 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

Die Konfiguration wird in ~/.content-core/config.toml gespeichert. Priorität: Befehls-Flags > Umgebungsvariablen > Konfigurationsdatei > Standardwerte.

Installation ohne Installation mit uvx

Alle Befehle funktionieren ohne Installation mit uvx:

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

Python-API

Extraktion

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)

Zusammenfassung

import content_core

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

Konfiguration

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)

MCP-Integration

Content Core enthält einen Model Context Protocol (MCP)-Server zur Verwendung mit Claude Desktop und anderen MCP-kompatiblen Anwendungen.

Fügen Sie dies zu Ihrer claude_desktop_config.json hinzu:

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

Der MCP-Server stellt zwei Tools bereit: extract_content und summarize_content. Beide geben Klartext zurück.

Für eine detaillierte Einrichtung siehe die MCP-Dokumentation.

Claude Code Skill

Content Core enthält eine SKILL.md, die KI-Agenten beibringt, wie sie es zum Extrahieren von Inhalten aus externen Quellen verwenden können. Um sie in Ihrem Claude Code-Projekt verfügbar zu machen, kopieren Sie sie in Ihr Skills-Verzeichnis:

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

Nach der Installation kann Claude Code content-core verwenden, um Inhalte aus URLs, Dokumenten und Mediendateien zu extrahieren – entweder über CLI (uvx content-core) oder MCP, falls konfiguriert.

KI-Anbieter

Content Core verwendet Esperanto, um mehrere LLM- und STT-Anbieter zu unterstützen. Wechseln Sie die Anbieter durch Ändern der Konfiguration – es sind keine Codeänderungen erforderlich:

# 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

Zu den unterstützten Anbietern gehören OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama und mehr. Die vollständige Liste finden Sie in der Esperanto-Dokumentation.

Konfiguration

Content Core verwendet ContentCoreConfig, basierend auf pydantic-settings. Die Einstellungen werden in Prioritätsreihenfolge aufgelöst: Konstruktor-Argumente > Umgebungsvariablen (CCORE_*) > Konfigurationsdatei (~/.content-core/config.toml) > Standardwerte.

Umgebungsvariablen

Variable	Beschreibung	Standardwert
`CCORE_URL_ENGINE`	URL-Extraktions-Engine (`auto`, `simple`, `firecrawl`, `jina`, `crawl4ai`)	`auto`
`CCORE_DOCUMENT_ENGINE`	Dokumenten-Extraktions-Engine (`auto`, `simple`, `docling`)	`auto`
`CCORE_AUDIO_CONCURRENCY`	Gleichzeitige Audio-Transkriptionen (1-10)	`3`
`CRAWL4AI_API_URL`	Crawl4AI Docker API-URL (für lokalen Browser-Modus weglassen)	-
`FIRECRAWL_API_URL`	Benutzerdefinierte Firecrawl API-URL für selbst gehostete Instanzen	-
`CCORE_FIRECRAWL_PROXY`	Firecrawl-Proxy-Modus (`auto`, `basic`, `stealth`)	`auto`
`CCORE_FIRECRAWL_WAIT_FOR`	Wartezeit in ms vor der Extraktion	`3000`
`CCORE_LLM_PROVIDER`	LLM-Anbieter für Zusammenfassungen	-
`CCORE_LLM_MODEL`	LLM-Modell für Zusammenfassungen	-
`CCORE_STT_PROVIDER`	Speech-to-Text-Anbieter	-
`CCORE_STT_MODEL`	Speech-to-Text-Modell	-
`CCORE_STT_TIMEOUT`	Speech-to-Text-Timeout in Sekunden	-
`CCORE_YOUTUBE_LANGUAGES`	Bevorzugte YouTube-Transkriptsprachen	-

API-Schlüssel für externe Dienste werden über deren Standard-Umgebungsvariablen festgelegt (z. B. OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY).

Proxy-Konfiguration

Content Core liest automatisch die Standard-Umgebungsvariablen HTTP_PROXY / HTTPS_PROXY / NO_PROXY. Es ist keine zusätzliche Konfiguration erforderlich.

Optionale Abhängigkeiten

# 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]

Verwendung mit LangChain

Bei der Installation mit dem langchain-Extra bietet Content Core LangChain-kompatible Tool-Wrapper:

from content_core.tools import extract_content_tool, summarize_content_tool

tools = [extract_content_tool, summarize_content_tool]

Dokumentation

Nutzungsanleitung -- Details zur Python-API, Konfiguration und Beispiele
Prozessoren -- Wie die Inhaltsextraktion für jedes Format funktioniert
MCP-Server -- Claude Desktop und MCP-Integration

Entwicklung

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

uv sync --group dev

# Run tests
make test

# Lint
make ruff

Lizenz

Dieses Projekt ist unter der MIT-Lizenz lizenziert.

Mitwirken

Beiträge sind willkommen! Bitte lesen Sie unseren Leitfaden für Mitwirkende für Details.

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

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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

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Appeared in Searches

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

Content Core

Unterstützte Formate

Schnellstart

CLI-Nutzung

Extrahieren

Zusammenfassen

MCP-Server

Konfiguration

Installation ohne Installation mit uvx

Python-API

Extraktion

Zusammenfassung

Konfiguration

MCP-Integration

Claude Code Skill

KI-Anbieter

Konfiguration

Umgebungsvariablen

Proxy-Konfiguration

Optionale Abhängigkeiten

Verwendung mit LangChain

Dokumentation

Entwicklung

Lizenz

Mitwirken

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