by lfnovo
Extract text and audio from URLs, docs, videos, and images with AI voice generator and text to speech for unified conten
Extracts and processes content from URLs, documents, videos, audio files, and images into clean, structured text. Uses AI to automatically detect media types and apply the right extraction method.
Content Core is a community-built MCP server published by lfnovo that provides AI assistants with tools and capabilities via the Model Context Protocol. Extract text and audio from URLs, docs, videos, and images with AI voice generator and text to speech for unified conten It is categorized under ai ml, productivity. This server exposes 1 tool that AI clients can invoke during conversations and coding sessions.
You can install Content Core in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport.
MIT
Content Core is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Share your MCP server with the developer community
Useful MCP listing: Content Core is the kind of server we cite when onboarding engineers to host + tool permissions.
I recommend Content Core for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Content Core benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Content Core is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
Content Core has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
I recommend Content Core for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
Strong directory entry: Content Core surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Content Core reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Content Core is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
We evaluated Content Core against two servers with overlapping tools; this profile had the clearer scope statement.
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Content Core is a powerful, AI-powered content extraction and processing platform that transforms any source into clean, structured content. Extract text from websites, transcribe videos, process documents, and generate AI summaries—all through a unified interface with multiple integration options.
Extract content from anywhere:
Process with AI:
# Extract content from any source
uvx --from "content-core" ccore https://example.com
uvx --from "content-core" ccore document.pdf
# Generate AI summaries
uvx --from "content-core" csum video.mp4 --context "bullet points"
One-click setup with Model Context Protocol (MCP) - extract content directly in Claude conversations.
Smart auto-detection commands:
Right-click any file in Finder → Services → Extract or Summarize content instantly.
import content_core as cc
# Extract from any source
result = await cc.extract("https://example.com/article")
summary = await cc.summarize_content(result, context="explain to a child")
uvx for instant access without installationasyncio for efficient processingInstall Content Core using pip - no system dependencies required!
# Basic installation (PyMuPDF + BeautifulSoup/Jina extraction)
pip install content-core
# With enhanced document processing (adds Docling)
pip install content-core[docling]
# With local browser-based URL extraction (adds Crawl4AI)
# Note: Requires Playwright browsers (~300MB). Run:
pip install content-core[crawl4ai]
python -m playwright install --with-deps
# Full installation (with all optional features)
pip install content-core[docling,crawl4ai]
Note: The core installation uses pure Python implementations and doesn't require system libraries like libmagic, ensuring consistent, hassle-free installation across Windows, macOS, and Linux. Optional features like Crawl4AI (browser automation) may require additional system dependencies.
Alternatively, if you’re developing locally:
# Clone the repository
git clone https://github.com/lfnovo/content-core
cd content-core
# Install with uv
uv sync
Content Core provides three CLI commands for extracting, cleaning, and summarizing content: ccore, cclean, and csum. These commands support input from text, URLs, files, or piped data (e.g., via cat file | command).
Zero-install usage with uvx:
# Extract content
uvx --from "content-core" ccore https://example.com
# Clean content
uvx --from "content-core" cclean "messy content"
# Summarize content
uvx --from "content-core" csum "long text" --context "bullet points"
Extracts content from text, URLs, or files, with optional formatting. Usage:
ccore [-f|--format xml|json|text] [-d|--debug] [content]
Options:
-f, --format: Output format (xml, json, or text). Default: text.-d, --debug: Enable debug logging.content: Input content (text, URL, or file path). If omitted, reads from stdin.Examples:
# Extract from a URL as text
ccore https://example.com
# Extract from a file as JSON
ccore -f json document.pdf
# Extract from piped text as XML
echo "Sample text" | ccore --format xml
Cleans content by removing unnecessary formatting, spaces, or artifacts. Accepts text, JSON, XML input, URLs, or file paths. Usage:
cclean [-d|--debug] [content]
Options:
-d, --debug: Enable debug logging.content: Input content to clean (text, URL, file path, JSON, or XML). If omitted, reads from stdin.Examples:
# Clean a text string
cclean " messy text "
# Clean piped JSON
echo '{"content": " messy text "}' | cclean
# Clean content from a URL
cclean https://example.com
# Clean a file’s content
cclean document.txt
Summarizes content with an optional context to guide the summary style. Accepts text, JSON, XML input, URLs, or file paths.
Usage:
csum [--context "context text"] [-d|--debug] [content]
Options:
--context: Context for summarization (e.g., "explain to a child"). Default: none.-d, --debug: Enable debug logging.content: Input content to summarize (text, URL, file path, JSON, or XML). If omitted, reads from stdin.Examples:
# Summarize text
csum "AI is transforming industries."
# Summarize with context
csum --context "in bullet points" "AI is transforming industries."
# Summarize piped content
cat article.txt | csum --context "one sentence"
# Summarize content from URL
csum https://example.com
# Summarize a file's content
csum document.txt
You can quickly integrate content-core into your Python projects to extract, clean, and summarize content from various sources.
import content_core as cc
# Extract content from a URL, file, or text
result = await cc.extract("https://example.com/article")
# Clean messy content
cleaned_text = await cc.clean("...messy text with [brackets] and extra spaces...")
# Summarize content with optional context
summary = await cc.summarize_content("long article text", context="explain to a child")
# Extract audio with custom speech-to-text model
from content_core.common import ProcessSourceInput
result = await cc.extract(ProcessSourceInput(
file_path="interview.mp3",
audio_provider="openai",
audio_model="whisper-1"
))
For more information on how to use the Content Core library, including details on AI model configuration and customization, refer to our Usage Documentation.
Content Core includes a Model Context Protocol (MCP) server that enables seamless integration with Claude Desktop and other MCP-compatible applications. The MCP server exposes Content Core's powerful extraction capabilities through a standardized protocol.
<a href="https://glama.ai/mcp/servers/@lfnovo/content-core"> <img width="380" height="200" src="https://glama.ai/mcp/servers/@lfnovo/content-core/badge" /> </a># Install Content Core (MCP server included)
pip install content-core
# Or use directly with uvx (no installation required)
uvx --from "content-core" content-core-mcp
Add to your claude_desktop_config.json:
{
"mcpServers": {
"content-core": {
"command": "uvx",
"args": [
"--from",
"content-core",
"content-core-mcp"
]
}
}
}
For detailed setup instructions, configuration options, and usage examples, see our MCP Documentation.
Content Core features an optimized PyMuPDF extraction engine with significant improvements for scientific documents and complex PDFs.
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.