What can you do with this server?

This MCP server provides AI-powered content extraction from URLs and files through Content Core's intelligent auto-detection engine. • Extract from URLs - Retrieve clean, structured content from web pages using smart engine selection (Firecrawl → Jina → BeautifulSoup fallback) • Process documents - Extract text from PDF, Word, PowerPoint, Excel, Markdown, HTML, and EPUB files (Docling → Enhanced PyMuPDF fallback) • Transcribe media - Convert video (MP4, AVI, MOV) and audio (MP3, WAV, M4A) to text using OpenAI Whisper speech-to-text • Extract from images - Process JPG, PNG, and TIFF images with OCR text recognition • Handle archives - Extract and analyze content from ZIP, TAR, and GZ files • Automatic optimization - The 'auto' engine intelligently selects the best extraction method based on content type • Structured output - Returns JSON responses with extracted content, metadata, and supports multiple formats (text, JSON, XML) • Multiple interfaces - Access through CLI commands, Python library, MCP server, Raycast extension, and macOS Services

Which integrations are available for this server?

Exposes a set of compatible tools for Langchain framework, enabling extraction, cleaning, and summarization capabilities directly within Langchain agents and chains. Enables right-click integration with macOS Finder through Services, allowing content extraction and summarization from any supported file with options for clipboard or TextEdit output. Integrates with OpenAI services for transcription (Whisper) and content processing, allowing for AI-powered content extraction and summarization. Provides a Python library for programmatic access to content extraction, cleaning, and summarization capabilities, with asynchronous functionality and customizable options. Offers a Raycast extension with smart auto-detection commands for extracting and summarizing content from various sources, including URLs and files, with multiple output options and visual feedback.

How do I use content-core?

1. Click on "Deploy Server". 2. Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state. 3. In the chat, type @ followed by the MCP server name and your instructions, e.g., "@content-core extract the main points from this article: https://example.com/tech-news" That's it! The server will respond to your query, and you can continue using it as needed. Here is a step-by-step guide with screenshots.

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content-core

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

Extract, process, and summarize content from URLs, files, and text through a unified async Python API, CLI, or MCP server.

Supported Formats

Category	Formats
Web	URLs, HTML pages, YouTube videos, Reddit posts
Documents	PDF, DOCX, PPTX, XLSX, EPUB, HTML, Markdown, plain text
Media	MP3, WAV, M4A, FLAC, OGG (audio); MP4, AVI, MOV, MKV (video)

Related MCP server: FreeCrawl MCP Server

Quick Start

pip install content-core

import content_core

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

Or with zero install:

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

CLI Usage

Content Core provides a unified content-core command with subcommands for extraction, summarization, and MCP server.

Extract

# 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

Summarize

# 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

Configuration

# Set persistent config
content-core config set llm_provider anthropic
content-core config set llm_model claude-sonnet-5

# List current config
content-core config list

# Delete a config value
content-core config delete llm_provider

Config is stored in ~/.content-core/config.toml. Priority: command flags > env vars > config file > defaults.

Zero-Install with uvx

All commands work without installation using uvx:

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

Python API

Extraction

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)

Summarization

import content_core

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

Configuration

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 includes a Model Context Protocol (MCP) server for use with Claude Desktop and other MCP-compatible applications.

Add to your claude_desktop_config.json:

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

The MCP server exposes two tools: extract_content and summarize_content. Both return plain text.

For detailed setup, see the MCP documentation.

Agent Skill (Claude Code & Codex)

Content Core ships an Agent Skill that teaches AI agents how to use it for extracting content from external sources. This repository is also a plugin marketplace, so the skill installs natively in both harnesses.

Claude Code — add the marketplace and install the plugin:

/plugin marketplace add lfnovo/content-core
/plugin install content-core@content-core

Codex — the repository carries a Codex plugin manifest (.codex-plugin/plugin.json) and marketplace catalog (.agents/plugins/marketplace.json) pointing at the same skill.

Manual fallback — copy the skill file directly into your project:

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

Once installed, the agent can use content-core to extract content from URLs, documents, and media files — either via CLI (uvx content-core) or MCP if configured.

AI Providers

Content Core uses Esperanto to support multiple LLM and STT providers. Switch providers by changing the config — no code changes needed:

# Use Anthropic for summarization
content-core config set llm_provider anthropic
content-core config set llm_model claude-sonnet-5

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

Supported providers include OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama, and more. See the Esperanto documentation for the full list.

Configuration

Content Core uses ContentCoreConfig powered by pydantic-settings. Settings are resolved in priority order: constructor args > env vars (CCORE_*) > config file (~/.content-core/config.toml) > defaults.

Environment Variables

Variable	Description	Default
`CCORE_URL_ENGINE`	URL extraction engine (`auto`, `simple`, `firecrawl`, `jina`, `crawl4ai`)	`auto`
`CCORE_DOCUMENT_ENGINE`	Document extraction engine (`auto`, `simple`, `docling`) — `docling` raises `ConfigurationError` if the extra is not installed; `auto` falls back silently	`auto`
`CCORE_AUDIO_CONCURRENCY`	Concurrent audio transcriptions (1-10)	`3`
`CRAWL4AI_API_URL`	Crawl4AI Docker API URL (omit for local browser mode)	-
`CRAWL4AI_API_TOKEN`	Bearer token for the Crawl4AI Docker API (required by Crawl4AI >= 0.9.0)	-
`FIRECRAWL_API_URL`	Custom Firecrawl API URL for self-hosted instances or Firecrawl-compatible backends (e.g. fastCRW)	-
`CCORE_FIRECRAWL_PROXY`	Firecrawl proxy mode (`auto`, `basic`, `stealth`)	`auto`
`CCORE_FIRECRAWL_WAIT_FOR`	Wait time in ms before extraction	`3000`
`CCORE_LLM_PROVIDER`	LLM provider for summarization	-
`CCORE_LLM_MODEL`	LLM model for summarization	-
`CCORE_STT_PROVIDER`	Speech-to-text provider	-
`CCORE_STT_MODEL`	Speech-to-text model	-
`CCORE_STT_TIMEOUT`	Speech-to-text timeout in seconds	-
`CCORE_YOUTUBE_LANGUAGES`	Preferred YouTube transcript languages	-

API keys for external services are set via their standard environment variables (e.g., OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY).

Proxy Configuration

Content Core reads standard HTTP_PROXY / HTTPS_PROXY / NO_PROXY environment variables automatically. No additional configuration is needed.

Optional Dependencies

# Docling for advanced document parsing (PDF, DOCX, PPTX, XLSX)
# Required by document_engine="docling", which raises ConfigurationError without it.
# Use document_engine="auto" (default) or "simple" to proceed without Docling.
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]

Using with LangChain

When installed with the langchain extra, Content Core provides LangChain-compatible tool wrappers:

from content_core.tools import extract_content_tool, summarize_content_tool

tools = [extract_content_tool, summarize_content_tool]

Documentation

Usage Guide -- Python API details, configuration, and examples
Processors -- How content extraction works for each format
MCP Server -- Claude Desktop and MCP integration

Development

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

uv sync --group dev

# Run tests
make test

# Lint
make ruff

License

This project is licensed under the MIT License.

Contributing

Contributions are welcome! Please see our Contributing Guide for 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

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

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Content Core

Supported Formats

Quick Start

CLI Usage

Extract

Summarize

MCP Server

Configuration

Zero-Install with uvx

Python API

Extraction

Summarization

Configuration

MCP Integration

Agent Skill (Claude Code & Codex)

AI Providers

Configuration

Environment Variables

Proxy Configuration

Optional Dependencies

Using with LangChain

Documentation

Development

License

Contributing

Available Tools

Output Schema

TDQS

Output Schema

TDQS

Tool Schema Changelog

TDQS

Maintenance

Resources

Related MCP Connectors

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