content-core
Content Core
통합 비동기 Python API, CLI 또는 MCP 서버를 통해 URL, 파일 및 텍스트에서 콘텐츠를 추출, 처리 및 요약합니다.
지원 형식
카테고리 | 형식 |
웹 | URL, HTML 페이지, YouTube 비디오, Reddit 게시물 |
문서 | PDF, DOCX, PPTX, XLSX, EPUB, Markdown, 일반 텍스트 |
미디어 | MP3, WAV, M4A, FLAC, OGG (오디오); MP4, AVI, MOV, MKV (비디오) |
Related MCP server: FreeCrawl MCP Server
빠른 시작
pip install content-coreimport content_core
result = await content_core.extract_content(url="https://example.com")
print(result.content)또는 설치 없이 사용:
uvx content-core extract "https://example.com"CLI 사용법
Content Core는 추출, 요약 및 MCP 서버를 위한 하위 명령어가 포함된 통합 content-core 명령어를 제공합니다.
추출
# 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 extract요약
# 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 서버
content-core mcp구성
# 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_provider설정은 ~/.content-core/config.toml에 저장됩니다. 우선순위: 명령 플래그 > 환경 변수 > 설정 파일 > 기본값.
uvx를 사용한 무설치 실행
모든 명령어는 uvx를 사용하여 설치 없이 작동합니다:
uvx content-core extract "https://example.com"
uvx content-core summarize "text" --context "one sentence"
uvx content-core mcpPython API
추출
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)요약
import content_core
summary = await content_core.summarize("long article text", context="bullet points")구성
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 통합
Content Core는 Claude Desktop 및 기타 MCP 호환 애플리케이션에서 사용할 수 있는 MCP(Model Context Protocol) 서버를 포함합니다.
claude_desktop_config.json에 추가하세요:
{
"mcpServers": {
"content-core": {
"command": "uvx",
"args": ["content-core", "mcp"],
"env": {
"OPENAI_API_KEY": "sk-..."
}
}
}
}MCP 서버는 extract_content와 summarize_content라는 두 가지 도구를 제공합니다. 둘 다 일반 텍스트를 반환합니다.
자세한 설정은 MCP 문서를 참조하세요.
Claude Code 스킬
Content Core는 AI 에이전트가 외부 소스에서 콘텐츠를 추출하는 방법을 학습할 수 있는 SKILL.md를 포함합니다. Claude Code 프로젝트에서 사용하려면 스킬 디렉토리에 복사하세요:
# Download the skill
curl -o .claude/skills/content-core/SKILL.md --create-dirs \
https://raw.githubusercontent.com/lfnovo/content-core/main/SKILL.md설치가 완료되면 Claude Code는 CLI(uvx content-core) 또는 구성된 경우 MCP를 통해 content-core를 사용하여 URL, 문서 및 미디어 파일에서 콘텐츠를 추출할 수 있습니다.
AI 제공업체
Content Core는 Esperanto를 사용하여 여러 LLM 및 STT 제공업체를 지원합니다. 설정을 변경하여 제공업체를 전환하세요. 코드 변경은 필요하지 않습니다:
# 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-v3지원되는 제공업체에는 OpenAI, Anthropic, Google, Groq, DeepSeek, Ollama 등이 포함됩니다. 전체 목록은 Esperanto 문서를 참조하세요.
구성
Content Core는 pydantic-settings 기반의 ContentCoreConfig를 사용합니다. 설정은 우선순위 순으로 결정됩니다: 생성자 인수 > 환경 변수 (CCORE_*) > 설정 파일 (~/.content-core/config.toml) > 기본값.
환경 변수
변수 | 설명 | 기본값 |
| URL 추출 엔진 ( |
|
| 문서 추출 엔진 ( |
|
| 동시 오디오 전사 (1-10) |
|
| Crawl4AI Docker API URL (로컬 브라우저 모드 시 생략) | - |
| 자체 호스팅 인스턴스를 위한 사용자 지정 Firecrawl API URL | - |
| Firecrawl 프록시 모드 ( |
|
| 추출 전 대기 시간 (ms) |
|
| 요약을 위한 LLM 제공업체 | - |
| 요약을 위한 LLM 모델 | - |
| 음성-텍스트 변환 제공업체 | - |
| 음성-텍스트 변환 모델 | - |
| 음성-텍스트 변환 시간 제한 (초) | - |
| 선호하는 YouTube 자막 언어 | - |
외부 서비스에 대한 API 키는 표준 환경 변수(예: OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY)를 통해 설정됩니다.
프록시 구성
Content Core는 표준 HTTP_PROXY / HTTPS_PROXY / NO_PROXY 환경 변수를 자동으로 읽습니다. 추가 구성은 필요하지 않습니다.
선택적 종속성
# 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]LangChain과 함께 사용
langchain 추가 기능과 함께 설치하면 Content Core는 LangChain 호환 도구 래퍼를 제공합니다:
from content_core.tools import extract_content_tool, summarize_content_tool
tools = [extract_content_tool, summarize_content_tool]문서
개발
git clone https://github.com/lfnovo/content-core
cd content-core
uv sync --group dev
# Run tests
make test
# Lint
make ruff라이선스
이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다.
기여
기여를 환영합니다! 자세한 내용은 기여 가이드를 참조하세요.
Available Tools
2 toolsextract_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 | Default |
|---|---|---|---|
| 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
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.
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.
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.
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.
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.
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
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.
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.
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.
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.
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.
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 updates
v2.1.1- Changed
extract_content6 fields changed- added
Input schema / properties / engine / descriptionAdded 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." - added
Input schema / properties / file_path / descriptionAdded value: +"Local file path to extract content from" - added
Input schema / properties / formulas / descriptionAdded value: +"Enable formula extraction via Docling (requires engine=docling)" - added
Input schema / properties / no_ocr / descriptionAdded value: +"Disable OCR in Docling (requires engine=docling)" - added
Input schema / properties / pictures / descriptionAdded value: +"Enable image description + chart data extraction via Docling (requires engine=docling)" - added
Input schema / properties / url / descriptionAdded value: +"URL to extract content from (web page, YouTube video, PDF link, etc.)"
- Changed
summarize_content2 fields changed- added
Input schema / properties / content / descriptionAdded value: +"The text content to summarize" - added
Input schema / properties / context / descriptionAdded value: +"Optional context to guide summarization (e.g., \"summarize as bullet points\")"
2 tool updates
v2.0.4- Changed
extract_content9 fields changed- added
Input schema / additionalPropertiesAdded value: +false - added
Input schema / properties / engineAdded value: +{ + "anyOf": [ + { + "type": "string" + }, + { + "type": "null" + } + ], + "default": null +} - added
Input schema / properties / formulasAdded value: +{ + "default": false, + "type": "boolean" +} - added
Input schema / properties / no_ocrAdded value: +{ + "default": false, + "type": "boolean" +} - added
Input schema / properties / picturesAdded value: +{ + "default": false, + "type": "boolean" +} - removed
Output schema / additionalPropertiesRemoved value: -true - added
Output schema / propertiesAdded value: +{ + "result": { + "type": "string" + } +} - added
Output schema / requiredAdded value: +[ + "result" +] - added
Output schema / x-fastmcp-wrap-resultAdded value: +true
- Added
summarize_content
1 tool update
v1.0.0- Changed
extract_content2 fields changed- removed
Input schema / properties / file_path / titleRemoved value: -"File Path" - removed
Input schema / properties / url / titleRemoved value: -"Url"
1 tool update
- First observed
extract_content
TDQS
Scored across 2 tools
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.
Both names follow the same verb_noun snake_case pattern (extract_content, summarize_content), with no deviations in convention or style.
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.
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.
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