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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

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-core

import 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 extract

요약

# 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 mcp

구성

# 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

설정은 ~/.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 mcp

Python 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.md

설치가 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-v3

지원되는 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
`CCORE_URL_ENGINE`	URL 추출 엔진 (`auto`, `simple`, `firecrawl`, `jina`, `crawl4ai`)	`auto`
`CCORE_DOCUMENT_ENGINE`	문서 추출 엔진 (`auto`, `simple`, `docling`)	`auto`
`CCORE_AUDIO_CONCURRENCY`	동시 오디오 전사 (1-10)	`3`
`CRAWL4AI_API_URL`	Crawl4AI Docker API URL (로컬 브라우저 모드 시 생략)	-
`FIRECRAWL_API_URL`	자체 호스팅 인스턴스를 위한 사용자 지정 Firecrawl API URL	-
`CCORE_FIRECRAWL_PROXY`	Firecrawl 프록시 모드 (`auto`, `basic`, `stealth`)	`auto`
`CCORE_FIRECRAWL_WAIT_FOR`	추출 전 대기 시간 (ms)	`3000`
`CCORE_LLM_PROVIDER`	요약을 위한 LLM 제공업체	-
`CCORE_LLM_MODEL`	요약을 위한 LLM 모델	-
`CCORE_STT_PROVIDER`	음성-텍스트 변환 제공업체	-
`CCORE_STT_MODEL`	음성-텍스트 변환 모델	-
`CCORE_STT_TIMEOUT`	음성-텍스트 변환 시간 제한 (초)	-
`CCORE_YOUTUBE_LANGUAGES`	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

사용 가이드 -- Python API 세부 정보, 구성 및 예제
프로세서 -- 각 형식에 대한 콘텐츠 추출 작동 방식
MCP 서버 -- Claude Desktop 및 MCP 통합

개발

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

uv sync --group dev

# Run tests
make test

# Lint
make ruff

라이선스

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다.

기여

기여를 환영합니다! 자세한 내용은 기여 가이드를 참조하세요.

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

Content Core

지원 형식

빠른 시작

CLI 사용법

추출

요약

MCP 서버

구성

uvx를 사용한 무설치 실행

Python API

추출

요약

구성

MCP 통합

Claude Code 스킬

AI 제공업체

구성

환경 변수

프록시 구성

선택적 종속성

LangChain과 함께 사용

문서

개발

라이선스

기여

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

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