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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

統一された非同期Python API、CLI、またはMCPサーバーを通じて、URL、ファイル、テキストからコンテンツを抽出、処理、要約します。

サポートされているフォーマット

カテゴリ	フォーマット
Web	URL、HTMLページ、YouTube動画、Reddit投稿
ドキュメント	PDF、DOCX、PPTX、XLSX、EPUB、Markdown、プレーンテキスト
メディア	MP3、WAV、M4A、FLAC、OGG (音声); MP4、AVI、MOV、MKV (動画)

Related MCP server: FreeCrawl MCP Server

クイックスタート

pip install content-core

import content_core

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

またはインストール不要で実行:

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

CLIの使用方法

Content Coreは、抽出、要約、MCPサーバー用のサブコマンドを備えた統一された content-core コマンドを提供します。

抽出

# 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

要約

# 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サーバー

content-core mcp

設定

# 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

設定は ~/.content-core/config.toml に保存されます。優先順位: コマンドフラグ > 環境変数 > 設定ファイル > デフォルト値。

uvxによるインストール不要の実行

すべてのコマンドは uvx を使用してインストールなしで動作します:

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

Python API

抽出

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)

要約

import content_core

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

設定

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統合

Content Coreには、Claude Desktopやその他のMCP互換アプリケーションで使用するためのModel Context Protocol (MCP) サーバーが含まれています。

claude_desktop_config.json に以下を追加してください:

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

MCPサーバーは extract_content と summarize_content の2つのツールを公開します。どちらもプレーンテキストを返します。

詳細なセットアップについては、MCPドキュメントを参照してください。

Claude Codeスキル

Content Coreには、AIエージェントが外部ソースからコンテンツを抽出する方法を教える SKILL.md が含まれています。Claude Codeプロジェクトで利用できるようにするには、スキルディレクトリにコピーしてください:

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

インストールが完了すると、Claude CodeはCLI (uvx content-core) または設定済みの場合はMCPを介して、content-coreを使用してURL、ドキュメント、メディアファイルからコンテンツを抽出できるようになります。

AIプロバイダー

Content Coreは Esperanto を使用して、複数のLLMおよびSTTプロバイダーをサポートしています。設定を変更するだけでプロバイダーを切り替えることができ、コードの変更は不要です:

# 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

サポートされているプロバイダーには、OpenAI、Anthropic、Google、Groq、DeepSeek、Ollamaなどがあります。全リストについては Esperantoドキュメントを参照してください。

設定

Content Coreは pydantic-settings を利用した ContentCoreConfig を使用します。設定は優先順位の高い順に解決されます: コンストラクタ引数 > 環境変数 (CCORE_*) > 設定ファイル (~/.content-core/config.toml) > デフォルト値。

環境変数

変数	説明	デフォルト
`CCORE_URL_ENGINE`	URL抽出エンジン (`auto`, `simple`, `firecrawl`, `jina`, `crawl4ai`)	`auto`
`CCORE_DOCUMENT_ENGINE`	ドキュメント抽出エンジン (`auto`, `simple`, `docling`)	`auto`
`CCORE_AUDIO_CONCURRENCY`	同時音声文字起こし数 (1-10)	`3`
`CRAWL4AI_API_URL`	Crawl4AI Docker API URL (ローカルブラウザモードの場合は省略)	-
`FIRECRAWL_API_URL`	セルフホストインスタンス用のカスタムFirecrawl API URL	-
`CCORE_FIRECRAWL_PROXY`	Firecrawlプロキシモード (`auto`, `basic`, `stealth`)	`auto`
`CCORE_FIRECRAWL_WAIT_FOR`	抽出前の待機時間 (ms)	`3000`
`CCORE_LLM_PROVIDER`	要約用LLMプロバイダー	-
`CCORE_LLM_MODEL`	要約用LLMモデル	-
`CCORE_STT_PROVIDER`	音声認識(STT)プロバイダー	-
`CCORE_STT_MODEL`	音声認識(STT)モデル	-
`CCORE_STT_TIMEOUT`	音声認識(STT)タイムアウト (秒)	-
`CCORE_YOUTUBE_LANGUAGES`	YouTube文字起こしの優先言語	-

外部サービスのAPIキーは、標準の環境変数 (例: OPENAI_API_KEY, FIRECRAWL_API_KEY, JINA_API_KEY) を介して設定されます。

プロキシ設定

Content Coreは、標準の HTTP_PROXY / HTTPS_PROXY / NO_PROXY 環境変数を自動的に読み取ります。追加の設定は不要です。

オプションの依存関係

# 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]

LangChainでの使用

langchain エクストラをインストールすると、Content CoreはLangChain互換のツールラッパーを提供します:

from content_core.tools import extract_content_tool, summarize_content_tool

tools = [extract_content_tool, summarize_content_tool]

ドキュメント

使用ガイド -- Python APIの詳細、設定、例
プロセッサ -- 各フォーマットのコンテンツ抽出の仕組み
MCPサーバー -- Claude DesktopおよびMCP統合

開発

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

uv sync --group dev

# Run tests
make test

# Lint
make ruff

ライセンス

このプロジェクトは MITライセンスの下でライセンスされています。

貢献

貢献を歓迎します！詳細については貢献ガイドを参照してください。

Available Tools

2 tools

extract_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
`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

A3.5/5.0

Behavior3/5

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.

Conciseness5/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines3/5

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

B3.2/5.0

Behavior3/5

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.

Conciseness4/5

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.

Completeness3/5

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.

Parameters3/5

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.

Purpose4/5

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.

Usage Guidelines2/5

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 updatesv2.1.1Oct 5, 2026

Changedextract_content6 fields changed

addedInput schema / properties / engine / description

Added 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."

addedInput schema / properties / file_path / description

Added value: +"Local file path to extract content from"

addedInput schema / properties / formulas / description

Added value: +"Enable formula extraction via Docling (requires engine=docling)"

addedInput schema / properties / no_ocr / description

Added value: +"Disable OCR in Docling (requires engine=docling)"

addedInput schema / properties / pictures / description

Added value: +"Enable image description + chart data extraction via Docling (requires engine=docling)"

addedInput schema / properties / url / description

Added value: +"URL to extract content from (web page, YouTube video, PDF link, etc.)"

Changedsummarize_content2 fields changed
- addedInput schema / properties / content / description
```
Added value: +"The text content to summarize"
```
- addedInput schema / properties / context / description
```
Added value: +"Optional context to guide summarization (e.g., \"summarize as bullet points\")"
```

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

A3.6/5.0

Scored 2026-10-05 10:20 across 2 tools

Disambiguation5/5

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.

Naming Consistency5/5

Both names follow the same verb_noun snake_case pattern (extract_content, summarize_content), with no deviations in convention or style.

Tool Count3/5

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.

Completeness4/5

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.

Maintenance

ActivityNo data

ResponsivenessSyncing

Resources

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Appeared in Searches

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

Content Core

サポートされているフォーマット

クイックスタート

CLIの使用方法

抽出

要約

MCPサーバー

設定

uvxによるインストール不要の実行

Python API

抽出

要約

設定

MCP統合

Claude Codeスキル

AIプロバイダー

設定

環境変数

プロキシ設定

オプションの依存関係

LangChainでの使用

ドキュメント

開発

ライセンス

貢献

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