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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_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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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