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MCP JinaAI Reader Server

by spences10
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JavaScript
Remote

mcp-jinaai-リーダー

⚠️お知らせ

このリポジトリはメンテナンスされなくなりました。

このツールの機能は、複数の MCP ツールを 1 つの統合パッケージにまとめたmcp-omnisearchで利用できるようになりました。

代わりにmcp-omnisearchを使用してください。

Jina.aiのReader APIとLLMを統合するためのモデルコンテキストプロトコル（MCP）サーバー。このサーバーは、ドキュメント作成とWebコンテンツ分析に最適化された、効率的で包括的なWebコンテンツ抽出機能を提供します。

Related MCP server: Jina AI Remote MCP Server

特徴

  • 📚 Jina.ai Reader API による高度な Web コンテンツ抽出

  • 🚀 高速かつ効率的なコンテンツ検索

  • 📄 構造を保持したまま完全なテキスト抽出

  • 🔄 LLM向けに最適化されたクリーンなフォーマット

  • 🌐 ドキュメントを含むさまざまなコンテンツタイプをサポート

  • 🏗️ モデルコンテキストプロトコルに基づいて構築

構成

このサーバーはMCPクライアント経由で設定する必要があります。以下に、様々な環境における設定例を示します。

傾斜構成

Cline MCP 設定に以下を追加します:

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "node",
			"args": ["-y", "mcp-jinaai-reader"],
			"env": {
				"JINAAI_API_KEY": "your-jinaai-api-key"
			}
		}
	}
}

WSL 構成の Claude デスクトップ

WSL 環境の場合は、Claude Desktop 構成に以下を追加します。

{
	"mcpServers": {
		"jinaai-reader": {
			"command": "wsl.exe",
			"args": [
				"bash",
				"-c",
				"JINAAI_API_KEY=your-jinaai-api-key npx mcp-jinaai-reader"
			]
		}
	}
}

環境変数

サーバーには次の環境変数が必要です。

  • JINAAI_API_KEY : Jina.ai APIキー（必須）

API

サーバーは、構成可能なパラメータを持つ単一の MCP ツールを実装します。

読み取りURL

Jina.ai Reader を使用して、任意の URL を LLM 対応テキストに変換します。

パラメータ:

  • url (文字列、必須): 処理するURL

  • no_cache (ブール値、オプション): 最新の結果を得るためにキャッシュをバイパスします。デフォルトはfalseです。

  • format (文字列, オプション): レスポンスのフォーマット ("json" または "stream")。デフォルトは "json"

  • timeout （数値、オプション）：ウェブページの読み込みを待つ最大時間（秒）

  • target_selector （文字列、オプション）：特定の要素に焦点を当てるCSSセレクタ

  • wait_for_selector (文字列、オプション): 特定の要素を待機するための CSS セレクター

  • remove_selector (文字列、オプション): 特定の要素を除外する CSS セレクタ

  • with_links_summary (ブール値、オプション): レスポンスの最後にすべてのリンクを収集します

  • with_images_summary (ブール値、オプション): レスポンスの最後にすべての画像を収集します

  • with_generated_alt (ブール値、オプション): キャプションのない画像に代替テキストを追加する

  • with_iframe (ブール値、オプション): レスポンスにiframeコンテンツを含める

発達

設定

  1. リポジトリをクローンする

  2. 依存関係をインストールします:

npm install

  1. プロジェクトをビルドします。

npm run build

  1. 開発モードで実行:

npm run dev

出版

  1. package.json のバージョンを更新する

  2. プロジェクトをビルドします。

npm run build

  1. npm に公開:

npm publish

貢献

貢献を歓迎します！お気軽にプルリクエストを送信してください。

ライセンス

MIT ライセンス - 詳細についてはLICENSEファイルを参照してください。

謝辞

Available Tools

1 tool
read_urlB

Convert any URL to LLM-friendly text using Jina.ai Reader

ParametersJSON Schema
NameRequiredDescriptionDefault
urlYesURL to process
no_cacheNoBypass cache for fresh results
formatNoResponse format (json or stream)json
timeoutNoMaximum time in seconds to wait for webpage load
target_selectorNoCSS selector to focus on specific elements
wait_for_selectorNoCSS selector to wait for specific elements
remove_selectorNoCSS selector to exclude specific elements
with_links_summaryNoGather all links at the end of response
with_images_summaryNoGather all images at the end of response
with_generated_altNoAdd alt text to images lacking captions
with_iframeNoInclude iframe content in response

TDQS

B3.4/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden but only states the basic function without disclosing behavioral traits like rate limits, authentication needs, error handling, or performance characteristics. It mentions the external service (Jina.ai Reader) but doesn't explain implications of using a third-party service.

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?

The description is a single, efficient sentence that clearly states the tool's purpose without unnecessary words. It's appropriately sized and front-loaded with the core functionality, making every word earn its place.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a complex tool with 11 parameters and no output schema, the description is insufficient. It doesn't explain what 'LLM-friendly text' means in practice, doesn't describe the response format, and provides no guidance on parameter interactions or error cases. The lack of output schema increases the need for more descriptive context.

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%, providing comprehensive parameter documentation. The description adds no parameter-specific information beyond the schema, maintaining the baseline score. It doesn't explain relationships between parameters or provide usage examples.

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 purpose with specific verb ('Convert') and resource ('any URL') while specifying the method ('using Jina.ai Reader') and output format ('LLM-friendly text'). It distinguishes this as a URL-to-text conversion tool with no siblings to differentiate from.

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?

The description implies usage context ('Convert any URL to LLM-friendly text') but provides no explicit guidance on when to use this tool versus alternatives, prerequisites, or limitations. With no sibling tools, the baseline is adequate but lacks specific usage scenarios.

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.

  1. 1 tool updatev1.0.0
    • First observedread_url
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

B3.4/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool's purpose is clearly defined and distinct by default.

Naming Consistency5/5

A single tool inherently has perfect naming consistency, as there are no other tools to compare against. The tool name 'read_url' follows a clear verb_noun pattern.

Tool Count2/5

One tool is too few for a server with a purpose that could reasonably support more operations, such as handling different URL types or providing metadata extraction. This minimal set feels thin and under-scoped for the domain.

Completeness2/5

The server's domain appears to be URL content reading, but the single tool only covers basic text conversion. There are obvious gaps, such as no tools for handling errors, extracting structured data, or managing different content formats, which limits agent effectiveness.

Maintenance

ActivityInactive
ResponsivenessNo issues

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