DuckDuckGo Web Search MCP Server
DuckDuckGo ウェブ検索 MCP サーバー
このプロジェクトは、DuckDuckGo 検索エンジンを使用して Web を検索し、オプションで見つかった URL のコンテンツを取得して要約できる MCP (Model Context Protocol) サーバーを提供します。
特徴
Web 検索: DuckDuckGo を使用して Web を検索します。
**結果の抽出:**検索結果からタイトル、URL、スニペットを抽出します。
**コンテンツの取得 (オプション):**検索結果で見つかった URL のコンテンツを取得し、jina api を使用して markdown 形式に変換します。
**並列フェッチ:**複数の URL を同時にフェッチして、処理を高速化します。
**エラー処理:**検索および取得中のタイムアウトやその他の潜在的なエラーを適切に処理します。
**構成可能:**返される検索結果の最大数を設定できます。
Jina API : jina API を使用して HTML を Markdown に変換します。
MCP 準拠: このサーバーは、MCP 互換のあらゆるクライアントで使用できるように設計されています。
Related MCP server: DuckDuckGo MCP Server
使用法
前提条件:
uvxパッケージマネージャー
クロードデスクトップ構成
Claude Desktop を使用している場合は、
claude_desktop_config.jsonファイルにサーバーを追加できます。
{ "mcpServers": { "web-search-duckduckgo": { "command": "uvx", "args": [ "--from", "git+https://github.com/kouui/web-search-duckduckgo.git@main", "main.py" ] } } }上記の設定が機能しない場合は、リポジトリをローカルPCにクローンし、次の設定を使用する必要があります。
{ "mcpServers": { "web-search-duckduckgo": { "command": "uv", "args": [ "--directory", "/path/to/web-search-duckduckgo", "run", "main.py" ] } } }道具
MCP クライアント (例: Claude) では、次のツールを使用できるようになりました。
search_and_fetch: Web を検索し、URL のコンテンツを取得します。query: 検索クエリ文字列。limit: 返される結果の最大数 (デフォルト: 3、最大: 10)。
**
fetch:**特定の URL のコンテンツを取得します。url: 取得する URL。
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ます。(ライセンスを指定する場合は、ライセンス ファイルを追加してください)。
Available Tools
2 toolsfetchC
scrape the html content and return the markdown format using jina api.
Args:
url: The search query string
Returns:
text : html in markdown format
| Name | Required | Description | Default |
|---|---|---|---|
| url | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions using the Jina API and the transformation to markdown format, but doesn't disclose important behavioral traits: rate limits, authentication requirements, error handling, whether this makes external network calls, or what happens with invalid URLs. For a tool that performs web scraping with an external API, this is a significant gap.
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?
The description is brief but has structural issues. The first sentence is clear, but the 'Args:' and 'Returns:' sections use inconsistent formatting and terminology ('search query string' for a URL parameter). While concise, it could be more effectively structured with clearer separation of concerns.
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?
Given no annotations, no output schema, and a tool that performs web scraping via an external API, the description is incomplete. It doesn't address important contextual aspects: error conditions, rate limits, authentication, what types of URLs are supported, or the structure/limitations of the returned markdown. For a tool with external dependencies and potential complexity, this is inadequate.
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?
The description adds minimal parameter semantics beyond the schema. With 0% schema description coverage, the description states 'url: The search query string' which is somewhat confusing (calling it a 'search query string' when it's clearly a URL parameter). It doesn't explain URL format requirements, validation, or provide examples. The baseline would be lower given the coverage gap, but it does at least identify the parameter's purpose.
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?
The description clearly states the tool's purpose: 'scrape the html content and return the markdown format using jina api.' It specifies the verb (scrape/return), resource (html content), and transformation (to markdown format). However, it doesn't explicitly differentiate from its sibling tool 'search_and_fetch' - we can infer it's a direct fetch while the sibling might search first, but this isn't stated.
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?
No guidance is provided about when to use this tool versus alternatives. The description doesn't mention the sibling tool 'search_and_fetch' or explain when direct fetching is appropriate versus searching and fetching. There's no context about prerequisites, limitations, or appropriate use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
search_and_fetchA
Search the web using DuckDuckGo and return results.
Args:
query: The search query string
limit: Maximum number of results to return (default: 3, maximum 10)
Returns:
List of dictionaries containing
- title
- url
- snippet
- summary markdown (empty if not available)
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| limit | No |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It discloses the search engine (DuckDuckGo) and return format, but doesn't mention rate limits, authentication needs, error conditions, or whether this is a read-only operation. The behavioral disclosure is adequate but incomplete.
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?
The description is well-structured with clear sections (Args, Returns) and front-loaded purpose. Every sentence earns its place - no redundant information. The formatting with bullet points enhances readability without unnecessary verbosity.
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?
Given the tool's moderate complexity (2 parameters, no output schema, no annotations), the description provides good coverage of purpose, parameters, and return format. It could benefit from more behavioral context (like rate limits or error handling) but is largely complete for a search 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?
The description adds significant value beyond the 0% schema coverage by explaining both parameters: 'query' as the search string and 'limit' with its default (3) and maximum (10) values. This compensates well for the lack of schema descriptions, though it doesn't cover all potential edge cases.
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?
The description clearly states the tool's purpose with specific verb ('Search the web using DuckDuckGo') and resource ('return results'). It distinguishes from the sibling 'fetch' tool by specifying it's a search operation rather than a direct fetch operation.
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?
The description implies usage context (searching the web) but doesn't explicitly state when to use this tool versus the 'fetch' sibling. It provides basic parameter guidance but lacks explicit alternatives or exclusion criteria.
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
- First observed
fetch - First observed
search_and_fetch
TDQS
Scored across 2 tools
The two tools have distinct primary purposes: 'search_and_fetch' performs web searches and returns results, while 'fetch' scrapes HTML content from a given URL. However, there is some potential for confusion because 'fetch' accepts a 'url' argument but its description mentions 'search query string' (likely a documentation error), which could blur the boundary between searching and fetching.
The tool names follow a consistent verb-based pattern ('fetch' and 'search_and_fetch'), with clear action-oriented naming. The minor deviation is that 'search_and_fetch' uses an 'and' conjunction, but overall the naming is readable and predictable.
With only 2 tools, the server feels thin for a web search and scraping domain. While it covers basic search and fetch operations, more tools (e.g., for advanced search filtering, caching, or handling different content types) would provide better scope. It's borderline but not severely lacking.
The server covers core web search and content fetching workflows, but there are notable gaps. For example, it lacks tools for managing search history, refining queries, handling pagination, or supporting different output formats beyond markdown. Agents can work around these, but the surface is not fully comprehensive.
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