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Deep Research MCP

Deep Research MCP

by ali-kh7

ディープリサーチMCP🌐

ディープリサーチMCP
ダウンロードリリース

Deep Research MCPリポジトリへようこそ!このプロジェクトは、モデルコンテキストプロトコル(MCP)に準拠したサーバーを提供します。包括的なウェブリサーチを容易にするように設計されています。Tavilyの検索APIとクロールAPIを活用することで、サーバーは様々なトピックに関する詳細な情報を収集し、大規模言語モデル(LLM)を用いた高品質なマークダウン文書の作成をサポートするためにデータを構造化します。

目次

Related MCP server: Deep Research MCP Server

特徴

  • MCP コンプライアンス: サーバーはモデル コンテキスト プロトコルに準拠しており、さまざまなツールやサービスとの互換性が保証されます。

  • データ集約: 複数のソースからデータを効率的に収集し、構造化します。

  • マークダウン生成: 収集したデータを適切に構造化されたマークダウン ドキュメントに変換します。

  • Web クロール: Tavily の検索およびクロール API を使用して、詳細な Web 調査を実行します。

  • Node.js と TypeScript : パフォーマンスと保守性を向上させるために最新のテクノロジーを使用して構築されています。

インストール

Deep Research MCP を開始するには、次の手順に従います。

  1. リポジトリをクローンします:

    git clone https://github.com/ali-kh7/deep-research-mcp.git
  2. プロジェクトディレクトリに移動します:

    cd deep-research-mcp
  3. 依存関係をインストールします:

    npm install
  4. サーバーを実行します:

    npm start

ダウンロード可能なファイルと特定のバージョンについては、リリースセクションを確認することもできます。

使用法

サーバーが起動したら、API経由でサーバーとやり取りできます。効果的な使い方は以下のとおりです。

  1. 情報収集リクエストを送信します:

    特定のトピックを指定してサーバーにリクエストを送信し、データを収集できます。サーバーは、マークダウン生成に適した構造化された情報を返します。

    リクエストの例:

    POST /api/research
    Content-Type: application/json
    
    {
      "topic": "Artificial Intelligence"
    }
  2. 構造化データを受信する:

    サーバーは構造化された形式でデータを返します。このデータはそのまま使用することも、マークダウンドキュメントに変換することもできます。

  3. マークダウンドキュメントを生成する:

    構造化データは、API で提供されている関数を使用してマークダウンに変換できます。

Markdown出力の例

# Artificial Intelligence

## Overview
Artificial Intelligence (AI) refers to the simulation of human intelligence in machines.

## Applications
- Healthcare
- Finance
- Transportation

## Conclusion
AI is transforming industries and shaping the future.

APIドキュメント

詳細なAPIドキュメントについては、このリポジトリのdocsフォルダをご覧ください。利用可能なすべてのエンドポイント、リクエスト形式、レスポンス構造に関する情報が含まれています。

エンドポイント

  • POST /api/research : 特定のトピックに関する情報を収集します。

  • GET /api/status : サーバーのステータスを確認します。

貢献

Deep Research MCPの改善に向けた貢献を歓迎します。貢献をご希望の場合は、以下の手順に従ってください。

  1. リポジトリをフォークします。

  2. 新しいブランチを作成します:

    git checkout -b feature/YourFeatureName
  3. 変更を加えます。

  4. 変更をコミットします:

    git commit -m "Add your message here"
  5. ブランチにプッシュ:

    git push origin feature/YourFeatureName
  6. プルリクエストを開きます。

ライセンス

このプロジェクトはMITライセンスの下で提供されています。詳細はLICENSEファイルをご覧ください。

サポート

問題が発生した場合や質問がある場合は、リリースセクションを確認するか、リポジトリで問題を開いてください。


Deep Research MCPをご利用いただきありがとうございます!このツールがあなたのWebリサーチ能力をさらに高めてくれることを願っています。コーディングを楽しみましょう!

Available Tools

1 tool
deep-research-toolC

Performs extensive web research using Tavily Search and Crawl. Returns aggregated JSON data including the query, search summary (if any), detailed research findings, and documentation instructions. The documentation instructions will guide you on how the user wants the research data to be formatted into markdown.

NameRequiredDescriptionDefault
chunks_per_sourceNoFor 'advanced' search: number of content chunks from each source (1-3).
crawl_allow_externalNoAllow crawler to follow links to external domains.
crawl_categoriesNoFilter crawl URLs by categories (e.g., 'Blog', 'Documentation').
crawl_exclude_domainsNoRegex for domains/subdomains to exclude.
crawl_exclude_pathsNoRegex for URL paths to exclude.
crawl_extract_depthNoExtraction depth for crawl ('basic' or 'advanced').basic
crawl_include_imagesNoExtract image URLs from crawled pages.
crawl_instructionsNoNatural language instructions for the crawler.
crawl_limitNoTotal links crawler will process per root URL (1-20).
crawl_max_breadthNoMax links to follow per page level during crawl (1-10).
crawl_max_depthNoMax crawl depth from base URL (1-2). Higher values increase processing time significantly.
crawl_select_domainsNoRegex for domains/subdomains to crawl (e.g., '^docs\.example\.com$'). Overrides auto-domain focus.
crawl_select_pathsNoRegex for URLs paths to crawl (e.g., '/docs/.*').
crawl_timeoutNoTimeout in seconds for Tavily crawl requests.
daysNoFor 'news' topic: number of days back from current date to include results.
documentation_promptNoOptional. Custom prompt for LLM documentation generation. Overrides 'DOCUMENTATION_PROMPT' env var and default. If none set, a comprehensive default is used.
exclude_domains_searchNoList of domains to specifically exclude from search.
hardware_accelerationNoTry to use hardware acceleration (WebGPU) if available.
include_answerNoInclude an LLM-generated answer from Tavily search (true implies 'basic').
include_domains_searchNoList of domains to specifically include in search.
include_raw_content_searchNoInclude cleaned HTML from initial search results.
include_search_image_descriptionsNoInclude image descriptions from initial search results.
include_search_imagesNoInclude image URLs from initial search results.
max_search_resultsNoMax search results to retrieve for crawling (1-20).
output_pathNoOptional. Path where generated research documents and images should be saved. If not provided, a default path in user's Documents folder with timestamp will be used.
queryYesThe main research topic or question.
search_depthNoDepth of the initial Tavily search ('basic' or 'advanced').advanced
search_timeoutNoTimeout in seconds for Tavily search requests.
time_rangeNoTime range for search results (e.g., 'd' for day, 'w' for week, 'm' for month, 'y' for year).
topicNoCategory for the Tavily search ('general' or 'news').general

TDQS

C2.8/5.0
Behavior2/5

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 mentions the tool returns aggregated JSON data and documentation instructions, but fails to disclose critical behavioral traits: it doesn't indicate whether this is a read-only or mutating operation, potential side effects (e.g., network usage, rate limits), error handling, or performance characteristics. For a complex tool with 30 parameters, 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.

Conciseness3/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences, which is efficient, but it's not optimally front-loaded. The first sentence covers purpose and output, while the second adds detail on documentation instructions. However, for such a complex tool, more structure (e.g., bullet points or clearer sections) could improve readability without adding waste.

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?

Given high complexity (30 parameters), no annotations, and no output schema, the description is incomplete. It covers basic purpose and output format but lacks crucial context: no behavioral transparency, no usage guidelines, and minimal parameter guidance. For a tool of this scale, the description should provide more holistic guidance to compensate for missing structured data.

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 fully documents all 30 parameters. The description adds no parameter-specific information beyond implying the 'query' parameter is central. It mentions 'documentation instructions' which relates to the 'documentation_prompt' parameter, but this is minimal. Baseline 3 is appropriate as 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?

The description clearly states the tool 'performs extensive web research using Tavily Search and Crawl' and specifies it 'returns aggregated JSON data' with specific components like query, search summary, findings, and documentation instructions. It distinguishes the tool as a comprehensive research tool, though without sibling tools, differentiation isn't needed. The purpose is specific but could be more precise about the verb (e.g., 'conducts' vs. 'performs').

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 provides no guidance on when to use this tool versus alternatives, prerequisites, or typical use cases. It mentions the tool's output includes documentation instructions for formatting, but this is about post-processing rather than usage context. With no sibling tools, the bar is lower, but it still lacks basic contextual cues for an agent.

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 observeddeep-research-tool

TDQS

B3/5.0

Scored across 1 tool

Disambiguation5/5

With only one tool, there is no possibility of ambiguity or overlap with other tools. The tool's purpose is clearly defined as performing extensive web research, and there are no other tools to confuse it with.

Naming Consistency5/5

Since there is only one tool, naming consistency is inherently perfect. The tool name 'deep-research-tool' follows a clear and descriptive pattern, and there are no other tools to create inconsistency with.

Tool Count2/5

A single tool is generally too few for a server named 'Deep Research MCP', which suggests a broader scope of research-related operations. While the tool is comprehensive, the lack of additional tools (e.g., for query refinement, result filtering, or different research methods) makes the set feel thin and underdeveloped for the domain.

Completeness2/5

The tool covers basic research functionality but leaves significant gaps in the research domain. There are no tools for managing research queries, saving or organizing results, or performing specialized research tasks, which limits the server's ability to handle complex research workflows effectively.

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