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Deep Research MCP

Deep Research MCP

by ali-kh7

심층 연구 MCP 🌐

심층 연구 MCP
다운로드 릴리스

Deep Research MCP 저장소에 오신 것을 환영합니다! 이 프로젝트는 모델 컨텍스트 프로토콜(MCP)을 준수하는 서버를 제공하며, 포괄적인 웹 리서치를 지원하도록 설계되었습니다. Tavily의 검색 및 크롤링 API를 활용하여 다양한 주제에 대한 자세한 정보를 수집하고, 이 데이터를 구조화하여 대규모 언어 모델(LLM)을 활용한 고품질 마크다운 문서 생성을 지원합니다.

목차

Related MCP server: Deep Research MCP Server

특징

  • MCP 준수 : 서버는 모델 컨텍스트 프로토콜을 준수하여 다양한 도구 및 서비스와의 호환성을 보장합니다.

  • 데이터 집계 : 여러 소스에서 효율적으로 데이터를 수집하고 구조화합니다.

  • 마크다운 생성 : 수집된 데이터를 잘 구성된 마크다운 문서로 변환합니다.

  • 웹 크롤링 : Tavily의 검색 및 크롤링 API를 활용하여 심층적인 웹 조사를 수행합니다.

  • Node.js와 TypeScript : 더 나은 성능과 유지관리성을 위해 최신 기술을 사용하여 구축되었습니다.

설치

Deep Research MCP를 시작하려면 다음 단계를 따르세요.

  1. 저장소를 복제합니다 .

    지엑스피1

  2. 프로젝트 디렉토리로 이동합니다 .

    cd deep-research-mcp
  3. 종속성을 설치합니다 :

    npm install
  4. 서버를 실행합니다 :

    npm start

다운로드 가능한 파일과 특정 버전을 확인하려면 릴리스 섹션을 확인하세요.

용법

서버가 실행되면 API를 통해 서버와 상호 작용할 수 있습니다. API를 효과적으로 사용하는 방법은 다음과 같습니다.

  1. 정보 수집 요청 보내기 :

    특정 주제를 지정하여 서버에 요청을 보내 데이터를 수집할 수 있습니다. 서버는 마크다운 생성에 적합한 구조화된 정보를 반환합니다.

    요청 예시:

    POST /api/research
    Content-Type: application/json
    
    {
      "topic": "Artificial Intelligence"
    }
  2. 구조화된 데이터 수신 :

    서버는 구조화된 형식의 데이터로 응답합니다. 이 데이터는 직접 사용하거나 마크다운 문서로 변환할 수 있습니다.

  3. 마크다운 문서 생성 :

    구조화된 데이터는 API에서 제공되는 함수를 사용하여 마크다운으로 변환될 수 있습니다.

마크다운 출력 예시

# 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 문서

자세한 API 문서는 이 저장소의 docs 폴더를 참조하세요. 사용 가능한 모든 엔드포인트, 요청 형식 및 응답 구조에 대한 정보가 포함되어 있습니다.

엔드포인트

  • POST /api/research : 특정 주제에 대한 정보를 수집합니다.

  • GET /api/status : 서버 상태를 확인합니다.

기여하다

Deep Research MCP 개선을 위한 여러분의 기여를 환영합니다. 기여를 원하시면 다음 단계를 따르세요.

  1. 저장소를 포크합니다 .

  2. 새로운 지점을 만듭니다 .

    git checkout -b feature/YourFeatureName
  3. 변경 사항을 적용하세요 .

  4. 변경 사항을 커밋하세요 .

    git commit -m "Add your message here"
  5. 지점으로 푸시 :

    git push origin feature/YourFeatureName
  6. 풀 리퀘스트를 엽니다 .

특허

이 프로젝트는 MIT 라이선스에 따라 라이선스가 부여됩니다. 자세한 내용은 라이선스 파일을 참조하세요.

지원하다

문제가 발생하거나 질문이 있는 경우 릴리스 섹션을 확인하거나 저장소에서 문제를 열어주세요.


Deep Research MCP를 사용해 주셔서 감사합니다! 이 도구가 여러분의 웹 검색 능력을 향상시켜 주기를 바랍니다. 즐거운 코딩 되세요!

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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