프로토타입 · 미리 보기: 공식 레지스트리의 모든 서버를 담고 있으며 30분마다 동기화해요. 리뷰와 계정 기능은 다음 단계에서 열려요.
PopNx MCPAI를 쓰는 사람을 위한 MCP 서버 · 쉬운 말로 설명해요

ResearchZosho

제작자: Wyrdsekai버전 0.6.9i★ –i내 컴퓨터에서 실행
레지스트리 이름io.github.Wyrdsekai/researchzosho

설치

개요

제작자가 쓴 설명(영어 원문)

Deep research assistant that checks its citations against the sources and keeps what it found.

PopNx MCP의 세 기둥

이 세 가지는 절대 하나의 점수로 합치지 않아요. 의견, 측정, 인기는 서로 다른 것이니까요.

할 수 있는 일

이 서버는 아직 쉬운 말로 된 요약을 쓰지 못했어요. 아래는 레지스트리에서 읽어 온 사실이에요.

  • 연결 방식: 내 컴퓨터에서 실행
  • 전송 방식: stdio
  • 카테고리: 검색 및 AI 기억

설치

쓰고 있는 AI 앱을 고른 다음 설정을 복사하세요.

설치하기 전에

  • 기본 점검: 1개 중 1개 통과
  • 내 컴퓨터에서 내 사용자 계정의 권한(파일, 네트워크, 앱)으로 실행돼요.
  • 직접 입력할 값: RESEARCHZOSHO_LIBRARY

필요한 것

  • npmNode.js 설치(npx 명령어용)

어떤 앱을 쓰세요?

위에서 앱을 고르세요. 기억해 두었다가 다음부터는 버튼에 “…에 설치”라고 표시해요.

Claude Desktop → Settings → Developer → Edit Config를 열고 claude_desktop_config.json에 이 내용을 붙여 넣으세요

{
  "mcpServers": {
    "researchzosho": {
      "command": "npx",
      "args": [
        "-y",
        "@wyrdsekai/researchzosho-mcp"
      ],
      "env": {
        "RESEARCHZOSHO_LIBRARY": "your-value"
      }
    }
  }
}

터미널에서 이 명령어를 실행하세요

claude mcp add researchzosho --env RESEARCHZOSHO_LIBRARY=your-value -- npx -y @wyrdsekai/researchzosho-mcp

실행하기 전에 빈 값을 채워 주세요.

Cursor에서 열기

또는 설정을 직접 복사하기

프로젝트의 .cursor/mcp.json 또는 ~/.cursor/mcp.json에 붙여 넣으세요

{
  "mcpServers": {
    "researchzosho": {
      "command": "npx",
      "args": [
        "-y",
        "@wyrdsekai/researchzosho-mcp"
      ],
      "env": {
        "RESEARCHZOSHO_LIBRARY": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

VS Code에서 열기

또는 설정을 직접 복사하기

프로젝트의 .vscode/mcp.json에 붙여 넣으세요

{
  "servers": {
    "researchzosho": {
      "type": "stdio",
      "command": "npx",
      "args": [
        "-y",
        "@wyrdsekai/researchzosho-mcp"
      ],
      "env": {
        "RESEARCHZOSHO_LIBRARY": "your-value"
      }
    }
  }
}

브라우저가 앱을 열지 물어본 다음, 앱이 설정을 보여 주고 확인을 요청해요. 키 값은 비워 두니 직접 입력하세요.

이 서버는 내 컴퓨터에서 실행되는데, ChatGPT는 인터넷에 있는 서버에만 연결할 수 있어요. 다른 앱을 고르세요.

선택한 앱은 이 기기의 쿠키에만 저장돼요. 계정은 필요 없어요.

직접 입력할 값

  • RESEARCHZOSHO_LIBRARY env
    The library folder (default ~/researchzosho-library)

이 설정은 제작자의 레지스트리 정보로 만들었어요. README와 다르면 README를 따르세요.

할 수 있는 일

내 컴퓨터에서 실행돼요. 설치하면 AI 앱에 도구 목록이 나타나요.

기본 점검i

누구나 볼 수 있는 정보를 점검했고, 코드는 읽지 않았어요. 해당하지 않는 항목은 개수에서 뺐어요. · 2026년 11월 8일 점검

  • ✓ 통과

    제작자와 저장소 일치

    레지스트리 이름이 GitHub 계정 Wyrdsekai 기준으로 확인되었고, 이 계정이 저장소를 소유하고 있어요

  • … 아직 점검 안 함

    계속 관리되고 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • … 아직 점검 안 함

    라이선스 있음

    아직 GitHub에서 이 저장소를 읽어 오지 않았어요

  • – 해당 없음

    엔드포인트 응답

    내 컴퓨터에서 실행되는 서버라 인터넷 엔드포인트가 없어요

  • – 해당 없음

    OAuth로 로그인

    내 컴퓨터에서 실행되며, 대신 로컬 설정을 사용해요

  • … 아직 점검 안 함

    도구 설명에 숨겨진 지시 없음

    내 컴퓨터에서 실행돼요. 도구 목록은 인터넷 엔드포인트가 있는 서버에서만 읽을 수 있어요

검색 및 AI 기억 카테고리의 다른 서버와 비교i

  • 이 카테고리의 다른 서버 5676개 중 0%보다 스타가 많아요
  • 이 카테고리의 다른 서버 5676개 중 96%보다 최근에 레지스트리에 공개됐어요
  • 기본 점검 결과가 이 카테고리의 다른 서버 5676개 중 100%와 같거나 더 좋아요

레지스트리에서 이 카테고리의 활성 서버 전체와 비교했어요. 스타는 게시자가 저장소를 소유한 경우에만 세요.

자주 묻는 질문

ResearchZosho, 안전한가요?

해당하는 기본 점검 1개 중 1개를 통과했어요. 코드는 읽지 않았어요. 키나 토큰을 넣기 전에 서버가 어떤 권한을 요구하는지 확인하고, 꼭 필요한 권한만 있는 키를 쓰세요.

어떤 키나 코드가 필요한가요?

레지스트리에 필수 키가 나와 있지 않아요. 다만 처음 쓸 때 로그인을 요구하는 서버도 있어요.

내 컴퓨터에서 실행되나요, 클라우드에서 실행되나요?

내 컴퓨터에서 실행돼요. AI 앱이 필요할 때 실행하고, 데이터는 제작자의 서버를 거치지 않아요(서버 자체가 온라인 서비스를 호출하는 경우는 제외).

아직 관리되고 있나요?

GitHub 저장소는 마지막으로 – 변경됐고, 최신 버전(0.6.9)은 그저께 레지스트리에 올라왔어요.

어떤 앱에서 쓸 수 있나요?

Claude Desktop, Claude Code, Cursor, VS Code처럼 MCP를 지원하는 앱에서 쓸 수 있어요. 내 컴퓨터에 Node.js, Docker도 있어야 해요.

대신 쓸 수 있는 건 뭐가 있나요?

같은 카테고리에는: Context7, Codebase Memory, Exa

대안

같은 카테고리에서 다른 제작자가 만든 서버예요. 스타 순으로 정렬했어요.

카드 보는 법:★ GitHub 스타i세 기둥: 리뷰 · 점검 · 사용 정도i업데이트 = 최신 버전을 레지스트리에 공개한 날i

Context7

제작자: upstash

Up-to-date docs for AI agents

컴퓨터 또는 클라우드★ 64,224MIT어제 업데이트
설치

Codebase Memory

제작자: DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

내 컴퓨터에서 실행★ 48,148MIT21일 전 업데이트
설치

Exa

제작자: exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

클라우드에서 실행★ 5,213MIT2개월 전 업데이트
설치

제작자의 README

README 열기

제작자의 저장소에서 쓰인 그대로 가져왔어요. PopNx MCP가 검토하거나 보증한 내용이 아니에요. HTML, 이미지, 스크립트는 제거했어요. 링크를 누르면 이 사이트를 벗어나요.

ResearchZosho

研究蔵書, *the research holdings.* The Research Harness For The Rest Of Us.

ResearchZosho is an open-source deep research assistant. Give it a question and it searches the web and scholarly papers, or only your own PDFs, and writes a report in which every claim points at a source. It runs on your own computer with a local LLM (llama.cpp, Ollama, LM Studio) or a hosted API.

Three people, one problem.

Your vet mentions a new drug for your dog's cancer. The follow-up is in three days. You want to know what the studies say: how well it works, for which breeds, and what the side effects are. The answer is spread across twenty papers, a regulator's review, and many pages that only look like medical advice.

You are tracing your family. Your great-grandfather is in the 1911 census in Osaka. By 1921 he is gone from every record the family knows about. Somewhere there are ship manifests, a prefecture's emigration lists, and an old newspaper notice. Most of them are in Japanese. None of them are on the first page of a search.

You are writing one chapter of a thesis on the influence of the Soviet-Afghan War on 1990s British science fiction. You have forty papers and articles saved, in three languages. You have one week to work out what they agree on, where they disagree, and what nobody has looked at yet.

None of that can be read in one evening. That is the job ResearchZosho does. You give it the question. It reads and notes each fact with the source it came from. When it is done, a write-up is waiting: what it found, where the sources disagree, and what it could not resolve. Every claim in it points at a source you can check. It keeps what it found, so the next question on the same subject does not start from zero. If a paper you used gets a new version, or two of your own findings contradict each other, it tells you.

Nothing counts as valid until you have reviewed it.

It runs on your own computer with a model you pick, local or hosted. It is free and open source. It is for anyone who needs reliable research done.

This is The Research Harness For The Rest Of Us.

→ [LIBRARIAN_HOWTOUSE.md](docs/LIBRARIAN_HOWTOUSE.md): how to use it day to day → [LIBRARY_PROTOCOL.md](docs/LIBRARY_PROTOCOL.md): for programmers, how a program talks to it → [FAMILY_HISTORY.md](docs/FAMILY_HISTORY.md): researching your family, from a relative's notes to records and a family tree → [YOUTUBE.md](docs/YOUTUBE.md): researching what is on YouTube — channels, what is said in a video and when, channels like one you know — without an API key

→ [BEDROCK.md](docs/BEDROCK.md): using the models in your own AWS account (Amazon Bedrock) with your own AWS sign-in

→ [MODELS.md](docs/MODELS.md): which model to run, from what we measured: the local models by your graphics card's VRAM first, then the hosted ones by what they got right, how long a run took and what it cost


What you can do with it

Send it a question. It works out what needs answering. It reads in parallel: the web, and the scholarly literature by DOI through Crossref and OpenAlex, and when the question calls for them the web archives, the Internet Archive, digitised historical newspapers, the National Diet Library's digitised Japanese books, the archives of source code and the video sites beside YouTube. A page that is gone is read from its archived copy. It writes the answer up in sections. It keeps going until the job is done, or for as long as you allow. It also reads sources in other languages, especially when the question points at a non-English language. The write-up says which languages its sources were in.

Check the answer. Every write-up ends with a table of the facts it used. Each fact has its source and a quote. A numbered list of unique sources follows. Before the write-up is shelved, each sentence that cites a source is checked against that source. A problem is called out. If it cannot read a source, because of a paywall, a login or another issue, it lists that source so you can supply the text yourself.

Keep bad sources out. A claim with one source stays a draft until a second, independent source backs it. A retracted paper disputes the claims that cite it. A site you ban is not used. A site you trust counts as a primary source.

Decide what stands. Each claim starts as a draft. You accept it, dispute it or retire it. The library uses what you accept next time.

Talk to it. researchzosho chat is a conversation with the Librarian. Ask what the shelves hold, ask a follow-up, say "find out …" to start a research run, ask "how did that go?", or go through the inbox. Every reply cites a look-up you can open. A figure it cannot back is marked as its guess. The same conversation is on the pages at /chat.

Ask what you already know. researchzosho ask "…" shows what the library has on a question: each claim, where it came from, and whether it is accepted or still in dispute.

Bring your own documents. A PDF, a web page, or a whole folder. Point it at a folder and every document in it is shelved as a collection. Then ask a question that searches only those documents, or one that starts there and goes to the web afterwards for more.

Get book recommendations. Ask for a book to read first on a subject, what a book covers, or what to read next. The recommendations come from the library catalogues (Open Library, Japan's National Diet Library and others), never from the model's memory, so every book named is a real one with a link to its record. It looks past the bestseller lists and says what each book covers and who it is for.

Use your own shelf. Point it at your Calibre library and it learns what you go for: the subjects, the authors you come back to, the decades and languages. researchzosho myshelf like " " recommends books on your shelf like one by what the texts say, not by who wrote them. A nightly reading of your books builds a card for each, which describes the book and nothing about you. Say which rows you liked and disliked, and the next lists move.

Know what you own. Import your purchase records (the zip Amazon sends you, a mailbox of order confirmations, a card statement) and the library can tell what you already have. Every night it checks the open product-safety feeds against them, and a recall that may concern you waits in the inbox.

Research a purchase. Ask what to buy and the run looks only at what shops list right now in your country, with the price, the datasheet, recall notices, repairability and what owners say. The table judges fit against your need, not popularity. For used, vintage or out-of-print things it asks eBay and gives you ready search links for the marketplaces no program may read.

Ask how to do or make something. A craft, a hobby, a sport, a martial art or a trade. The answer comes with the kit: every material and tool, what it is for, what stands in for it, and where to get it. For a sport or a martial art, the gear in the order you need it and where to learn. For a community you want to join, such as the SCA or a reenactment society, how to join and what its rules ask of your equipment.

Podcasts and videos. A question about a podcast, a host or an episode reads what was said, with the time it was said, and cites the episode. Follow a show and its new episodes are noted as they arrive. A question about videos searches YouTube and the sites beside it and cites the moment.

Ongoing research, every night. Every night it runs the searches you have declared ongoing. It looks into questions it could not answer before. It re-reads a few accepted claims against their sources. It notices new versions of papers you cite. It keeps a dated backup. On Sundays it leaves you two lists: claims that look like duplicates, and documents you might clean out. It never merges, deletes or decides for you.

See how things connect. When a claim says a person lived in a place, or an author wrote a work, the library keeps the connection. researchzosho map "Arthur Ellis" shows everything around a name. The service draws it in your browser. This connects the research you have built up.

Open it in a browser. The service has basic pages: search, ask, read a claim with its sources, walk the subjects, see what changed and what is running, send a question, and download any write-up as Markdown or PDF. For a desktop editor, researchzosho vault writes a folder that Obsidian, SoloMD or SilverBullet can import.

Let other programs use it. Claude Code, Codex, Gemini CLI and any other MCP host can ask the library directly. A small HTTP service with Python and Java clients covers everything else.

Reach it from outside your home. researchzosho share makes the library reachable now, behind an encrypted relay (a Cloudflare quick tunnel at first, Tailscale Funnel for an address that lasts), free, with no port opened at home: anyone with the link sees the sign-in page, and only a person you invited gets in, with their name and password, a passkey, or a one-time code as a second step. Invite a person with one link; they choose their password when they open it.

Sign in simply. No tokens to paste. You make your account once, with a name and a password, and sign in from any phone or computer. On the library's own computer your browser needs no sign-in. Add a passkey, so your fingerprint or face signs you in, or a one-time code from an authenticator app as a second step. Programs still use tokens.

Getting started

Research runs need a model server that speaks the OpenAI chat API. That can be a local one (llama.cpp, Ollama, LM Studio) or a hosted API with a key (OpenAI, DeepSeek, Gemini, OpenRouter and others). You also need Java 21 or newer, unless you take a build that carries its own runtime. The installer picks that build for you when Java is missing.

Research runs also need a web search backend. It matters as much as the model. Use a Brave Search API key (free plan), or SearXNG (setup starts one with Docker). With neither, the built-in fallback searches Wikipedia and the scholarly literature only. Asking what the library already has works without a model.

git clone https://github.com/Wyrdsekai/researchzosho.git

cd researchzosho bin/researchzosho setup

When it is done, open http://127.0.0.1:4649/ in a browser.

setup asks a few questions. Each has an answer already filled in:

  • where the library installs
  • which model it uses, and which it checks for
  • which web search backend
  • whether search is by meaning, by keyword, or both
  • whether to run it as a service
  • which programs to connect

Then it shelves a document you name and answers a question about it. Run it again any time to change one thing. --yes takes every default.

The release installs on Linux, macOS and Windows. If you already use CodeZaiku, codezaiku install researchzosho fetches the release, checks it, and runs setup.

Settings live in ~/.researchzosho/config as key = value lines, or in the environment. These are the ones you are most likely to change:

settingwhat it is
RESEARCHZOSHO_DRIVEthe model server, for example http://localhost:8080 or https://api.openai.com/v1
RESEARCHZOSHO_MODELthe model name to ask for
RESEARCHZOSHO_JUDGE_DRIVE / RESEARCHZOSHO_JUDGE_MODELa second, stronger model for the judgment steps (planning, the critic, the write-up, the citation check), while a local model does the reading
RESEARCHZOSHO_EMBEDan embeddings server (OpenAI embeddings call), so search works by meaning as well as by words; off for words only. researchzosho embed start runs one with Docker (Text Embeddings Inference, about nine times faster than llama.cpp on the same model)

| `RESEARCHZOSHO_LIBRA

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