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Agent Skill · 研究检索

readwise-mcp(readwise MCP 搜索)

readwise-mcp 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

readwise-mcp 是研究检索类 Agent Skill,可用宿主为 Codex、Claude、Cursor、Gemini CLI,通过GitHub 仓库获取,权限为调用外部服务;许可证信息还不明确,第三方扫描有提醒项,按安装量在研究检索类的 73,675 个 Skill 里排第 20,254。

总安装 5,284周安装 218GitHub Stars 201下载 1,727核验 2026-05-01

安装说明

本站只整理中文说明,不托管安装包,也不代用户安装。
GitHub
许可证
unknown

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:readwise-mcp(readwise MCP 搜索)
来源仓库:https://github.com/readwiseio/readwise-skills
仓库路径:skills/readwise-mcp
安装命令:
npx skills add https://github.com/readwiseio/readwise-skills --skill readwise-mcp
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 npx skills 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

skills.shnpx skills
npx skills add https://github.com/readwiseio/readwise-skills --skill readwise-mcp

这个 Skill 是做什么的

研究检索类 Skill 主要解决「找资料、核对事实、把来源整理清楚」的问题,通常会让助手先检索再总结。

readwise-mcp 用于查找、检索和筛选相关信息,适合在 Codex、Claude、Cursor、Gemini CLI 中需要根据关键词、任务场景或来源线索快速定位候选结果时使用。适用于研究检索类任务,如资料收集、信息筛选和知识管理,可结合具体需求调用搜索接口。

通过 npx skills add 命令从 GitHub 仓库安装,需确认权限范围和联网能力。建议安装前检查维护状态及是否触发文件读写或命令执行,确保符合安全策略。适用宿主包括 Codex、Claude、Cursor、Gemini CLI,接入前应确认版本、权限和运行环境要求。

来源仓库内路径:skills/readwise-mcp

上手步骤

4 步
  1. 1

    确认自己的宿主工具

    公开资料标注支持 Codex、Claude、Cursor、Gemini CLI;先确认自己用的是其中之一,再按对应工具的 Skills 目录放置。

  2. 2

    拿到安装命令

    可以直接复制的命令是 npx skills add https://github.com/readwiseio/readwise-skills --skill readwise-mc…;命令里的占位符换成自己的路径或密钥再执行。

  3. 3

    安装后重启宿主

    命令执行完要完全退出宿主工具再打开,让新装的 Skill 被重新扫描到,否则列表里可能看不到。

  4. 4

    按最小权限试用

    它需要调用外部服务,先在测试目录或测试账号里跑,确认没问题再放到正式项目。

常见问题

6 条

readwise-mcp 怎么安装?

在终端执行 npx skills add https://github.com/readwiseio/readwise-skills --skill readwise-mcp。装完完全退出宿主工具再打开,让它重新扫描本地 Skill。

readwise-mcp 支持哪些 AI 工具?

公开资料标注支持 Codex、Claude、Cursor、Gemini CLI。其他工具只要能读取本地 Skill 目录,一般也能手动放进去,但要自己确认触发方式是否一致。

readwise-mcp 需要什么权限?

权限级别是调用外部服务。该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

readwise-mcp 是免费的吗?

公开资料没有标注明确许可证,收费和商用限制都无法确认。正式使用前请到来源仓库核对授权说明。

readwise-mcp 用的人多吗?

公开数据是累计安装 5,284 次,近一周安装 218 次,下载 1,727 次,GitHub 201 星。这些数字来自第三方目录统计,只能当作热度参考,不代表质量或安全性。

readwise-mcp 安全吗?

第三方扫描有提醒项,说明存在需要人工确认的行为。安装前请打开源码看清它读了什么、联网到哪里。

值得选的地方和要先想清楚的地方

值得选的地方

  • 同时适配 4 种工具,换客户端不用重新找替代品。

先想清楚的地方

  • 许可证没有明确标注,能不能商用、能不能再分发都不确定。
  • 第三方扫描存在提醒项或未通过项,需要人工逐条确认。
  • 第三方目录整理,版本更新和行为细节以来源仓库为准。

适不适合你

更适合

  • 已经在用 Codex,想直接扩展它能力的人。
  • 需要处理研究检索类任务、又不想每次都手写提示词的人。

不太适合

  • 需要明确授权才能用到公司项目里的场景。
  • 需要厂商级技术支持和 SLA 的生产系统。

研究检索类 Skill 对比

6 个同类
Skill总安装GitHub 星权限许可证扫描
find-skills

寻找技能 · Codex、Claude

31,199,81316,486操作浏览器MIT有提醒
self-improving-agent

自我改进 Agent · OpenClaw

16,464,0023,396接触敏感数据MIT-0通过
soultrace

灵魂追踪 · Codex、Claude

7,588,94412需要联网未明确有提醒
self-improving

自我完善 · OpenClaw

6,829,2841,046需要联网MIT-0通过
microsoft-foundry

微软代工厂 · Codex、Claude

6,614,144764调用外部服务未明确有提醒
readwise-mcp

readwise MCP 搜索 · 本页

5,284201调用外部服务未明确有提醒

按安装量取同分类前几名;数字来自第三方目录统计,只作热度参考,不代表质量结论。

SKILL.md 原文

Readwise MCP

Use the Readwise MCP tools to access the user's Readwise highlights and Reader documents. Readwise has two products:

  • Readwise — highlights from books, articles, podcasts, and more. Includes daily review and spaced repetition.
  • Reader — a read-later app for saving and reading articles, PDFs, EPUBs, RSS feeds, emails, tweets, and videos.

Setup

Add the Readwise MCP server to your client's configuration:

{
  "readwise": {
    "type": "http",
    "url": "https://mcp2.readwise.io/mcp"
  }
}

The server handles authentication via OAuth — the user will be prompted to authorize on first use.

Tool Reference

All tools are prefixed with mcp__readwise__. Each tool name maps directly to a Readwise or Reader API action.

Reader Tools

Searching documents

reader_search_documents(query="spaced repetition")

Hybrid search (semantic + keyword) across all saved documents. Combine with filters to narrow results:

# Search articles saved for later
reader_search_documents(query="machine learning", category_in=["article"], location_in=["later", "shortlist"])

# Search by author
reader_search_documents(query="AI agents", author_search="Simon Willison")

# Search within a date range
reader_search_documents(query="transformers", published_date_gt="2024-01-01")

# Search by tags
reader_search_documents(query="productivity", tags_in=["research"])

Other filters: title_search, summary_search, note_search, url_search, source_search, document_id, limit (default 20, max 100).

Browsing documents

# List 10 most recent inbox items with minimal fields
reader_list_documents(location="new", limit=10, response_fields=["title", "author", "summary", "word_count", "category", "saved_at"])

# Archived articles with a specific tag
reader_list_documents(location="archive", tag=["research"], category="article")

# Unseen inbox items
reader_list_documents(location="new", seen=false)

# RSS feed items
reader_list_documents(location="feed", limit=20, response_fields=["title", "author", "summary", "site_name"])

# Get a specific document by ID
reader_list_documents(id="<document_id>")

# Paginate through results
reader_list_documents(location="later", limit=10, page_cursor="<cursor_from_previous_response>")

Locations: new (inbox), later, shortlist, archive, feed. When the user says "inbox", use new. Only use feed when the user explicitly asks about RSS/feeds.

Use response_fields to limit returned data and save tokens. The id field is always included. Available fields: url, title, author, source, category, location, tags, site_name, word_count, reading_time, created_at, updated_at, published_date, summary, image_url, content, source_url, notes, parent_id, reading_progress, first_opened_at, last_opened_at, saved_at, last_moved_at, html_content, is_deleted.

Tip: unseen documents have first_opened_at=null. Mark as seen via reader_bulk_edit_document_metadata.

Reading and highlighting

# Get full document details including Markdown content
reader_get_document_details(document_id="<id>")

# Get all highlights on a document
reader_get_document_highlights(document_id="<id>")

# Create a highlight — html_content must match the document's HTML exactly
# Get the HTML via reader_list_documents with response_fields=["html_content"]
reader_create_highlight(document_id="<id>", html_content="<p>The exact passage to highlight</p>")

# Highlight with a note and tags
reader_create_highlight(document_id="<id>", html_content="<p>Key insight</p>", note="Connects to spaced repetition", tags=["review", "concept"])

Saving documents

# Save a URL — Reader scrapes it automatically
reader_create_document(url="https://example.com/article")

# Save with metadata and tags
reader_create_document(url="https://example.com", title="Great Article", tags=["research", "ai"], notes="Recommended by Alice")

# Save raw Markdown content (provide a unique URL as identifier)
reader_create_document(title="Meeting Notes", markdown="# Notes from today...", url="https://me.com#notes-march-2025")

Organizing

# Move documents between locations (max 50 per call)
reader_move_documents(document_ids=["<id1>", "<id2>"], location="archive")

# Bulk mark documents as seen
reader_bulk_edit_document_metadata(documents=[{"document_id": "<id>", "seen": true}])

# Bulk update metadata
reader_bulk_edit_document_metadata(documents=[{"document_id": "<id>", "title": "Better Title", "tags": ["ai", "research"]}])

Note: reader_move_documents and reader_bulk_edit_document_metadata share a rate limit of 20 calls/minute. Batch document IDs into fewer calls.

Tags

# List all tags
reader_list_tags()

# Add tags to a document
reader_add_tags_to_document(document_id="<id>", tag_names=["important", "research"])

# Remove tags from a document
reader_remove_tags_from_document(document_id="<id>", tag_names=["old-tag"])

# Add tags to a highlight
reader_add_tags_to_highlight(document_id="<id>", highlight_document_id="<hid>", tag_names=["concept"])

# Remove tags from a highlight
reader_remove_tags_from_highlight(document_id="<id>", highlight_document_id="<hid>", tag_names=["old-tag"])

# Set notes on a highlight (pass null to clear)
reader_set_highlight_notes(document_id="<id>", highlight_document_id="<hid>", notes="My updated note")

Exporting

# Export all documents as a ZIP of Markdown files (async — returns export_id)
reader_export_documents()

# Delta export — only docs updated since last export
reader_export_documents(since_updated="2024-01-01T00:00:00Z")

# Poll for completion
reader_get_export_documents_status(export_id="<id>")

Poll reader_get_export_documents_status until status is "completed", then use the download_url.

Readwise Tools

Searching highlights

# Semantic search across all highlights
readwise_search_highlights(vector_search_term="learning techniques")

# Search with full-text filter on a specific field
readwise_search_highlights(vector_search_term="memory", full_text_queries=[{"field_name": "document_title", "search_term": "psychology"}])

Full-text query fields: document_author, document_title, highlight_note, highlight_plaintext, highlight_tags.

Browsing highlights

# List 20 most recent highlights
readwise_list_highlights(page_size=20)

# Highlights from a specific book
readwise_list_highlights(book_id=12345)

# Highlights from the last month
readwise_list_highlights(highlighted_at_gt="2025-02-01T00:00:00Z")

# Paginate
readwise_list_highlights(page_size=100, page=2)

Creating and editing highlights

# Create a highlight (matched to a book by title/author, or goes into "Quotes")
readwise_create_highlights(highlights=[{"text": "The key insight here", "title": "Book Title", "author": "Author Name"}])

# Create multiple highlights at once
readwise_create_highlights(highlights=[{"text": "First quote", "title": "Book A"}, {"text": "Second quote", "title": "Book B", "note": "Great point"}])

# Update a highlight
readwise_update_highlight(highlight_id=12345, note="New note", add_tags=["concept", "review"], color="blue")

# Delete a highlight
readwise_delete_highlight(highlight_id=12345)

Colors: yellow, blue, pink, orange, green, purple.

Daily review

# Get today's spaced repetition review
readwise_get_daily_review()

Returns highlights selected by the algorithm plus a URL for interactive review.

Example Workflows

Triage the inbox: List recent saves, read each one, decide what's worth the user's time.

reader_list_documents(location="new", limit=10, response_fields=["title", "author", "summary", "word_count", "category", "saved_at"])
reader_get_document_details(document_id="<id>")
reader_move_documents(document_ids=["<id>"], location="later")     # worth reading
reader_move_documents(document_ids=["<id>"], location="archive")   # skip

Search across everything: Find all content on a topic — run both in parallel.

reader_search_documents(query="spaced repetition")
readwise_search_highlights(vector_search_term="spaced repetition")

Quiz on a recent read: Find a finished document, get its content and highlights.

reader_list_documents(location="archive", limit=10, response_fields=["title", "author", "summary", "word_count"])
reader_get_document_details(document_id="<id>")
reader_get_document_highlights(document_id="<id>")

Catch up on RSS: Browse feed items, surface the best ones, mark the rest as seen.

reader_list_documents(location="feed", limit=20, response_fields=["title", "author", "summary", "word_count", "site_name"])
reader_bulk_edit_document_metadata(documents=[{"document_id": "<id1>", "seen": true}, {"document_id": "<id2>", "seen": true}])
reader_move_documents(document_ids=["<id>"], location="later")

Save and annotate: Save a URL, highlight key passages, tag and organize.

reader_create_document(url="https://example.com/article", tags=["research"])
reader_create_highlight(document_id="<id>", html_content="<p>Key passage here</p>", note="This connects to...")
reader_add_tags_to_document(document_id="<id>", tag_names=["important"])

Build a reading recap: See what you've finished and highlighted recently.

reader_list_documents(location="archive", updated_after="2025-03-10T00:00:00Z", response_fields=["title", "author", "word_count", "reading_progress"])
reader_get_document_highlights(document_id="<id>")
readwise_list_highlights(highlighted_at_gt="2025-03-10T00:00:00Z", page_size=50)

平台分布

Codex

34.69%
按下载量换算599

Claude

29.1%
按下载量换算503

Cursor

18.52%
按下载量换算320

Gemini CLI

10.45%
按下载量换算180

安全审计

Gen Agent Trust Hub

通过

Socket

通过

Snyk

可疑

权限和风险

调用外部服务

该 Skill 可能调用第三方服务、云服务或外部模型 API,使用前需要确认账号、额度、数据发送范围和服务条款。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。来源安全扫描存在提醒或未通过项,安装前请自行复核。目前只有一个公开来源,建议同时核对源仓库。

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