Agentready Mcp

io.github.AshutoshRaj97由 GitHub 账号 AshutoshRaj97 发布v1.3.1更新于 Oct 24, 2026

Query any docs site via MCP. Submit a URL, ask questions, get cited answers.

发布方自己的服务:agentready.ch: Swiss company directory

已验证Streamable HTTP可网页运行网页搜索与抓取知识与记忆

概览

AI 生成的概览

托管服务,可索引公开网站,让助手就站点文档提出跨页问题并获得带引用的回答,并查看站点事实与能力信息。

可以做什么
  • 列出已索引站点,检查某域名是否已可用
  • 索引或重新抓取公开网站,包括 JavaScript 渲染的单页应用
  • 向站点提出自然语言问题,获得跨多个页面综合并带引用的回答
  • 查看站点的能力清单、新鲜度、schema 与端点,并获取定价、限制等结构化事实
  • 针对某站点的请求生成有依据的只读计划,附证据与风险
  • 对先前的回答进行质量评分
适用场景
当助手需要从文档站点获得可靠答案时适用,尤其是普通网页抓取返回空白或残缺内容的 JavaScript 站点,或答案需要跨多页综合并附引用时。也适合从结构化站点事实中比较定价与限制。
运行要求
可使用 agentready.it.com 的远程端点(HTTP 传输),或用 Node.js 通过 npx 在本地运行 npm 包 @agentreadyweb/mcp。未声明需要账号或 API 密钥;可选变量 AGENTREADY_MCP_URL 用于指向自托管实例。需要能访问 AgentReady 服务的网络。
安装前请注意
提交或刷新站点会把该公开网址发送到 AgentReady 服务器进行抓取,且索引为共享索引,其他使用者也能查询。需要登录的站点无法索引。该包默认在启动时发送匿名遥测,包含随机安装 ID、版本与平台;可将 AGENTREADY_TELEMETRY 设为 0 退出。索引与刷新约需一分钟,并会写入远程索引。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Agentready Mcp,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Web executable,通过 Streamable HTTP。 远程服务在工作区中配置后即可从网页运行时运行。

交给智能体安装

把这段提示词粘贴到 Claude Code、Codex、Cursor 等智能体,或 SourceWeft 对话中。智能体会读取该服务器的安装指南,先向你展示发布者、端点和登录方式,经你同意后再把它添加到你的客户端。密钥不会写进任何命令。

阅读 https://sourceweft.com/mcp/io.github.AshutoshRaj97%2Fagentready-mcp/install.md,并按其中说明把这个 MCP 服务器接入我的客户端。修改任何配置之前,先告诉我它的发布者、端点和登录方式,等我确认。不要把 API 密钥写进命令或我会提交的文件;问我密钥应从哪里获取。

查看智能体所依据的安装指南

其他 MCP 客户端

把它添加到你客户端的 mcpServers 配置中。

{
  "mcpServers": {
    "agentready": {
      "type": "http",
      "url": "https://www.agentready.it.com/api/mcp"
    }
  }
}

README

@agentreadyweb/mcp

[smithery badge] [npm]

What is AgentReady?

AgentReady is a hosted capability layer that makes any website discoverable and usable by agents through MCP.

Paste a URL → AgentReady crawls the site, generates a spec-compliant llms.txt, and hosts a live /ask RAG endpoint and MCP server. Any MCP-compatible agent client can then discover the site, ask natural-language questions, inspect capabilities, and create grounded read-only plans with citations.

The problem it solves: AI agents using web_fetch fetch one page at a time, get empty HTML from JavaScript SPAs (React, Next.js, Vue), and hallucinate when the answer spans multiple pages. AgentReady indexes the whole site, handles JS rendering, and retrieves across pages — so agents get the right answer instead of a confident wrong one.

What's already indexed: Browse the live AgentReady directory → or index any public site yourself.

Key properties:

  • Works on any public URL — static sites, React/Next.js SPAs, Docusaurus, GitBook, custom engines
  • No account required to index your first site
  • Shared index — one team member submits a site, everyone on the team can query it instantly
  • Handles JS-rendered pages that web_fetch returns empty for
  • Capability manifests expose freshness, schemas, endpoints, and read-only limits
  • Grounded plans return evidence, risks, confirmation requirements, and durable receipts

CLI

The same package doubles as a CLI — no install, no account:

bash
# Agent-readiness report card (llms.txt, sitemap, robots, JS-rendering, index status)npx @agentreadyweb/mcp grade yourdocs.com
# Ask any site a question, get a cited answer (auto-indexes new sites in ~60s)npx @agentreadyweb/mcp ask stripe.com "what is the test card number?"
# Index or re-crawl a sitenpx @agentreadyweb/mcp index yourdocs.comnpx @agentreadyweb/mcp refresh yourdocs.com

For CI, dashboards, or scripts, add --json to receive the raw report on stdout (progress remains on stderr):

bash
npx @agentreadyweb/mcp grade yourdocs.com --json | jq '.grade, .score'

The command still exits 1 when the grade is below B.

grade exits non-zero below a B, so you can use it as a CI gate. refresh in your docs deploy pipeline keeps the index fresh automatically:

yaml
# GitHub Actions — after your docs deploy step- run: npx @agentreadyweb/mcp refresh yourdocs.com

Connect any MCP-compatible client to AgentReady:

Local MCP bridge (stdio)

For clients that use an mcpServers configuration, add the AgentReady bridge:

json
{  "mcpServers": {    "agentready": {      "command": "npx",      "args": ["-y", "@agentreadyweb/mcp"]    }  }}

Restart your client. You'll have eight tools available:

  • list_sites — see all indexed websites
  • get_site_capabilities — index on demand, then inspect a site manifest, freshness, schemas, and endpoints
  • get_site_facts — key structured facts extracted from a site
  • ask_site — query any site with cited, multi-page answers
  • plan_site_action — index on demand, then create a grounded, read-only plan and receipt
  • submit_site — index any website so it can be queried
  • refresh_site — re-crawl a site, or perform its initial index when it is new
  • rate_answer — submit quality feedback

Cursor

Add to ~/.cursor/mcp.json:

json
{  "agentready": {    "command": "npx",    "args": ["-y", "@agentreadyweb/mcp"]  }}

Project or terminal setup

bash
npx @agentreadyweb/mcp

Or add the same agentready server to your client's project configuration to share it with your team:

json
{  "mcpServers": {    "agentready": {      "command": "npx",      "args": ["-y", "@agentreadyweb/mcp"]    }  }}

VS Code (GitHub Copilot agent mode)

Requires VS Code 1.99+ with the GitHub Copilot extension. Create .vscode/mcp.json in your project root:

json
{  "servers": {    "agentready": {      "type": "stdio",      "command": "npx",      "args": ["-y", "@agentreadyweb/mcp"]    }  }}

Copilot's MCP tools are only available in agent mode. Commit this file to share with your team.

Windsurf / Zed / other clients

Any MCP client that supports stdio transport works the same way — use npx -y @agentreadyweb/mcp as the command.

WebMCP (no install)

If your client supports HTTP transport, connect directly by URL — no npm required:

https://www.agentready.it.com/api/mcp

Docusaurus plugin

If your docs site uses Docusaurus, auto-index on every build:

bash
npm install @agentreadyweb/docusaurus-plugin
js
// docusaurus.config.jsplugins: [  ['@agentreadyweb/docusaurus-plugin', { domain: 'docs.yoursite.com' }]]

MkDocs plugin

If your docs site uses MkDocs, auto-index on every build:

bash
pip install mkdocs-agentready
yaml
# mkdocs.ymlplugins:  - search  - agentready

If site_url is not set, specify the domain explicitly:

yaml
plugins:  - agentready:      domain: docs.yoursite.com

Starlight plugin

If your docs site uses Starlight (Astro), auto-index on every build:

bash
npm install starlight-agentready
js
// astro.config.mjsimport agentready from 'starlight-agentready'
export default defineConfig({  site: 'https://docs.yoursite.com',  integrations: [    starlight({      plugins: [agentready()],    }),  ],})

Sphinx extension

If your docs site uses Sphinx, auto-index on every build:

bash
pip install sphinx-agentready
python
# conf.pyextensions = [    "sphinx_agentready.extension",]
# Domain is inferred from html_baseurl automatically, or set explicitly:agentready_domain = "docs.yoursite.com"

Available tools

list_sites

Lists all websites currently indexed by AgentReady with their titles and page counts. Use this to check if a domain is already available before submitting it.

get_site_capabilities

get_site_capabilities(domain: string)

Returns the site's capability manifest, freshness state, schemas, and available HTTP/MCP endpoints.

get_site_facts

get_site_facts(domain: string)

Returns key structured facts extracted from a site — pricing tiers, feature lists, limits, and other high-signal data points that agents frequently need.

plan_site_action

plan_site_action(domain: string, request: string)

Creates a grounded, read-only plan with evidence, risks, confirmation requirements, and a durable receipt. It does not execute side effects.

submit_site

submit_site(url: string)

Index any website with AgentReady. Takes ~60 seconds. Handles static sites, server-rendered pages, and JavaScript-heavy SPAs via a four-layer pipeline. Once done, query it with ask_site.

Example: submit_site("https://docs.example.com")

ask_site

ask_site(domain: string, query: string, url?: string)

Ask a question about any website and get a cited answer grounded in its content. Synthesises information across multiple pages. If the site isn't indexed yet, AgentReady crawls and indexes it automatically before answering (~60s).

Example: ask_site("stripe.com", "What are the fees for card payments?")

refresh_site

refresh_site(domain: string)

Force a full re-crawl of a site to pick up new or changed content. If the site is new, AgentReady performs its initial index automatically. Takes ~60 seconds.

Example: refresh_site("docs.example.com")

rate_answer

rate_answer(domain: string, rating: number, request_id?: string, comment?: string)

Submits 1–5 quality feedback, optionally tied to the exact ask_site request.

Deploy webhook

Automatically re-index your docs on every deploy. No auth required — rate limited to once per hour per domain.

bash
curl -X POST https://www.agentready.it.com/api/webhook/refresh \  -H "Content-Type: application/json" \  -d '{"domain": "docs.yoursite.com"}'

Or pass the domain as a query param (works with Vercel/Netlify form-encoded webhook payloads):

https://www.agentready.it.com/api/webhook/refresh?domain=docs.yoursite.com

How indexing works

AgentReady uses a layered approach to handle any public website:

  1. llms.txt / llms-full.txt — if the site publishes one, it's used as a high-quality structured content source
  2. Standard HTML crawl — fetches up to 10 pages via sitemap or link crawling, extracts clean text
  3. __NEXT_DATA__ extraction — for Next.js apps, parses server-side rendered data embedded in the HTML
  4. Jina Reader fallback — for JS-only SPAs that return empty HTML, uses a remote rendering service to extract content

Sites behind authentication or with no public HTML content cannot be indexed.

Environment variables

VariableDefaultDescription
AGENTREADY_MCP_URLhttps://www.agentready.it.com/api/mcpOverride the MCP endpoint (for self-hosted)
AGENTREADY_TELEMETRY1Set to 0 to disable anonymous usage telemetry

Telemetry

This package sends a lightweight anonymous ping to agentready.it.com/api/telemetry each time the MCP bridge starts or a CLI command runs. It contains: a random install ID (stored in ~/.agentready/install-id), package version, Node version, platform, and which mode/command was used. No query content, no domain names, no personal data. Set AGENTREADY_TELEMETRY=0 to opt out.

来源:README.md,提交 dbf7fd0

工具

8

list_sites

List all websites currently indexed in AgentReady. Use this to check if a domain is already available before submitting it — indexed sites return instant cited answers via ask_site. The index covers developer tools, APIs, cloud platforms, frameworks, databases, and more, and grows as new sites are submitted.

无需输入参数。

get_site_capabilities

Return the AgentReady capability manifest for a website. If the site is not indexed yet, AgentReady indexes it automatically before returning its freshness, read-only limits, schemas, and available MCP and HTTP endpoints.

输入参数domain必填

plan_site_action

Turn a natural-language request into a grounded, read-only AgentReady plan for a website. If the site is not indexed yet, AgentReady indexes it automatically first. This prototype never executes side effects; it identifies supported steps, sources, risks, and whether a future execution would require confirmation.

输入参数domain必填request必填

submit_site

Index any website so it can be queried with ask_site. Use this when the site is not yet in list_sites. Handles JS-rendered pages (React, Next.js, Vue SPAs) that web_fetch cannot read — uses a four-layer pipeline: llms.txt → HTTP+cheerio → __NEXT_DATA__ extraction → Jina Reader headless browser. Takes ~60 seconds. Once indexed, ask_site queries are instant.

输入参数url必填

ask_site

Query any website's documentation and get cited, multi-page answers in natural language. Use ask_site when you need: (1) answers that synthesize information across multiple pages of a site, (2) documentation from JS-rendered sites (React, Next.js, Vue SPAs) where web_fetch returns empty or partial HTML, (3) citations linking back to the exact source pages, (4) faster results than fetching and reading individual pages one by one. For sites not yet indexed, ask_site auto-crawls and answers in ~60s — no separate submit_site call needed.

输入参数urlquery必填domain必填

refresh_site

Force a full re-crawl of a site to pick up new or changed content. If the site has never been indexed, AgentReady performs the initial indexing automatically. Use when ask_site returns outdated information or when you know the site has recently been updated. Takes ~60 seconds. After completion, ask_site returns fresh content.

输入参数domain必填

get_site_facts

Return structured facts extracted from a website — pricing, limits, features, contact info, and more. Facts marked as "owner-verified" have been explicitly attested by the site owner and are guaranteed accurate ground truth; use these in preference to scraped RAG answers for critical comparisons. Ideal for price comparison across multiple sites, feature matrix lookups, or any task where you need structured data rather than a natural-language synthesis.

输入参数domain必填category

rate_answer

Rate the quality of a previous ask_site answer from 1 (not useful) to 5 (excellent). Pass the request_id returned in structuredContent when available so the rating can be tied to the exact answer.

输入参数domain必填rating必填commentrequest_id

版本历史

1
  1. v1.3.1最新Sep 16, 2026

评分

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其他 Agentready 集成