Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
Keyless web search and page reading for AI agents, built to get past bot walls.
这三项永远不会合并成一个分数:意见、测量和人气是不同的东西。
我们还没有为它写通俗的介绍。下面是我们从注册中心读取到的事实。
选择你使用的 AI 应用,然后复制设置。
你需要
你用的是哪个应用?
在上方选择你的应用。我们会记住它,下次按钮会显示“安装到 …”。
打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"searchio": {
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add searchio -- npx -y @ioserviceslabs/searchio-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"searchio": {
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"searchio": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"@ioserviceslabs/searchio-mcp"
]
}
}
}浏览器会先询问是否打开应用,然后应用会显示设置并请你确认。密钥的值会留空,由你自己填写。
这个服务器在你的电脑上运行,而 ChatGPT 只能连接互联网上的服务器。请选择其他应用。
你的选择通过 Cookie 保存在这台设备上,无需账户。
这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月8日
注册名称已通过 GitHub 账户 IOServicesLabs 验证,该账户拥有此仓库
我们还没有从 GitHub 读取这个仓库
我们还没有从 GitHub 读取这个仓库
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 1 项适用的基础检查中通过了 1 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于–,最新版本(0.1.1)于27天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:Context7, Codebase Memory, Exa
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Up-to-date docs for AI agents
Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.
Connect AI agents to Exa for web search, content fetching, and multi-step research.
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
searchio is a tool for search, page reading, and product lookup. Use it from the command line, from Python, or over HTTP. Use it as a person or as an AI agent. Search and reading need no API key.
An AI agent can set up searchio for its user in one pass. Follow these steps in order. Stop when the verify step passes.
1. Use Docker when Docker runs. Try:
docker run -i --rm ghcr.io/ioserviceslabs/searchio-mcp
The image holds the full system: fetch ladder, Rust engine sidecar, and a Chromium for the challenge tier. No other install step. Go to step 3.
2. No Docker: install from source. Needs Python 3.10 or newer.
pip install "searchio[mcp]"
curl -fsSL https://raw.githubusercontent.com/IOServicesLabs/searchio-engine/main/scripts/install.sh | sh
The pip command installs the searchio package with the MCP server from PyPI. The curl command adds the Rust engine sidecar at ~/.searchio/bin/se-serve (linux, macOS, Windows with Git Bash). The sidecar is the fast tier-2 backend. It is optional but recommended. For the latest development build use pip install "searchio[mcp] @ git+https://github.com/IOServicesLabs/searchio".
3. Add the MCP server to the client. Claude Desktop and Cursor use this config shape.
Docker:
{
"mcpServers": { "searchio": { "command": "docker", "args": ["run", "-i", "--rm", "ghcr.io/ioserviceslabs/searchio-mcp"] } } }
Source install:
{
"mcpServers": { "searchio": { "command": "searchio-mcp" } } }
4. Verify. Restart the client. Ask: "Use searchio to search the web for the current time in Tokyo." Real results mean the setup works. An error about a missing command means the config points at a path that does not exist; fix the config and retry.
Note. npx @ioserviceslabs/searchio-mcp also works as the client command in step 3. The launcher starts the Docker image when Docker runs. Without Docker it starts the local install from step 2.
You need Python 3.10 or newer.
Path A -- pip (one command):
pip install "searchio[mcp]"
searchio-mcp # MCP server on stdio, ready for an LLM client
searchio doctor checks the install. The Rust engine sidecar is optional. Install it with the curl command in the quick start, step 2.
```
Path B -- Docker (no Python needed):
docker run -i --rm ghcr.io/ioserviceslabs/searchio-mcp # stdio MCP
docker run -p 8080:8080 ghcr.io/ioserviceslabs/searchio-mcp \ searchio-mcp --transport sse --host 0.0.0.0 --port 8080
No prebuilt image for your platform? Build from source:
docker build -t searchio https://github.com/IOServicesLabs/searchio.git
The image holds the full ladder: the Python tiers, the Rust engine sidecar, and a Chromium for the challenge tier. State (clearance, sessions) persists in a volume at /root/.searchio:
docker run -i --rm -v searchio-state:/root/.searchio ghcr.io/ioserviceslabs/searchio-mcp
Path C -- from source:
git clone https://github.com/IOServicesLabs/searchio.git
cd searchio python -m venv .venv .venv\Scripts\activate # Windows
pip install -e '.[mcp]' searchio doctor
searchio doctor shows which fetch tiers work on your machine. Tier 0 is plain HTTP. Tier 1 adds TLS impersonation. Tier 2 adds a real browser for pages that block bots. Tier 2 starts on its own the first time it is needed.
Tier 2 uses the Rust [searchio-engine]( https://github.com/IOServicesLabs/searchio-engine) sidecar when a se-serve binary is present. It boots in milliseconds and has no Chromium. The Docker image includes it. On a host install, one command adds it:
curl -fsSL https://raw.githubusercontent.com/IOServicesLabs/searchio-engine/main/scripts/install.sh | sh
The installer drops the binary at ~/.searchio/bin/se-serve and searchio finds it on its own. You can also set SEARCHIO_ENGINE_BIN to any binary.
Point any MCP client at the server:
{
"mcpServers": { "searchio": { "command": "searchio-mcp" } } }
With Docker, use "command": "docker", "args": ["run", "-i", "--rm", "searchio"].
No Python at all? The npm wrapper picks a backend for you (Docker when present, else a local install):
{
"mcpServers": { "searchio": { "command": "npx", "args": ["-y", "@ioserviceslabs/searchio-mcp"] } } }
searchio search "best noise cancelling headphones 2026"
searchio search "rust async runtime comparison" --intent code searchio search "artemis mission" --domains nasa.gov searchio search "heat pump retrofit" --k 20 --json
You can set an intent: web (default), news, video, shopping, academic, code, forum, reference, local. You can also put site:nasa.gov in the query.
searchio read https://example.com/some/article
searchio read https://example.com/paper.pdf --chars 20000
The command prints the readable text. It climbs walls, bot checks, and JavaScript-only pages on its own. If a page cannot be read, you get a clear reason. You do not get an empty page.
searchio items "sony wh-1000xm5" # one row per product across retailers
searchio items "dyson v15" --domains bestbuy.com --json searchio marketplace "ford f-150" --city seattle # Facebook Marketplace, browser-backed
This part costs money. Set one key:
set ANTHROPIC_API_KEY=... # Windows
searchio research "What changed in the EU battery regulation this year, and who is affected?"
To use DeepSeek instead, set SEARCHIO_LLM_PROVIDER=deepseek and DEEPSEEK_API_KEY=.... The answer comes back with findings. Each finding cites pages the workers read.
searchio is also an MCP (Model Context Protocol) server. AI agents call it over MCP. The same tools that the CLI uses are available to the agent.
Install the MCP extra:
pip install -e '.[mcp]'
Start the server over stdio:
searchio mcp
python -m searchio.mcp
Start the server over HTTP:
searchio mcp --transport streamable-http --host 127.0.0.1 --port 8080 --path /mcp
The server has these tools:
| Tool | What it does |
|---|---|
search | Fused, ranked web search across keyless providers. |
search_items | Product listings with prices, merged across retailers. |
search_local | Facebook Marketplace classifieds near a city (browser-backed). |
youtube_transcript | Caption transcript of one YouTube video: timed segments + full text. |
read_url | Read one URL through the blocking-resistant ladder. Returns markdown. |
research | Run the multi-step research swarm on a question (needs an LLM key). |
stats | Ladder and provider health (per-domain learned tiers, block rates). |
The server also has one resource: searchio://health returns service health as a small JSON object.
The MCP server has a payment gate. It uses the x402 shape. When the gate is on, a billable tool returns a 402 (Payment Required) with an X-Payment challenge until the caller pays.
Set the gate with SEARCHIO_MCP_PAYMENT:
| Value | Result |
|---|---|
(empty, default) | Gate open. Nothing is charged. For local and dev use. |
static: | The caller must send Authorization: Bearer . Without it the tool returns a 402 + X-Payment challenge. |
This is the shape a real verifier uses. It has no ledger. When you host the server for others, put a real x402 verifier in front. The verifier checks payment, then the tool runs. This is how searchio is hosted next to SwarmIO for search and research credits.
searchio serve --port 8080
| Endpoint | What it does |
|---|---|
GET /search?q=...&intent=web&k=10&domains=a.com,b.org&freshness=week&locale=de-DE | Search results |
GET /read?url=...&session=me | Readable text of one page |
GET /items?q=...&domains=... | Product listings merged per product |
GET /marketplace?q=...&city=seattle | Facebook Marketplace listings |
GET /transcript?url=...&lang=en | YouTube caption transcript (400 not-a-video · 404 no captions · 451 refused) |
POST /research {"question": "..."} | The AI research swarm |
GET /providers, GET /stats, GET /healthz | Status |
Errors are honest status codes: 422 for a bad request, 400 for a target the policy refuses, 451 for a page blocked by an anti-bot wall, 502 or 503 when providers failed or are all cooling down after failures. A search answer lists providers_used, providers_failed and providers_skipped (breakers open, with the cooldown left).
If several people or agents share one server, give each its own session on /read. Then cookies from one never ride along on another's requests.
The Dockerfile in this repo builds the full ladder: Python tiers, the Rust engine sidecar, and a Chromium for the challenge tier. See Path B in section 1 for the build and run commands. docker compose up does the same with the state volume wired up.
Inside the container, tier 2 is the engine sidecar first; the patchright Chromium takes the pages that need a real browser (the challenge tier). Cache, learned per-domain tiers, and clearance cookies live in the searchio-state volume, so they survive restarts. Keys for the research swarm go in as environment variables, the same names as in section 10.
import asyncio
from searchio.engine import Engine
async def main(): eng = Engine() res = await eng.search("lithium iron phosphate battery lifespan", intent="web", k=5) for doc in res.docs: print(doc.title, doc.url)
doc = await eng.read("https://example.com/article") print(doc.text[:500])
items = await eng.find_items("sony wh-1000xm5") for it in items: print(it.title, it.price, it.url)
await eng.close()
asyncio.run(main())
Everything is an environment variable with the SEARCHIO_ prefix, or a line in a .env file next to where you run it. The ones people change most:
| Variable | Default | Meaning |
|---|---|---|
SEARCHIO_MAX_TIER | 2 | 0 HTTP only, 1 adds TLS impersonation, 2 adds the browser |
SEARCHIO_ROBOTS_POLICY | warn | enforce, warn, or off |
SEARCHIO_PROXY | none | Proxy URL for the HTTP tiers |
SEARCHIO_FACEBOOK_CITY | none | Default city for Marketplace searches |
SEARCHIO_CACHE_ENABLED | true | Cache fetched pages for an hour |
SEARCHIO_STATE_DIR | ~/.searchio | Where cache, profiles and clearance cookies live |
SEARCHIO_MCP_PAYMENT | | MCP payment gate (see 6.2) |
SEARCHIO_MCP_BEARER | none | Bearer token the MCP gate checks |
research calls a paid model.searchio providers lists every source and whether it is configured.searchio doctor is the first thing to run when something looks off.