Context7
Up-to-date docs for AI agents
作者提供的说明(英文原文)
One sentence in, the best-matching visual style out: art reference chart and prompt encyclopedia.
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 …”。
适用于网页版、桌面版和手机版 Claude:
https://xiximm.cloud/mcp打开 Claude Desktop → Settings → Developer → Edit Config,把下面的内容粘贴到 claude_desktop_config.json
{
"mcpServers": {
"feeling-search": {
"command": "npx",
"args": [
"-y",
"feeling-search-mcp"
]
}
}
}在终端中运行这条命令
claude mcp add --transport http feeling-search https://xiximm.cloud/mcpclaude mcp add feeling-search -- npx -y feeling-search-mcp粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"feeling-search": {
"url": "https://xiximm.cloud/mcp"
}
}
}粘贴到项目中的 .cursor/mcp.json,或 ~/.cursor/mcp.json
{
"mcpServers": {
"feeling-search": {
"command": "npx",
"args": [
"-y",
"feeling-search-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": {
"feeling-search": {
"type": "http",
"url": "https://xiximm.cloud/mcp"
}
}
}粘贴到项目中的 .vscode/mcp.json
{
"servers": {
"feeling-search": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"feeling-search-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。
ChatGPT 只能连接互联网上的服务器:
https://xiximm.cloud/mcp菜单名称可能因套餐和版本不同而略有差异。
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。
工具名称和描述与服务器在 2026年11月7日 报告的完全一致。这些内容由作者撰写,不是我们写的;我们以纯文本形式显示。
word_analyze把一句中文/英文口语描述解析成感受词与视觉风格。默认纯本地词典(免费、不联网);本地一个词都没命中且允许时,才上云。返回 score 是相对值不是概率,styles[].matched 是可以直接引用给用户看的命中词。
word_lookup查一个感受词:落在哪些风格、对立词是什么、常一起出现的词、是否标识词。纯本地查表。
word_list列出全部感受词(带共享度)或视觉风格(带定义与词集规模)。调 analyze 之前先拉一次可避免名字拼错。
style_get按风格 id(或中文名)取一个风格:定义、生成用提示词、感受词、年代地域,以及它对应的图片(thumb 320 / full 960 绝对地址,带 kind/credit/license)。kind=work 是有出处的真作品,synthetic 是 AI 示意,placeholder 是占位图。
word_status返回词表版本、规模(词/风格/变种/挂靠率)以及是否配置了云端额度。
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7日
发布者(xiximm.cloud)不是仓库所有者(xixi048);可能是其他人代为上架的
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
在 2118 毫秒内响应(HTTP 200)
无需登录
已读取 5 个工具描述,未发现针对 AI 的隐藏指令
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 3 项适用的基础检查中通过了 2 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。
两种方式都可以:安装在你的电脑上,或者连接云端版本。
GitHub 仓库最后更新于–,最新版本(0.2.6)于8天前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。它通过 URL 连接,所以接受远程 MCP 服务器的应用也可以使用,例如开启开发者模式的 ChatGPT。
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。
MCP server for feeling-search — turn one colloquial sentence into the
visual style that matches it best, plus the feeling words that led there. Every style carries
its name, a one-line definition and the words that triggered it.
A commercial-grade art reference chart, art-style encyclopedia and art-style AI prompt dictionary, exposed over the Model Context Protocol.
"下雨天一个人走在便利店门口,冷白灯光"
→ 感受词:星光 / 窗光 / 冷光 … → 视觉风格:垂直式哥特 / 星图 / 托莱多画派 …
Two shapes. Neither needs an account or an API key.
Local (default, recommended) — the package now ships the whole thing: 762-word lexicon, the 598-style index, and both semantic engines. It runs offline.
{
"mcpServers": { "feeling-search": { "command": "npx", "args": ["-y", "feeling-search-mcp"] } } }
First npx run downloads ~21 MB (the two engines are 8 MB zh + 10 MB en). After that: lexicon hits answer in ~10 ms, whole-sentence queries in ~25 ms, all on your machine.
Remote (no download) — point the client at the endpoint:
{
"mcpServers": { "feeling-search": { "type": "streamableHttp", "url": "https://xiximm.cloud/mcp" } } }
Or keep the local install but forward: npx feeling-search-mcp --remote. Remote answers in 0.76–1.64 s (network round trip + cold start), local in 10–90 ms, and local never sends your text anywhere.
Machine-readable copy of both variants: mcp.json.
| Tool | What it does | Cost |
|---|---|---|
word_analyze | One sentence → the best-matching visual styles + the feeling words behind them. source tells you which layer answered (local lexicon / engine semantic model / cloud fallback) | free, offline |
word_lookup | Look up a single feeling word: definition, near-synonyms, landing styles | free |
word_list | Browse / search the full feeling-word lexicon | free |
style_get | One style in full: definition, parent style, member words, reference images | free |
word_status | Lexicon version, coverage — use it to check the data is fresh | free |
Every tool is read-only and costs nothing. The paid /v2/analyze cloud path is off by default on the public endpoint and only runs when a caller explicitly opts in.
Pricing status (2026-10-28): everything is free — no API key, no quota token. The upstream
service runs free by default; paid 402 / x402 is only enabled if the operator explicitly sets
FS_PAID=1. Only rate limits apply: 20 requests/minute per IP, andPOST /v2/analyzeis capped
at 200 calls per visitor per day. Nothing in this bridge needs to change either way.
Want to know how much traffic the lexicon is getting? The upstream exposes a public GET /v2/stats telemetry endpoint (page views, unique visitors, referrers, AI crawler hits, no personal data) — see https://xiximm.cloud/docs/api.html.
word_analyze only escalates when it has to:
| Layer | When it runs | Latency | What it returns |
|---|---|---|---|
Lexicon (source: "local") | The sentence contains 2+ lexicon words | ~10 ms | Exact feeling words, each with its landing styles |
Semantic engine (source: "engine") | The lexicon hits 0–1 words — i.e. it didn't really understand the sentence | ~25 ms | Real words from the 762-entry lexicon (cosine against 762 word vectors) + styles ranked against 598 style vectors |
Cloud (source: "cloud") | Only on the remote endpoint, and only with allow_cloud: true | 0.8–1.6 s | Paid fallback |
Two engines, picked by language:
有点 lonely 的雨夜 gets shredded by the Chinese tokenizer.moody rainy night in old Hong Kong.lang: "zh" | "en"; default is auto.Negation is computed outside the model and is deterministic: 不要压抑 drops 压抑 and demotes every style that carries it, on both layers.
| Path | Size | Why |
|---|---|---|
data/src/words.json, data/standards/*.json | ~2 MB | Lexicon, 598 styles, aliases, version |
data/standards/zh-engine/ | 8.1 MB | Chinese ternary engine (wasm core + weights) |
data/standards/ternlight/ | 9.8 MB | English engine (wasm, weights embedded) |
data/standards/idx/ | 0.9 MB | 598 style vectors, per language, int8 |
lib/ | <100 KB | Tool definitions, engine loader, lexicon |
Everything is generated from the upstream repo by deploy/pack-mcp.mjs, which also runs a --check drift comparison — the package can't silently fall behind the data.
| Property | Value |
|---|---|
| Transport | Streamable HTTP (sse also accepted) |
| URL | https://xiximm.cloud/mcp |
| Auth | none — anonymous initialize and tools/list both succeed |
| Tools | 5 (word_analyze, word_lookup, word_list, style_get, word_status) |
| npm package | feeling-search-mcp |
| MCP Registry name | cloud.xiximm/feeling-search |
| Env / flag | Default | Meaning |
|---|---|---|
FS_MCP_URL / --endpoint | https://xiximm.cloud/mcp | Endpoint to bridge to |
FS_MCP_TOKEN | unset | Sent as Authorization: Bearer when the endpoint is gated |
--timeout | 30000 | Per-request timeout in ms |
--remote | off | Forward to the endpoint instead of running locally |
--data / FS_DATA_ROOT | package data/ | Where the lexicon and engines live |
stdout carries protocol messages only; all diagnostics go to stderr.
One command checks every channel this package is listed on — npm downloads, GitHub traffic and referrers, MCP Registry versions, directory availability, and open PRs:
node scripts/traffic.mjs # human-readable report
node scripts/traffic.mjs --json # machine-readable, for diffing over time
Requires the gh CLI to be logged in (GitHub traffic needs push permission on the repo). Mind the reporting lag: npm download stats appear 24–48h after the first publish, and GitHub traffic only covers the last 14 days.
referrers is the useful signal for directories — it shows which registry actually sends visitors. Directory sites themselves publish no view counts.
xixi048/feeling-search-skill — same vocabulary, works offline from a local cacheMIT