Feeling Search

npx add-mcp 'https://xiximm.cloud/mcp'

feeling-search-mcp

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 …

Install

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.

Tools

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, and POST /v2/analyze is 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.

Two layers, and which one answers

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:

• Chinese — ask in whole Chinese sentences. Any Han character makes it the Chinese engine; don't mix, e.g. 有点 lonely 的雨夜 gets shredded by the Chinese tokenizer.
• English — ask in whole English sentences. moody rainy night in old Hong Kong.
• Force one with 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.

What's in the package

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.

Endpoint

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

Configuration

Env / flag	Default	Meaning
FS_MCP_URL / --endpoint	https://xiximm.cloud/mcp	Endpoint to bridge to
FS_MCP_TOKEN	unset	Sent as Authorization: Bearer <token> when the endpoint is gated
--timeout	30000	Per-request timeout in ms
--remote	off	Forward to the endpoint instead of running locally
--data <dir> / FS_DATA_ROOT	package data/	Where the lexicon and engines live

stdout carries protocol messages only; all diagnostics go to stderr.

Reach & traffic

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.

Related

• Agent skill: xixi048/feeling-search-skill — same vocabulary, works offline from a local cache
• Site + API: xiximm.cloud

License

MIT