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Chinese Sensitive Words MCP Server

Community
Chinese sensitive/prohibited word detection MCP Server for Xiaohongshu, Douyin, Kuaishou and Bilibili
Category
Other #142 of 230
Stars
★ 104 Popular
Transport
stdio (local process)
Runtime
Node.js
Credentials
Optional API key
License
MIT
Last commit
⚠
Tools
2
51FMRS · D

A Chinese content-compliance focused MCP server featuring multi-platform dictionaries (Xiaohongshu, Douyin, Kuaishou, Bilibili), four-level risk classification with replacement suggestions, plus homophone, character-skipping, phone number/URL detection and NER filtering. Free usage is 100 requests/day, unlimited with a TOKEN. It suits Chinese content creators and operations teams doing pre-publish checks, but its output is not a substitute for official platform moderation.

Strongest · Setup experience 12/20 Weakest · Reliability 8/20

Reliability
8/20
Security and permissions
8/20
Maintenance
11/20
Documentation
12/20
Setup experience
12/20
Why each score
Reliability 8/20
Static review sees only server.json and README; no executable source, test files, or CI workflows are supplied, so MCP initialization, the tools/list handshake, and whether check_sensitive_words / get_word_suggestions behave as declared cannot be verified. The README usage examples are untrusted evidence and do not count as execution proof. Plausible positives: the package is npm-published, uses stdio transport, and names/tools are internally consistent, suggesting a workable happy path. However error handling, quota exhaustion (100/day), and upstream-service outage behavior are undisclosed, and boundary cases are absent. With the static cap of 12 and missing evidence treated as a deduction rather than a best case, the score is 8.
Security and permissions 8/20
No malware, credential theft, or covert exfiltration is visible, and install examples contain only placeholder tokens (your_token_here), so no red line is triggered. Concerns: the server ships user text (potentially containing phone numbers and WeChat IDs) to a remote WORDSCHECK_API_BASE endpoint whose privacy and retention behavior is undocumented; WORDSCHECK_ACCESS_TOKEN is passed in plain env with no scoping or least-privilege note; WORDSCHECK_API_BASE is user-overridable, widening the data-egress surface. The tool surface is read-only detection with no delete/pay/deploy side effects, which limits operational risk. Incomplete disclosure of data flow and permissions yields 8.
Maintenance 11/20
The repository is not archived, carries a clear MIT license, has one open issue, and the description claims daily dictionary updates, indicating ongoing activity; contact channels (WeChat, email) are provided. But within the static evidence there is no CHANGELOG, no semantic release history, no dependency-update policy, and no security-response/security policy; version 1.0.0 makes cadence hard to judge, and CONTRIBUTING.md is referenced but not evidenced. Governance and versioning gaps yield 11.
Documentation 12/20
The README is well layered: bilingual entry points, feature list, tool table, multi-client install (Claude Desktop/Cursor/Windsurf/Claude Code/OpenClaw), runnable JSON config snippets, a full detection output example with JSON return shape, risk-level table, platform dictionary table, quota table (free 100/day, unlimited with token), and contact info. Deductions: no statement on remote-API privacy or data retention, no error-code/timeout/quota-exhaustion troubleshooting, no input-schema detail for the two tools (e.g. platform parameter), and it embeds promotional links (mcp-custom-dev) plus personal contact details. Usable overall but with hidden assumptions, so 12.
Setup experience 12/20
The install path is clear and short: npx -y chinese-sensitive-words-mcp runs as a stdio server, and the README provides copy-pasteable config blocks for mainstream clients plus a one-line Claude Code variant. Node.js/npx is a prerequisite but is not stated explicitly. Custom endpoint and token are configured via env in a conventional, reversible way. Deductions: no version pinning (upstream drift risk), no offline/registration-unavailable fallback, and no connectivity verification step. Constrained by the static cap of 15 (no CI, no committed tests), the score is 12.

Static review · not runListed 2026-09-10

Read the FMRS scoring method →

Fit and risk

What it can accessUses the network

Best for

  • Creators and operators publishing Chinese content on Xiaohongshu, Douyin, Kuaishou and Bilibili
  • Brands, e-commerce and livestream teams that need pre-publish compliance checks on Chinese copy
  • Users who want sensitive-word detection inside Claude Desktop, Cursor, Windsurf, Claude Code or OpenClaw

Not for

  • Scenarios requiring moderation of non-Chinese content
  • Users expecting a fully offline local dictionary (the npm package calls a remote API, which can be pointed elsewhere via WORDSCHECK_API_BASE and WORDSCHECK_ACCESS_TOKEN)
  • Users who treat its output as an official platform moderation verdict

Required permissions

  • Execute the chinese-sensitive-words-mcp package via npx
  • Optionally pass WORDSCHECK_ACCESS_TOKEN as an environment variable to lift the free quota
  • Optionally set WORDSCHECK_API_BASE as an environment variable to point to a custom service address

Risks and side effects

  • Without a TOKEN the free quota is 100 requests/day and usage is limited beyond that
  • Detection only covers its own dictionary, so both misses and false positives are possible; it does not replace official platform moderation
  • When a custom WORDSCHECK_API_BASE is configured, your copy is sent to that address, so you must verify its trustworthiness
  • Judgements about sensitive or prohibited words change over time; although the dictionary is updated daily it may lag behind platform rule changes

Setup

Before you start

Runtime:Node.js

WORDSCHECK_ACCESS_TOKEN optionalsecret Optional access token that lifts the free 100-calls-per-day limit to unlimited usage; obtained by registering with the service (WeChat chenganp or email [email protected]).
Other optional settings (1)
WORDSCHECK_API_BASE optional Optional custom backend API base URL to override the default service endpoint (e.g., a self-hosted https://your-server.com/api).

Add the following to your Claude Desktop / Cursor / Windsurf config file: {"mcpServers":{"chinese-sensitive-words":{"command":"npx","args":["-y","chinese-sensitive-words-mcp"]}}}. For Claude Code run: claude mcp add chinese-sensitive-words -- npx -y chinese-sensitive-words-mcp. For OpenClaw run: npx mcporter config add chinese-sensitive-words-mcp --stdio "npx -y chinese-sensitive-words-mcp". To remove the usage limit, set WORDSCHECK_ACCESS_TOKEN in env; to use a custom service address, also set WORDSCHECK_API_BASE.

claude_desktop_config.json
{
  "mcpServers": {
    "chinese-sensitive-words": {
      "command": "npx",
      "args": ["-y", "chinese-sensitive-words-mcp"]
    }
  }
}

Shown for Claude Desktop. Other clients may use a different file or key (VS Code uses "servers") — the configurator below converts it.

.vscode/mcp.json
{
  "servers": {
    "chinese-sensitive-words": {
      "command": "npx",
      "args": [
        "-y",
        "chinese-sensitive-words-mcp"
      ]
    }
  }
}

Goes in your project's .vscode/mcp.json (VS Code uses a "servers" key).

Terminal
claude mcp add chinese-sensitive-words -- npx -y chinese-sensitive-words-mcp

Run it in a terminal; replace any <…> placeholders with your own values first.

Check that it works

After configuring the client, confirm that check_sensitive_words and get_word_suggestions appear in the tool list; then run a check on text containing "最好用" — a response with risk levels and replacement suggestions confirms the install works.

Troubleshooting

  1. First confirm npx can fetch and run chinese-sensitive-words-mcp
  2. If you hit a quota limit, check whether WORDSCHECK_ACCESS_TOKEN is configured
  3. If the service cannot be reached, check whether WORDSCHECK_API_BASE is set correctly
  4. If you see false positives on person, place or organization names, rely on the NER-based filtering reflected in the results
  5. If the client does not list the tools, make sure the config is under the correct mcpServers key and restart the client

Things to try

Once connected, you can ask your AI assistant things like:

  • Check this Xiaohongshu copy for banned words: the best whitening product on the market, works instantly, add WeChat 13812345678 for a discount
  • What should "最好用" be replaced with?
  • Check whether this Douyin livestream script is compliant: our product is number one, order now
  • Detect sensitive words in this sentence and suggest safe replacements: 加薇信领取优惠

Tools 2

check_sensitive_words read-only
Detect sensitive/prohibited words in text; useful for checking copy, product descriptions and livestream scripts for compliance
get_word_suggestions read-only
Get safe replacement suggestions for a sensitive word marked by the detector

Use cases

Check Xiaohongshu copy for prohibited words and advertising superlatives
Check Douyin livestream scripts and traffic-diversion wording for compliance
Check product descriptions for medical-efficacy claims and false advertising
Get safe replacement suggestions for flagged sensitive words
Detect homophone variants (e.g. “薇信” for WeChat) and character-skipping obfuscation (e.g. “加 微 信”)
Detect phone numbers and external links in copy

Supported clients

Claude Desktop
Cursor
Windsurf
Claude Code
OpenClaw

Listed from the project's documentation, not tested by this site.

Overview

This is an MCP server for detecting sensitive and prohibited words in Chinese text, with dedicated dictionaries for Xiaohongshu, Douyin, Kuaishou and Bilibili. Its massive dictionary is updated daily and covers politics, pornography, violence, gambling, drugs, advertising-law superlatives and medical-efficacy claims. Results are classified by risk level — high (account ban), medium (throttling), low (recommend modification) and tip — and come with safe replacement suggestions. It also supports homophone variant detection, character-skipping detection, phone number/URL detection, and NER-based filtering to reduce false positives on place names, person names and organization names.

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Source revision c7abcbf7fce7 Data synced 2026-10-19 Read the FMRS scoring method