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CCCpan

Chinese Sensitive Words MCP Server

by CCCpan
OverviewSchemaRelated ServersScoreDiscussions
JavaScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.2

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one detects sensitive words in text, the other provides replacement suggestions. No overlap in functionality.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (check_sensitive_words, get_word_suggestions), making them predictable.

    Tool Count5/5

    With two tools, the server covers the core workflow (detection and suggestion) for a specialized compliance checking domain. No unnecessary tools.

    Completeness4/5

    The server covers the primary needs but lacks a tool for quick single-word checking or batch processing. Minor gap for advanced use cases.

  • Average 4.4/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • 1 of 1 community issues answered or closed in the last 6 months
    • 0 commits in the last 12 weeks
    • No stable releases found
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    With no annotations provided, the description carries full burden. It discloses free tier limits (100 requests/day without token), authentication requirement (WORDSCHECK_ACCESS_TOKEN for unlimited), max character length (3000), and return information (risk level, category, position, suggestions). Missing details on error handling or rate limiting prevent a higher score.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is concise (two sentences plus a note about limits) and well-structured: first sentence declares purpose and output, second sentence provides usage guidance. Every sentence adds value without redundancy.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness4/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    The description covers usage context, authentication, limits, and return values. However, it lacks explicit output format details (e.g., whether results are an array or object). Given no output schema, a slightly more structured specification would improve completeness.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters3/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema description coverage is 100%, so the schema already documents both parameters. The description adds value by naming example content types for the 'text' parameter, but does not provide critical information beyond the schema. Baseline 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description clearly states the tool's function: detecting sensitive words in Chinese text for specific social media platforms. It lists platforms (Xiaohongshu, Douyin, etc.) and typical use cases, effectively distinguishing it from the sibling tool 'get_word_suggestions' which likely provides suggestions rather than detection.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description explicitly advises using the tool for checking marketing copy, product descriptions, live-streaming scripts, or social media posts, providing clear context. However, it does not specify when not to use it or mention alternative tools beyond the implied sibling.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior4/5

    Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

    Without annotations, the description explains behavior: returns suggestions for a specific keyword if provided, otherwise full library organized by category. No side effects or authorization details are needed for this read-only suggestion tool.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness5/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    Two concise sentences, no redundant information. The purpose is front-loaded, and the behavior distinction is clearly stated. Every sentence adds value.

    Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

    Completeness5/5

    Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

    Given the tool's simplicity (one optional parameter, no output schema), the description is fully adequate. It covers purpose, usage scenario, and behavior variants. No further details are necessary.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters4/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    The input schema describes the 'keyword' parameter, and the description adds meaningful context: specifying how the tool behaves with and without the parameter. With 100% schema coverage, the description enhances understanding beyond the schema.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose5/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description explicitly states the tool's purpose: 'Get safe replacement suggestions for sensitive/prohibited Chinese words.' It specifies the resource and action, and distinguishes behavior based on keyword presence. Sibling tool 'check_sensitive_words' hints at a different function, reducing confusion.

    Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

    Usage Guidelines4/5

    Does the description explain when to use this tool, when not to, or what alternatives exist?

    The description provides clear usage guidance: 'Use this when users want to fix flagged words in their content.' This implies the tool should be used after detection. While it doesn't explicitly list when not to use it, the sibling tool name offers differentiation.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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