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BACH-AI-Tools

Chuck Norris MCP Server

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of ambiguity or overlap between tools. The single tool 'api_endpoint' stands alone with no other tools to confuse it with.

    Naming Consistency5/5

    A single tool inherently has perfect naming consistency, as there are no other tool names to compare it against. The name 'api_endpoint' follows a clear and descriptive pattern.

    Tool Count1/5

    A single tool is too few for a server named 'Chuck Norris MCP Server', which suggests a domain related to jokes, facts, or entertainment. One tool cannot provide meaningful coverage for such a broad or interactive domain.

    Completeness1/5

    The tool set is severely incomplete for the implied domain. With only one generic tool for adding endpoints, it lacks any specific operations for retrieving Chuck Norris jokes, facts, or other related content, leaving obvious gaps.

  • Average 2.4/5 across 1 of 1 tools scored.

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

    • No community issues 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 is passing
  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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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

  • Behavior2/5

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

    No annotations are provided, so the description carries the full burden of behavioral disclosure. The description hints at a manual addition process ('manually add'), suggesting a write operation, but doesn't clarify permissions, side effects, or response format. It lacks details on rate limits, error handling, or what 'endpoint information' entails, leaving significant gaps in understanding the tool's behavior.

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

    Conciseness4/5

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

    The description is a single, concise sentence in Chinese, making it efficient and front-loaded. However, it could be more structured by explicitly stating the action and context, but it avoids unnecessary verbosity. Every word contributes to the core message, though it lacks depth.

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

    Completeness2/5

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

    Given the tool has no annotations, no output schema, and a vague description, the description is incomplete. It doesn't explain what 'endpoint information' includes, how the addition process works, or what the expected outcome is. For a tool that likely involves configuration or creation (implied by 'add'), more context on behavior and results is needed to be adequately helpful.

    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?

    The input schema has 1 parameter with 100% description coverage ('参数' meaning 'parameter'), but the schema description is minimal. The tool description doesn't add any parameter-specific details beyond what's in the schema—it doesn't explain what 'param' represents (e.g., endpoint URL, configuration data) or how it relates to the RapidAPI page. With high schema coverage, the baseline is 3, but the description fails to enhance parameter understanding.

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

    Purpose2/5

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

    The description '请根据 RapidAPI 页面手动添加端点信息' (Please manually add endpoint information based on the RapidAPI page) is vague about the specific action. It mentions 'add endpoint information' but doesn't specify what resource or system this applies to, nor does it clarify if this creates, updates, or configures something. While it's not a tautology (doesn't just restate the name), it lacks specificity about the verb and target resource.

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

    Usage Guidelines2/5

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

    The description provides minimal guidance: it implies usage is based on a RapidAPI page, but doesn't specify when to use this tool versus alternatives (though there are no sibling tools listed, so this is less critical). It doesn't mention prerequisites, exclusions, or context for invocation, leaving the agent with little direction on appropriate usage scenarios.

    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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