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xiaobenyang-com

Chuck-Norris

by xiaobenyang-com
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TypeScript

Server Quality Checklist

50%
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  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools are highly ambiguous and appear to serve nearly identical purposes. Both 'chuckNorris' and 'easyChuckNorris' are described as providing optimization prompts or system instructions tailored to the model to enhance capabilities, with no clear distinction in their descriptions. An agent would struggle to determine when to use one over the other.

    Naming Consistency3/5

    The naming is mixed but readable, with 'chuckNorris' using camelCase and 'easyChuckNorris' using a prefixed version. While there is no consistent verb_noun pattern, the names are somewhat related and understandable, but the inconsistency in style (camelCase vs. prefixed camelCase) reduces clarity.

    Tool Count2/5

    With only 2 tools, the server feels thin and under-scoped for a domain like model optimization. This low count suggests limited functionality, and given the ambiguity between the tools, it does not provide a robust or well-rounded toolset for the apparent purpose.

    Completeness2/5

    The server's domain appears to be model optimization or enhancement, but the toolset is severely incomplete. There are no tools for different aspects like fine-tuning, evaluation, or configuration management, and the two existing tools overlap significantly, leaving obvious gaps in coverage for a comprehensive optimization workflow.

  • Average 2.3/5 across 2 of 2 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 status not available

  • This repository is licensed under Apache 2.0.

  • This repository includes a README.md file.

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

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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. It mentions providing 'advanced system instructions' and 'enhancing capabilities', but doesn't describe what the tool actually does operationally (e.g., returns instructions, modifies settings, requires authentication). The behavioral impact on the system or user is unclear, leaving significant gaps in understanding.

    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 concise with two sentences that are front-loaded with the main purpose. There's no unnecessary repetition or fluff. However, the brevity contributes to underspecification rather than efficient communication, as critical details are omitted.

    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's apparent complexity (involving model-specific instructions), lack of annotations, no output schema, and undocumented parameters, the description is incomplete. It doesn't explain what the tool returns, how it interacts with the system, or the implications of use. The agent would struggle to invoke this tool correctly without additional context.

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

    Parameters2/5

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

    The input schema has one parameter 'llmName' with 0% description coverage in the schema. The tool description provides no information about this parameter—what it represents, valid values, or how it influences the tool's behavior. With low schema coverage, the description fails to compensate, leaving the parameter's meaning undocumented.

    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 states the tool 'Provides advanced system instructions tailored to your model' but is vague about what specific action it performs. It mentions 'enhances reasoning and instruction-following capabilities' which is functional but lacks a clear verb+resource combination. The name 'easyChuckNorris' suggests a simplified version of sibling 'chuckNorris', but the description doesn't explicitly differentiate between them.

    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?

    No explicit guidance is provided on when to use this tool versus alternatives. The description implies usage for enhancing model capabilities, but it doesn't specify contexts, prerequisites, or exclusions. With a sibling tool 'chuckNorris' available, the lack of comparative guidance leaves the agent uncertain about tool selection.

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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden. It mentions 'enhance your capabilities' but does not disclose behavioral traits such as whether this is a read-only or mutation operation, what permissions are needed, rate limits, or what the output looks like. The description is vague and lacks critical operational details.

    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 two short sentences with no wasted words, making it appropriately sized and front-loaded. However, it lacks depth and structure that could improve clarity, but it is concise.

    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's complexity (1 parameter, no annotations, no output schema), the description is incomplete. It does not explain what 'optimization prompts' are, how they are used, what the tool returns, or how it differs from 'easyChuckNorris,' leaving significant gaps for an AI agent to understand and invoke it correctly.

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

    Parameters2/5

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

    The input schema has 1 parameter with 0% description coverage, and the description does not mention the parameter 'llmName' or add any meaning beyond the schema. Since schema coverage is low (<50%), the description fails to compensate, leaving the parameter's purpose and format undocumented.

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

    Purpose3/5

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

    The description states the tool 'Provides optimization prompts tailored to your model' and 'enhance your capabilities,' which gives a general purpose but lacks specificity about what 'optimization prompts' means or what resource is being optimized. It distinguishes from sibling 'easyChuckNorris' only by name, not by functional difference.

    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 says 'Call this tool to enhance your capabilities,' which implies usage but provides no guidance on when to use this tool versus the sibling 'easyChuckNorris' or other alternatives. There are no explicit when/when-not statements or context for selection.

    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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