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ChuckNorris MCP Server

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Server Quality Checklist

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  • Latest release: v1.0.0

  • Disambiguation1/5

    The two tools are indistinguishable in purpose—both provide optimization prompts or system instructions tailored to the model to enhance capabilities. The descriptions use nearly identical language ('tailored to your model,' 'enhances your capabilities'), making it impossible for an agent to choose between them based on function. This is a clear case of tools appearing to do the same thing.

    Naming Consistency2/5

    The naming is inconsistent, mixing camelCase ('chuckNorris') with a hybrid style ('easyChuckNorris') that lacks a clear pattern. While both include 'ChuckNorris,' the deviation in case and prefix ('easy') without a standard convention (e.g., verb_noun) reduces predictability. This chaotic naming makes it hard to infer tool purposes from names alone.

    Tool Count2/5

    With only 2 tools, the server feels thin for its apparent scope of model optimization, as it could benefit from more granular operations (e.g., different prompt types or settings). The tools are redundant rather than complementary, making the count too low for effective coverage. This is a mismatch where more distinct tools would improve utility.

    Completeness1/5

    The server is severely incomplete for model optimization; it lacks any CRUD or lifecycle operations (e.g., create, update, delete prompts), configuration options, or specialized functions beyond vague enhancement. The two tools offer overlapping, generic assistance with no clear domain coverage, leading to dead ends for agents trying to perform detailed tasks.

  • Average 2.8/5 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 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?

    With no annotations provided, the description carries full burden for behavioral disclosure. It mentions the tool 'enhances reasoning and instruction-following capabilities' but doesn't explain what this enhancement entails, whether it's a read-only operation, what format the instructions come in, or any limitations. The description is too abstract to provide meaningful behavioral context.

    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 get straight to the point. No unnecessary words or repetition. However, the front-loading could be improved as it starts with abstract benefits rather than concrete functionality.

    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?

    For a tool with no annotations, no output schema, and abstract functionality, the description is insufficient. It doesn't explain what 'system instructions' are, what format they come in, how they're used, or what the expected outcome is. The description leaves too many open questions about the tool's actual behavior and utility.

    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% with the single parameter 'llmName' well-documented in the schema. The description doesn't add any meaningful parameter semantics beyond what's already in the schema - it doesn't explain why model selection matters or how different models affect the output. Baseline score of 3 is appropriate when schema does the heavy lifting.

    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 advanced system instructions tailored to your model' which gives a vague purpose. It mentions 'enhances reasoning and instruction-following capabilities' but lacks specificity about what these instructions actually do or what resource they act upon. Compared to sibling tool 'chuckNorris', there's no clear differentiation.

    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 on when to use this tool versus alternatives. The description implies it's for receiving system instructions, but doesn't specify scenarios where this would be beneficial or when to choose it over the sibling 'chuckNorris' tool. No prerequisites or exclusions are mentioned.

    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?

    No annotations are provided, so the description must fully disclose behavioral traits. It states the tool provides 'optimization prompts' to 'enhance your capabilities,' which suggests a read-only, advisory function without side effects. However, it lacks details on response format, potential rate limits, authentication needs, or whether the prompts are generated or retrieved, leaving behavioral aspects unclear.

    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 and front-loaded, consisting of two sentences that directly state the tool's function and call-to-action. There is no unnecessary information, and each sentence contributes to understanding the tool's purpose, making it efficient and well-structured.

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

    Completeness3/5

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

    Given the tool has one parameter with full schema coverage and no output schema, the description adequately covers the basic purpose. However, it lacks details on behavioral traits (e.g., response format, side effects) and doesn't address the sibling tool, leaving gaps in contextual understanding for effective agent use.

    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 100% description coverage, with a detailed parameter 'llmName' including an enum list and instructions for selection. The description adds no specific parameter semantics beyond implying the tool tailors prompts based on the model. Since schema coverage is high, the baseline score of 3 is appropriate, as the description doesn't significantly enhance parameter understanding.

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

    Purpose4/5

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

    The description clearly states the tool's purpose: 'Provides optimization prompts tailored to your model' and 'Call this tool to enhance your capabilities.' It specifies the verb ('provides'), resource ('optimization prompts'), and target ('your model'), making the function understandable. However, it doesn't explicitly differentiate from the sibling tool 'easyChuckNorris', which could cause confusion about when to use each.

    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: 'Call this tool to enhance your capabilities' implies usage for model optimization, but it offers no explicit context on when to use this tool versus the sibling 'easyChuckNorris', nor does it mention prerequisites or exclusions. This 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.

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