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gabrielestes

vader-mcp

by gabrielestes

Server Quality Checklist

58%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    Each tool has a clear, non-overlapping role: one detects whether Vader mode should activate, one restyles an arbitrary message, and one returns a canned quote. There is little chance an agent would confuse them.

    Naming Consistency3/5

    The names are readable and all snake_case, but they follow different patterns: sense_disturbance is verb_noun, vader_speak is noun_verb, and vader_quote is noun_noun. Two share a vader_ prefix while the third does not, making the convention inconsistent.

    Tool Count5/5

    Three tools is appropriate for a narrow Vader roleplay server. Each tool earns its place and there is no bloat or redundancy.

    Completeness5/5

    The server covers the full intended workflow: detect when Vader's voice should engage, restyle a message into that voice, and provide authentic quotes. No obvious gaps or dead ends exist for the stated domain.

  • Average 3.9/5 across 3 of 3 tools scored.

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

    • No community issues in the last 6 months
    • 3 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
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  • 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

  • Behavior4/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently states that the tool returns a real Vader line and that the topic parameter is optional and constrained to a listed set. It does not mention edge behavior for invalid topics, but that is a minor gap for such a simple read-only 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?

    The description is a single, well-structured sentence that front-loads the core behavior and then provides the parameter constraints. Every phrase earns its place, with no redundant or tangential information.

    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?

    For a tool with one optional parameter and no output schema, the description is largely complete: it states the return type, the input options, and the optional nature of the parameter. Missing sibling differentiation and invalid-input behavior are minor for this low-complexity tool.

    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 schema already documents the topic parameter, and the description adds meaningful value by enumerating the exact allowed values ('father, faith, power, destiny, failure, deal, teacher, circle, impressive, escape, join, breathing') and clarifying that the parameter is optional. This goes beyond the schema's generic example.

    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 verb ('Return') and resource ('a real Darth Vader line'), and the title adds 'canonical' to reinforce scope. It is specific enough to understand what the tool does, though it does not explicitly distinguish itself from the sibling vader_speak.

    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 no guidance on when to use this tool versus the siblings sense_disturbance or vader_speak. There are no exclusions, alternatives, or contextual conditions, so an agent must infer usage from the name alone.

    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?

    With no annotations provided, the description carries the behavioral burden. It discloses meaningful transformation traits: formal register, no contractions, opening/closing framing, and verbatim usage. This gives the agent a clear model of what will happen to the input, which is strong for a simple text-restyling 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?

    The description is two sentences with no filler. The core purpose and style constraints are front-loaded, and the usage instruction ('use the result verbatim') earns its place. Every sentence contributes actionable information.

    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?

    For a low-complexity tool with only two parameters and full schema coverage, the description is largely complete. It explains the transformation style and the expected usage pattern. It lacks explicit sibling differentiation, but nothing critical is missing for an agent to invoke the tool correctly.

    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 well. The description adds some context by calling the input the 'plain version' of what to say, but it does not meaningfully elaborate on the intensity parameter beyond what the schema already provides.

    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 action ('Restyle a message') and the specific resource/voice ('Darth Vader's voice'), with concrete style details. It does not explicitly contrast with the siblings vader_quote or sense_disturbance, but the restyle intent is distinct enough for an agent to understand the tool's role.

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

    Usage Guidelines3/5

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

    The description implies when to use the tool: when the user has a plain message that should be turned into Vader-style speech. It also tells the agent to pass the plain version and use the result verbatim. However, it provides no explicit guidance about when not to use it or how it differs from sibling tools like vader_quote.

    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?

    No annotations are provided, so the description carries the behavioral burden. It discloses that the tool scans for keywords, returns an 'engage' flag, and that a true result should trigger a Darth Vader voice response. For a simple read-only detection tool, this is adequate transparency.

    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?

    Three concise sentences: the action, the trigger, and the conditional response. There is no filler, and the core purpose is front-loaded.

    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?

    For a simple one-parameter detection tool with no output schema, the description covers the trigger condition, the return flag behavior, and the expected follow-up action. It could mention what happens when 'engage' is false, but that is reasonably implied.

    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 coverage is 100%, and the schema already describes the 'text' parameter as 'The text to scan — usually the user's message.' The tool description adds little beyond that, so the schema carries the parameter meaning; 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 opens with a specific verb and resource: 'Scan text for Star Wars keywords.' This clearly distinguishes it from sibling tools like vader_speak and vader_quote, which are about speaking or quoting rather than detecting Star Wars content.

    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?

    It explicitly says when to call the tool: 'Call this on the user's message whenever Star Wars might have come up.' It does not spell out when not to use it or name alternatives, but the trigger condition is specific enough for an agent to apply correctly.

    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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