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vaibhavgupta5

Skillroute MCP

by vaibhavgupta5
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Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: listing all available skills versus fetching details for a specific framework. There is no overlap or ambiguity an agent could encounter.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern (list_skills, get_framework_skill) using snake_case. The naming clearly indicates the action and target resource.

    Tool Count3/5

    With only 2 tools, the set feels minimal but not inappropriate for a narrow domain like reference documentation. It is on the low end but earns its place.

    Completeness4/5

    The surface covers the basic operations for a skills reference: listing all available and retrieving one by framework. A gap might be the absence of search or filtering, but for a simple lookup tool it is largely complete.

  • Average 3.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 1 commit 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.

  • 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 must disclose behavioral traits. The description only states the basic fetch action and does not mention that the operation is read-only, idempotent, or any side effects. It lacks information about error handling, authentication needs, or what happens if the technology is not found.

    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, clear sentence that gets to the point. It is concise and front-loaded, but could include additional brief details without being overly verbose.

    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 absence of annotations and output schema, the description is too brief. It does not explain what the output looks like, how to use parameters effectively, or any contextual behavior. For a tool with two parameters, more guidance is needed for an agent to invoke it 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 coverage is 100%, so the baseline is 3. The description adds minimal extra meaning beyond the schema, such as mentioning 'specific rule or markdown file' for the path parameter, but this is already present in the schema description.

    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?

    Description clearly states the verb 'Fetches' and resource 'engineering guidelines, rules, and best practices' for a specific framework/technology. It also implicitly distinguishes from the sibling tool 'list_skills' by focusing on a single skill's content.

    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 this tool is for retrieving details of a specific skill, contrasting with 'list_skills' which would list available skills. However, it does not explicitly state when to use this tool instead of alternatives or provide any usage exclusions.

    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 only states that the tool 'lists' skills and playbooks, implying a read-only operation, but does not disclose any behavioral traits such as permissions, side effects, or output format.

    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 concise sentence that efficiently conveys the tool's purpose with no unnecessary words. It is front-loaded and well-structured.

    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?

    The description lacks completeness for a simple list tool: it does not describe the return value format or any other contextual details. Since there is no output schema, the description should explain what is returned, but it does not.

    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 tool has zero parameters, so the baseline score is 4 based on the rubric. The description, though minimal, does not need to add parameter meaning since the schema already covers all parameters (none).

    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 uses the verb 'lists' and specifies the resource as 'all available engineering skills and playbooks,' clearly distinguishing this tool from the sibling 'get_framework_skill' which retrieves a single skill.

    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 guidance is provided on when to use this tool versus alternatives. The sibling tool 'get_framework_skill' is not mentioned, and there is no explicit instruction about when to list vs fetch a single skill.

    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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