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ANeuronI

skills.sh MCP Server

by ANeuronI

Server Quality Checklist

75%
Profile completionA complete profile improves this server's visibility in search results.
  • Latest release: v1.0.4

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: searching, getting details, retrieving install commands, and viewing popular skills. No overlap or ambiguity.

    Naming Consistency5/5

    All tool names follow a consistent verb_noun pattern in snake_case (e.g., get_install_command, search_skills), making them predictable and easy to understand.

    Tool Count5/5

    With 4 tools covering search, details, install commands, and popular skills, the set is well-scoped for a skill discovery and installation server—neither too sparse nor excessive.

    Completeness5/5

    The tools provide a complete workflow: search for skills, examine details, retrieve install commands, and browse popular skills. No obvious gaps for the stated purpose.

  • Average 3.6/5 across 4 of 4 tools scored. Lowest: 2.9/5.

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

    • No community issues in the last 6 months
    • 4 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI status not available
  • This repository is licensed under ISC License.

  • This repository includes a README.md file.

  • Tools from this server were used 2 times in the last 30 days.

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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 present, so the description carries full burden. It does not disclose whether the tool is read-only, idempotent, or makes external API calls, limiting 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?

    The description is a single sentence with no extraneous information. It is efficiently front-loaded with the core action.

    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?

    There is no output schema and no explanation of the return value (e.g., format of the command). For a simple tool, more context about what is returned would be 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?

    Schema coverage is 100% with descriptions for both parameters. The description adds no extra meaning beyond the schema, so baseline score of 3 is appropriate.

    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 retrieves an npx install command for a skill. It is specific and meaningful, distinguishing it from sibling tools like search_skills or get_skill_details.

    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 on when to use this tool versus alternatives. There are no prerequisites, context, or when-not-to-use conditions provided.

    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 of behavioral disclosure. It only states 'from the leaderboard,' but fails to disclose whether the output is sorted, paginated, or requires authentication. The parameter default values (limit=20, timeframe='all') hint at behavior but are part of the schema, not the description.

    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 sentence with no wasted words. It is front-loaded with the core action and resource, making it easy to scan. Every word earns its place.

    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?

    The tool has two simple parameters and no output schema. The description mentions 'leaderboard' but does not clarify how 'popular' is determined or what the response format includes (e.g., just skill names or with metrics). This leaves gaps for an agent, though sibling tools may fill context. Adequate but not thorough.

    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%, with each parameter having a clear description (limit: 'Number of results', timeframe: enumeration of values). The description does not add additional meaning beyond the schema (e.g., what 'leaderboard' means for parameters). According to guidelines, baseline is 3 when coverage is high, and no extra value is provided.

    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 'Get popular skills from the leaderboard' clearly identifies the verb (get) and resource (popular skills from leaderboard), distinguishing it from sibling tools like search_skills (which searches) and get_skill_details (which returns details). However, it does not specify the source or definition of 'popular' (e.g., trending vs all-time), leaving some ambiguity.

    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 its siblings (get_install_command, search_skills, get_skill_details). The description does not indicate use cases, prerequisites, or exclusions. An agent would need to infer from context, which is inefficient.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It implies a read operation ('search') but does not detail behavior such as pagination, result structure, or any limitations. The workflow guidance adds some context, but core behavioral traits are missing.

    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 a clear lead sentence followed by bullet points. Every sentence serves a purpose, though the 'CRITICAL WORKFLOW' section makes it slightly longer than minimal but 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?

    For a simple tool with two parameters and no output schema, the description covers purpose and usage well but lacks details about the return format or what to expect from results. It is adequate for basic usage but not fully complete.

    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 both parameters (query, limit) are documented in the schema. The description does not add extra meaning beyond the schema's descriptions, meeting the baseline for high coverage.

    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 clearly states it 'search[es] for skills on skills.sh by query term', providing a specific verb and resource. It distinguishes itself from sibling tools like get_skill_details and get_popular_skills by focusing on query-based search.

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

    Usage Guidelines5/5

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

    The description provides explicit workflow steps, including generating multiple keywords, calling the tool multiple times, and using get_skill_details before recommending skills. It clearly contrasts with sibling tools by guiding when to use each.

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

  • Behavior3/5

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

    No annotations are provided, so the description carries the full burden. It mentions the tool provides 'critical data like total installs, weekly installs, and supported platforms,' but does not explicitly state it is read-only or disclose other behavioral traits like rate limits. Adequate but not exhaustive.

    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?

    Three sentences, front-loaded with purpose. Concise with no wasted words, though it could be slightly more compressed without losing clarity.

    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?

    Given no output schema, the description sufficiently explains the kind of data returned (installs, platforms). Combined with high schema coverage, it provides a complete picture for a simple retrieval 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?

    Schema coverage is 100%, so parameters are already described. The description adds value by instructing to 'Extract owner, repo, and skillId from the search results table,' which clarifies source and usage beyond schema definitions.

    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 clearly states 'Get detailed information about a specific skill,' which is a specific verb+resource. It distinguishes from siblings by instructing to use after 'search_skills' and naming sibling tools.

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

    Usage Guidelines5/5

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

    Explicitly states 'Use this tool ALWAYS after search_skills to vet a skill before recommending it,' providing clear context of when to use and a prerequisite.

    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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  • Confirm that there are no obvious security issues.
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