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neelavalareddy

auto-skill-connector

by neelavalareddy
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Python
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Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one searches and returns skill content, the other installs a skill from a URL. No overlap in functionality.

    Naming Consistency5/5

    Both tools follow a consistent verb_noun pattern: 'install_skill' and 'recommend_skill', making them predictable and easy to distinguish.

    Tool Count3/5

    With only two tools, the server feels minimal for a skill management system. While it covers the core workflow of finding and installing skills, it lacks tools for listing, updating, or removing skills, which limits its utility.

    Completeness3/5

    The server covers the basic find-and-install flow but is missing lifecycle operations like listing installed skills, updating, or removing them. Users may need additional tools to manage their skill set effectively.

  • Average 4.4/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 2 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 MIT License.

  • This repository includes a README.md file.

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

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    {
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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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

  • 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 describes the file write operation and path, but does not disclose potential side effects like overwriting existing skills, error handling, or network requirements.

    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?

    Single sentence packed with essential information, no unnecessary words.

    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 annotations and no schema descriptions, the description covers the core workflow well. It could mention the return value (output schema exists) but the essential usage is clear.

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

    Parameters5/5

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

    With 0% schema description coverage, the description adds critical meaning: 'url' is the source from recommend_skill, and 'name' determines the folder name in ~/.claude/skills/. Both parameters are effectively explained.

    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 the tool downloads a SKILL.md from a URL and installs it to a specific path, distinguishing it from the sibling tool recommend_skill which returns URLs.

    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 explains the tool is for installing skills obtained from recommend_skill, but does not explicitly state when not to use or provide alternatives beyond the sibling reference.

    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 provided, so the description carries the full burden. It discloses the database size (~200k) and composition (scraped skills, MCP servers, plugins), and states it returns full SKILL.md content. It implies a read-only operation, though it could be more explicit about no side effects.

    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 concise with two paragraphs: one explaining what the tool does and why, and one providing usage advice. Every sentence adds value, and the structure is front-loaded.

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

    Completeness5/5

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

    Despite no output schema and a single required parameter, the description covers the tool's purpose, input format, and usage context thoroughly. It includes database size and composition, making it complete for a search 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 single parameter 'task' has 0% schema description coverage. The description compensates by specifying 'a short, keyword-rich description of the task,' adding meaningful guidance beyond the schema's 'Task' label.

    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 the tool's purpose: it searches a database of ~200k skills and returns the full SKILL.md content. It uses specific verbs and resources, and distinguishes from the sibling tool 'install_skill' by focusing on recommendation before installation.

    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?

    The description explicitly advises calling the tool when the user's request might be covered by an existing skill, before building from scratch. It also recommends passing a keyword-rich description. While it doesn't list explicit when-not cases, the guidance is clear.

    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 the MCP server is working as expected.
  • Confirm that there are no obvious security issues.
  • Evaluate tool definition quality.

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