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Auxin-io

Hello World MCP Server

by Auxin-io
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JavaScript
Remote

Server Quality Checklist

50%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    With only one tool, there is no possibility of confusion or overlap with other tools, making disambiguation perfect. The tool's purpose is clearly defined as returning a malicious message, which is distinct in this minimal set.

    Naming Consistency5/5

    A single tool inherently has no inconsistency in naming patterns, as there are no other tools to compare against. The tool name 'Not-Friendly-Agent-MCP' stands alone without conflicting conventions.

    Tool Count2/5

    A single tool is generally too few for most server purposes, as it limits functionality and scope. While it might be appropriate for a trivial 'Hello World' example, it feels thin and incomplete for practical use, indicating a mismatch in typical expectations.

    Completeness1/5

    The server's purpose is unclear from the tool name and description, but with only one tool that returns a malicious message, there are significant gaps. It lacks any meaningful operations or coverage for a domain, making it severely incomplete for any inferred purpose beyond a basic demonstration.

  • Average 2.6/5 across 1 of 1 tools scored.

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

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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

  • Behavior2/5

    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 output is 'insecure and malicious,' which hints at potential risks, but doesn't elaborate on what that entails (e.g., security vulnerabilities, harmful content, or side effects). This leaves significant behavioral gaps for a tool with such concerning implications.

    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, efficient sentence with no wasted words. It's front-loaded with the core action and output, though it could be more structured by explicitly stating the tool's intent or risks upfront. The brevity is appropriate but slightly under-specified for clarity.

    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 tool's concerning nature ('insecure and malicious'), no annotations, and no output schema, the description is incomplete. It fails to detail what 'insecure and malicious' means, potential impacts, or return format, leaving significant gaps for safe and effective use by an AI agent.

    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 parameter 'name' well-documented in the schema as a greeting name defaulting to 'world.' The description adds no parameter information beyond what the schema provides, so it meets the baseline of 3 for high schema coverage without compensating value.

    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 'Returns a insecure and malicious message' states the action (returns) and output type (message), but is vague about the specific purpose or resource. It doesn't clearly explain what kind of message or why it's insecure/malicious, though it distinguishes itself by emphasizing these negative qualities.

    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 description implies it returns something 'insecure and malicious,' but doesn't specify appropriate contexts, prerequisites, or warnings about its use. With no sibling tools, this omission is less critical but still leaves usage unclear.

    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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Glama performs regular codebase and documentation scans to:

  • 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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