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mitchallen

mcp-hello-server

by mitchallen
OverviewSchemaRelated ServersScoreDiscussions
Python
Hybrid

Server Quality Checklist

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

  • Latest release: v0.4.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: one handles personalized greetings, the other provides server health info. No overlap or ambiguity.

    Naming Consistency4/5

    Both tool names use lowercase with underscores and are brief. However, 'greet' is an imperative verb while 'server_info' is a noun phrase, creating a minor inconsistency in naming style.

    Tool Count3/5

    Two tools is minimal but acceptable for a simple 'hello' server that only needs greeting and status. The scope is narrow, so the count fits, though it feels thin for a server with potential expansions.

    Completeness4/5

    The greeting tool covers the main domain (customizable greetings in multiple languages) and the server info tool covers health. No obvious gaps for the stated purpose, though additional tools like listing languages could be nice.

  • Average 3.8/5 across 2 of 2 tools scored. Lowest: 3/5.

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

    • No community issues in the last 6 months
    • 55 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

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

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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?

    With no annotations, the description carries full burden for behavioral transparency. It only states 'Health/status of the server' without disclosing whether it is read-only, safe, or any side effects, leaving significant ambiguity.

    Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

    Conciseness3/5

    Is the description appropriately sized, front-loaded, and free of redundancy?

    The description is extremely concise (four words) and front-loaded, but it lacks structure or any elaboration beyond the core idea. While not overly verbose, it could be more informative without losing conciseness.

    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?

    Given the presence of an output schema, the description need not detail return values. However, it does not clarify what constitutes 'health/status' or whether the tool is acceptable for frequent checks, leaving some gaps.

    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?

    There are zero parameters, and schema description coverage is 100%. The description adds no parameter semantics because none are needed; baseline for 0 params is 4.

    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 indicates the tool provides server health/status information, distinguishing it from the sibling 'greet' tool. However, it is phrased as a noun phrase rather than an action, which could be more explicit.

    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 usage guidance is provided. There is no mention of when to use this tool vs the sibling 'greet' or any prerequisites, making it suboptimal for an agent deciding between tools.

    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, the description discloses behavior: default language, case-insensitivity, supported languages, and return format. Missing error handling for unsupported languages, but sufficient for 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?

    Concise one-paragraph description with efficient structure: purpose, parameter details, and return format. 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?

    Covers purpose, parameters, and return format well. Lacks edge case handling (e.g., unsupported language) but overall complete for typical use, especially given the presence of an output schema.

    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?

    Schema has no parameter descriptions (0% coverage), but the description adds extensive detail: language accepts names/ISO codes (case-insensitive), lists supported languages, and explains name personalization. Fully compensates for schema gaps.

    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 returns a friendly greeting, supporting language and personalization. It distinguishes itself from sibling tool 'server_info'.

    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?

    Provides clear guidance on using language and name parameters, default language, and supported languages. Lacks explicit when-not-to-use scenarios but context 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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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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