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MatthewDailey

MCP Starter Server

by MatthewDailey
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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

    With only one tool, there is no possibility of ambiguity or overlap between tools, making it perfectly clear which tool to use for any task within this server's scope.

    Naming Consistency5/5

    The single tool name 'hello_tool' follows a consistent verb_noun pattern, and with no other tools to compare, there is no inconsistency in naming conventions.

    Tool Count2/5

    One tool is generally too few for a server's purpose unless it is extremely trivial, which 'hello_tool' suggests. This feels thin and under-scoped for a typical MCP server, indicating a mismatch in tool count appropriateness.

    Completeness1/5

    With only a 'hello_tool', the server lacks any meaningful coverage of a domain. It is severely incomplete, as there are no operations for CRUD, lifecycle management, or any substantive workflows, making it inadequate for agent-driven tasks.

  • Average 1.7/5 across 1 of 1 tools scored.

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

    • No community issues in the last 6 months
    • 0 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 The Unlicense.

  • 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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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

  • Behavior1/5

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

    No annotations are provided, so the description carries full burden for behavioral disclosure. 'Hello tool' reveals nothing about whether this performs read/write operations, requires authentication, has side effects, rate limits, or what the response format might be. It's completely inadequate for behavioral understanding.

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

    Conciseness2/5

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

    While technically concise with just two words, this represents under-specification rather than effective brevity. The description fails to provide any meaningful information that would help an AI agent understand or use the tool properly, making it inefficient despite its short length.

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

    Completeness1/5

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

    For a tool with no annotations and no output schema, the description 'Hello tool' is completely inadequate. It provides no information about what the tool does, how it behaves, what it returns, or any operational context. The description fails to compensate for the lack of structured metadata.

    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 single parameter 'name' clearly documented as 'The name of the person to greet'. The description adds no additional parameter information beyond what's in the schema, but with complete schema coverage, the baseline score of 3 is appropriate.

    Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

    Purpose2/5

    Does the description clearly state what the tool does and how it differs from similar tools?

    The description 'Hello tool' is a tautology that essentially restates the tool name without specifying what it actually does. While the name 'hello_tool' suggests a greeting function, the description fails to articulate a clear verb+resource combination or any specific purpose beyond the obvious implication from the name.

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

    Usage Guidelines1/5

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

    The description provides no guidance on when to use this tool, what context it's appropriate for, or any prerequisites. With no sibling tools mentioned, there's no need to differentiate from alternatives, but the description still offers zero usage instructions or contextual hints.

    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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