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JGPAS

mcp-first-server

Server Quality Checklist

75%
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  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools serve completely different purposes: one returns motivational quotes and the other returns cryptocurrency prices. There is no overlap or ambiguity in what each tool does.

    Naming Consistency5/5

    Both tools follow the same get_<object> naming pattern, making the naming predictable and consistent. An agent can easily infer the action and target from each name.

    Tool Count3/5

    With only two tools, the server feels thin and borderline for a general-purpose toolset. Each tool is independently useful, but the small count limits the server's overall utility.

    Completeness3/5

    The tools are isolated, self-contained lookups with no obvious surrounding lifecycle, so completeness is hard to assess. There are no clear dead ends, but the surface is minimal and lacks a cohesive domain.

  • Average 3.7/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 6 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.

  • Add a glama.json file to provide metadata about your server.

  • This server has been verified by its author.

  • Add related servers to improve discoverability.

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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

  • Behavior3/5

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

    With no annotations, the description carries the full burden. It clearly states the tool returns the current USD price and that input is a coin id, but does not disclose output format, freshness, error behavior, or limitations such as USD-only pricing. This is adequate for a simple read-only lookup but leaves gaps.

    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?

    A single, front-loaded sentence with no filler. Every word contributes meaning, and the examples reinforce the parameter format.

    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?

    For a one-parameter, no-output-schema lookup tool, the description provides enough to call it correctly: what to pass, what to expect in principle, and an example. It could mention the exact return shape, but that is a minor gap for such a simple tool.

    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 the schema fully documents the coin parameter. The description adds concrete examples of valid ids, which is helpful but redundant with the schema; baseline 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?

    States a specific verb (get), resource (cryptocurrency), and scope (current USD price by id), with concrete examples. It is clear on its own but does not differentiate itself from the sibling tool get_quote.

    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 get_quote, and no exclusions or alternative conditions are given. The usage context is only implied by the tool purpose.

    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 burden of behavioral disclosure. It does disclose that the quote is random and motivational, which is useful, but it does not mention failure modes, source behavior, or response shape. For a simple no-parameter tool this is acceptable but not detailed.

    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. The primary behavior is stated first, followed by a brief usage cue, making it easy to parse.

    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?

    For a tool with no parameters and no output schema, the description provides enough context for an agent to invoke it correctly. The lack of any mention of the return format is a minor gap, but the purpose is sufficiently clear.

    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 tool has zero parameters, so parameter semantics are trivially satisfied. The description also confirms the tool requires no input, which matches the empty schema.

    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 returns a random motivational quote, specifying both the verb and the resource. It does not explicitly differentiate it from sibling get_crypto_price, but the subject matter is distinct enough that an agent can infer the difference.

    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 gives a direct use case: when the user wants inspiration or a quote. It is clear about when to invoke the tool, though it does not explicitly mention when not to use it or compare it to get_crypto_price.

    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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