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kurokeita

quotable-api-mcp

by kurokeita
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.0.1

  • Disambiguation5/5

    The two tools have completely distinct purposes: one checks server health and the other fetches a quote. There is no overlap or ambiguity between them.

    Naming Consistency4/5

    Both tool names follow a consistent kebab-case pattern with two words. However, they use adjectives/nouns rather than a verb-noun convention, so the style is internally consistent but less typical.

    Tool Count3/5

    With only two tools, the server feels thin for a quotes API, but it could be intentionally minimal. The count is borderline and within the acceptable range for a single-purpose service.

    Completeness2/5

    The server only offers a random quote and a health check. Missing obvious operations like searching quotes, fetching by ID, or listing authors leaves significant gaps for a full-featured quotes API.

  • Average 2.4/5 across 2 of 2 tools scored. Lowest: 1.6/5.

    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

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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

  • Behavior1/5

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

    No annotations are provided, so the description must fully disclose side effects, safety, and return behavior. The placeholder text reveals nothing about whether this is read-only, what it returns, or what side effects it may have.

    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?

    The description is short but this is under-specification, not concise efficiency. A single placeholder sentence with no substantive content does not earn credit for conciseness because it fails to communicate essential information.

    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, no output schema, and an empty parameter schema, the description is the only source of context. 'HealthCheck tool description' provides none, leaving the agent with no understanding of purpose, behavior, or expected output.

    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 input schema has zero properties, so there are no parameter semantics for the description to clarify. Per the scoring rule, a zero-parameter tool receives a baseline of 4 because no parameter ambiguity exists.

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

    Purpose1/5

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

    The description 'HealthCheck tool description' is a placeholder that simply repeats the tool name without stating an action, resource, or outcome. It does not use a specific verb like 'perform' or 'check' and therefore gives the agent no concrete sense of what the tool actually does.

    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?

    There is no guidance about when to use this tool or how it differs from the sibling tool 'random-quote'. The description contains no context, prerequisites, or exclusion criteria, so the agent cannot decide when this tool is appropriate.

    Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

  • Behavior2/5

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

    There are no annotations, so the description must carry the full burden of behavioral disclosure. It only states 'Get a random quote from the API' without revealing side effects, randomness behavior, error handling, or how the parameters affect the result. This lacks transparency.

    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, front-loaded sentence with no wasted words, making it efficient for its length. However, it is terse and omits useful context about parameters, though this is more a completeness issue than a conciseness one.

    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?

    With no output schema and no annotations, the description needs to compensate by explaining how to invoke the tool correctly, including parameter usage and return value. It only covers the basic action and leaves the agent guessing about the response format and how filters are applied, making it incomplete for a tool with three optional parameters.

    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?

    The input schema has 100% coverage with descriptions for each parameter, so the baseline is 3. The description itself adds no additional meaning about the parameters, nor does it explain how tags, query, and author influence the random selection, leaving the agent to rely solely on the schema's minimal descriptions.

    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 identifies the tool's function with a specific verb ('Get') and resource ('a random quote'), and it is distinct from the sibling health-check tool. However, it fails to mention the tool's filtering capabilities (tags, query, author), which are part of its purpose.

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

    Usage Guidelines3/5

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

    The description implies the tool should be used when a random quote is needed, but it gives no explicit guidance on when to use filtering parameters or how this tool compares to alternatives. Since the only sibling is health-check, the distinction is obvious, but the usage context is not fully elaborated.

    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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