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Lulu-The-Narwhal

fx-converter-mcp

Server Quality Checklist

67%
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  • Latest release: v0.1.0

  • Disambiguation5/5

    The two tools have clearly distinct purposes: one converts an amount and returns the converted value, while the other simply returns the exchange rate without converting. There is no ambiguity between them.

    Naming Consistency5/5

    Both tools follow a verb_noun pattern: 'convert_currency' and 'get_exchange_rate'. The naming is consistent and predictable.

    Tool Count5/5

    Two tools is an appropriate scope for a focused currency conversion server. Each tool serves a distinct, essential function, and the count is neither too sparse nor excessive.

    Completeness4/5

    The tool set covers the core needs of converting currencies and fetching rates. A minor gap is the lack of a list of supported currency codes, but most users can work around this with ISO codes.

  • Average 4.6/5 across 2 of 2 tools scored.

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

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

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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

  • Behavior4/5

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

    Annotations already declare readOnlyHint=true, so the description doesn't need to restate safety. It adds valuable context: uses ECB reference rates, returns converted amount, rate used, and rate date. No contradictions.

    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?

    Three sentences, front-loaded with purpose, then usage, then parameter/return details. No redundant words; every sentence adds value.

    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 simple 3-parameter tool with an output schema and read-only annotation, the description covers inputs, outputs, and data source. It doesn't mention error handling or unsupported currencies, but these are less critical given the tool's simplicity.

    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?

    Schema coverage is 0%, so the description carries the burden. It explains from_currency/to_currency are 3-letter ISO codes with examples. Amount is self-explanatory from the context. This goes beyond the schema, though it could detail amount precision or edge cases.

    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 the tool converts an amount between currencies using ECB reference rates, with specific verb+resource. It distinguishes from the sibling get_exchange_rate by focusing on conversation, not just rate retrieval. Examples solidify the purpose.

    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?

    Explicitly says to use for 'how much is X in Y' questions and gives example queries. It doesn't mention when not to use it or explicitly contrast with get_exchange_rate, but the usage context is clear and sufficient.

    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?

    Annotations already mark readOnlyHint=true, and the description adds that the tool returns both the rate and the date, and that parameters are ISO codes. No contradictions with annotations.

    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 three concise sentences: purpose, usage example, and parameter/return details. No extraneous words.

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

    Completeness5/5

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

    With only 2 simple parameters and an output schema, the description covers purpose, usage, parameters, and return value, making it fully adequate.

    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 schema has no property descriptions (0% coverage), but the description clarifies that both parameters are 3-letter ISO codes, supplementing the bare schema.

    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 the tool looks up today's exchange rate between two currencies and explicitly notes it does not convert amounts, distinguishing it from the sibling convert_currency tool.

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

    Usage Guidelines5/5

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

    It provides a concrete example of when to use it ('what's the exchange rate between USD and EUR'), and the phrase 'without converting a specific amount' indicates when not to use it.

    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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