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hyen43

Model Context Protocol Server

by hyen43
OverviewSchemaRelated ServersScoreDiscussions
TypeScript
Hybrid

Server Quality Checklist

42%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation2/5

    The 'weather' and 'weather_api' tools have significant overlap in purpose, as both appear to fetch weather information for a city, making them ambiguous and prone to misselection. The 'example_tool' is distinct but vague, adding to the overall confusion in the tool set.

    Naming Consistency3/5

    The naming is mixed with 'example_tool' and 'weather_api' using snake_case, while 'weather' is a single word, showing some inconsistency. However, the names are still readable and follow a basic pattern, but lack a uniform verb_noun structure or consistent style.

    Tool Count3/5

    With only 3 tools, the count is borderline thin for a general-purpose server like 'Model Context Protocol Server', suggesting it might be under-scoped. While not extreme, it feels insufficient to cover a broad domain effectively, indicating a potential mismatch in scope.

    Completeness2/5

    The tool set is severely incomplete for the server's implied domain, with overlapping weather tools and a vague example tool, leaving obvious gaps in functionality. There is no clear coverage of core operations or a coherent workflow, which will likely cause agent failures due to missing essential capabilities.

  • Average 2.8/5 across 3 of 3 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

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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

  • Behavior2/5

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

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states 'processes messages' without detailing traits like side effects, permissions, rate limits, or output format, leaving significant gaps in understanding the tool's behavior.

    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, efficient sentence with no wasted words, making it appropriately concise. However, it lacks front-loaded critical information, such as specific actions or context, which slightly reduces its effectiveness.

    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?

    Given the lack of annotations and output schema, the description is incomplete. It fails to explain what 'processes' means, the tool's behavior, or return values, making it inadequate for a tool with one parameter and no structured support.

    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% description coverage, with the 'message' parameter documented as 'Message to process'. The description adds no additional meaning beyond this, such as examples or constraints, so it meets the baseline score for high schema coverage without compensating value.

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

    Purpose3/5

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

    The description states 'processes messages', which provides a vague purpose without specifying what processing entails (e.g., filtering, analyzing, transforming). It does not differentiate from sibling tools like 'weather' or 'weather_api', leaving ambiguity about its role in the toolset.

    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 is provided on when to use this tool versus alternatives, such as the sibling tools 'weather' and 'weather_api'. The description lacks context, prerequisites, or exclusions, offering no help in tool selection.

    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?

    No annotations are provided, so the description carries full burden for behavioral disclosure. The description only states what the tool does ('가져오기' - get/fetch), implying a read operation, but doesn't disclose any behavioral traits like rate limits, authentication needs, error conditions, or what format the weather information returns. For a tool with no annotations, this is insufficient.

    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 extremely concise with a single sentence that directly states the tool's purpose. It's front-loaded with the core functionality and contains no wasted words or redundant information, making it highly efficient.

    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?

    Given the tool's complexity (simple single-parameter query) and lack of annotations and output schema, the description is incomplete. It doesn't explain what weather information is returned (e.g., temperature, conditions), potential errors, or usage constraints. For a tool with no structured output or behavioral annotations, more context is needed.

    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 schema description coverage is 100%, with the single parameter 'city' fully documented in the schema. The description adds no additional meaning beyond the schema, as it doesn't explain parameter usage, constraints, or examples beyond what's already provided. With high 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.

    Purpose4/5

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

    The description clearly states the tool's purpose: '도시의 날씨 정보를 가져오기' (Get weather information for a city). It specifies the verb ('가져오기' - get/fetch) and resource ('날씨 정보' - weather information) with a target scope ('도시' - city). However, it doesn't differentiate from sibling tools like 'weather_api', so it doesn't reach a perfect score.

    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?

    The description provides no guidance on when to use this tool versus alternatives. There are sibling tools like 'weather_api' and 'example_tool', but the description doesn't mention any context, prerequisites, or exclusions for using this specific weather tool over others.

    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?

    No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions using the Open-Meteo API but does not describe traits like rate limits, authentication needs, error handling, or response format. This is a significant gap for an API-based tool with no output schema.

    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, efficient sentence in Korean that directly states the tool's function. It is appropriately sized and front-loaded, with no wasted words, though it could be slightly more structured for clarity.

    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?

    Given the complexity of an API tool with no annotations and no output schema, the description is incomplete. It lacks details on behavioral traits, response format, and differentiation from siblings, making it inadequate for an agent to fully understand how to use this tool effectively.

    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 schema description coverage is 100%, with the 'city' parameter fully documented in the schema. The description adds no additional parameter details beyond implying the city is used to fetch weather data, which is already clear from the schema. This meets the baseline for high schema coverage.

    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's purpose: '가져오기' (fetch/retrieve) weather information for a city using the Open-Meteo API. It specifies the resource (weather information) and the target (city), but does not distinguish it from the sibling 'weather' tool, which might have overlapping functionality.

    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?

    The description provides no guidance on when to use this tool versus alternatives like the sibling 'weather' tool. It lacks explicit context, exclusions, or prerequisites, leaving the agent to infer usage based on the tool name and description alone.

    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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