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pinnaclesoft-ko

Seoul Public Data MCP Server

by pinnaclesoft-ko
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TypeScript
Remote

Server Quality Checklist

67%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools have completely distinct purposes with no overlap. CulturalEventInfo retrieves cultural event information from Seoul's cultural portal, while KoreaSeoulSubwayStatus provides subway station passenger count data. An agent can easily differentiate between these two unrelated domains.

    Naming Consistency3/5

    The naming is mixed with no consistent pattern. CulturalEventInfo uses a descriptive noun phrase format, while KoreaSeoulSubwayStatus follows a different descriptive naming style. Both are readable but lack a consistent verb_noun or other predictable convention across the set.

    Tool Count2/5

    With only 2 tools, this server feels severely under-scoped for what appears to be a general 'Seoul Public Data' server. The tools cover two specific data endpoints, but the server name suggests broader public data access that would typically require more comprehensive coverage of Seoul's public data APIs.

    Completeness2/5

    Given the server name 'Seoul Public Data MCP Server,' there are significant gaps in coverage. Seoul likely offers numerous public data APIs beyond just cultural events and subway passenger counts. The surface is severely incomplete for what the server name promises, with no coverage of transportation, weather, demographics, or other typical public data domains.

  • Average 3/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 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 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

  • 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. It describes the return data format (JSON) and structure, which is helpful, but lacks critical behavioral details: it doesn't mention rate limits, authentication requirements, error handling, or whether this is a read-only operation (implied by '조회' but not explicit). The description adds some context but misses key operational traits.

    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 well-structured and appropriately sized. It starts with the tool's purpose, details the data source and fields, and explains the return format with a clear breakdown. However, the detailed field listing could be condensed or moved to an output schema for better conciseness, though it's informative.

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

    Completeness3/5

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

    Given the tool's complexity (2 parameters, no annotations, no output schema), the description is moderately complete. It covers the purpose, data fields, and return structure, which helps compensate for the lack of output schema. However, it lacks behavioral context (e.g., rate limits, errors) and usage guidelines, leaving gaps for an agent to operate 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 input schema has 100% description coverage, clearly documenting both parameters (startIndex and endIndex) with their purposes, types, and constraints. The description adds no parameter-specific information beyond what's in the schema, so it meets the baseline of 3 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.

    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: '서울시 문화행사 정보를 조회할 수 있는 도구입니다' (a tool to query cultural event information in Seoul). It specifies the data source (Seoul Culture Portal) and the type of information provided (performances, events with location, date, etc.). However, it doesn't explicitly differentiate from the sibling tool 'KoreaSeoulSubwayStatus', which handles subway status rather than cultural events.

    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. It mentions the data source but doesn't specify use cases, prerequisites, or exclusions. There's no comparison with the sibling tool or other potential tools for cultural event queries.

    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?

    With no annotations provided, the description carries the full burden. It discloses the return format (JSON) and data structure details, which is valuable. However, it doesn't mention important behavioral aspects like whether this is a read-only operation (implied but not stated), rate limits, authentication requirements, or error handling beyond the RESULT.CODE/MESSAGE fields.

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

    Conciseness3/5

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

    The description is appropriately sized but not optimally structured. It front-loads the purpose, but then mixes parameter format instructions with output structure details. The output structure section is quite detailed (listing 7 specific fields) which might be better suited for an output schema. Some sentences could be more efficiently combined.

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

    Completeness3/5

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

    Given 5 parameters, no annotations, and no output schema, the description provides reasonable coverage. It explains the purpose, parameter formats, and output structure. However, for a data retrieval tool with pagination parameters (startIndex/endIndex), it doesn't explain pagination behavior or how to interpret the list_total_count field in relation to the pagination 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?

    Schema description coverage is 100%, so the schema already documents all 5 parameters thoroughly. The description adds some value by reinforcing format requirements (YYYYMMDD for date, Korean for station names, specific formats for line numbers) and providing examples, but doesn't add significant semantic meaning beyond what's in the schema descriptions. Baseline 3 is appropriate when schema does most of the work.

    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: '서울시 지하철호선별 역별 승하차 인원 정보를 조회할 수 있는 도구입니다' (retrieves subway passenger boarding/alighting information by line and station in Seoul). It specifies the resource (subway passenger data) and verb (조회/retrieve), but doesn't explicitly differentiate from the sibling tool 'CulturalEventInfo' beyond being in a different domain.

    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. It doesn't mention the sibling tool 'CulturalEventInfo' or any other tools that might exist for similar data queries. Usage context is implied (when you need subway passenger data) but not explicitly stated.

    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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  • Evaluate tool definition quality.

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