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opendata-kr

io.github.opendata-kr/narajangteo-corpinfo-mcp

Official
by opendata-kr
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TypeScript
Remote

Server Quality Checklist

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

  • Latest release: v0.2.0

  • Disambiguation5/5

    Each tool has a clearly distinct purpose: check_company_qualification verifies specific qualification criteria, while get_company_profile provides a full company overview. The descriptions explicitly cross-reference each other, eliminating ambiguity.

    Naming Consistency5/5

    Both tool names follow the verb_noun pattern (check_company_qualification, get_company_profile), maintaining consistency in naming convention.

    Tool Count3/5

    With only two tools, the server is minimal but reasonably scoped for its narrow domain of company information lookup. However, it feels slightly thin for broader use cases.

    Completeness2/5

    The server lacks any mechanism to look up companies by name or other identifiers; it requires a business registration number with no resolver. This is a significant gap that limits agent workflows.

  • Average 4.8/5 across 2 of 2 tools scored.

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

    • No community issues in the last 6 months
    • 13 commits in the last 12 weeks
    • Last stable release on
    • No critical vulnerability alerts
    • No high-severity vulnerability alerts
    • No code scanning findings
    • CI is passing

  • 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

  • Behavior5/5

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

    Annotations indicate readOnlyHint=true (read-only) and openWorldHint=true. Description adds that lists may be truncated due to query limits and that each call consumes at least 4 API requests per facet, providing critical behavioral info beyond 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?

    Three efficient sentences: first states purpose, second gives usage guidance, third provides behavioral constraints. Every sentence earns its place with no redundancy.

    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?

    For a tool that assembles multiple facets with no output schema, the description covers purpose, usage, parameters, limitations, and cost. It is complete given the complexity and annotations.

    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 has 100% parameter coverage with pattern and required. Description adds that business number is mandatory and no resolver exists, but this only marginally improves understanding beyond the 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?

    Description clearly states it assembles and returns basic info, business types, supplied goods, and sanctions for a procurement company using a business registration number. It distinguishes from sibling tool check_company_qualification by scope.

    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?

    Explicitly advises when to use (for an overall company overview) and when not (use check_company_qualification for checking specific qualifications). Notes that business number input is mandatory and there is no company-name resolver.

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

  • Behavior5/5

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

    Annotations declare readOnlyHint and openWorldHint. Description adds important behaviors: no reverse lookup, list truncation may cause false negatives, and each call consumes at least 3 API requests per facet. No contradiction 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?

    Description is concise: 3 sentences with main action first, then usage guidance and limitations. No unnecessary words.

    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?

    Given no output schema, description explains tool behavior well (qualification check, limitations, cost). Could be more explicit about return format, but sufficient for correct invocation.

    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 100% with descriptions. The description adds context: explains the purpose of industry and product code parameters and behavior for empty arrays. Adds value beyond 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 checks qualification of a company for bidding, using business registration number, industry codes, and product codes. It explicitly distinguishes from sibling get_company_profile for overall profile.

    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?

    Provides explicit when to use (specific bidding eligibility), when not (use get_company_profile for overall profile), and mentions limitations (no reverse lookup, list truncation, API consumption).

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

GitHub Badge

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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