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Datajang

mcp-narajangteo

by Datajang
OverviewSchemaRelated ServersScoreDiscussions
Python

Server Quality Checklist

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

  • Latest release: v1.3.2

  • Disambiguation5/5

    Each tool has a clearly distinct purpose with no overlap. analyze_bid_detail focuses on document extraction and analysis, get_bids_by_keyword performs general keyword searches, and recommend_bids_for_dept provides personalized recommendations based on department context. The boundaries are well-defined and unambiguous.

    Naming Consistency5/5

    All three tools follow a consistent verb_noun pattern with clear, descriptive names: analyze_bid_detail, get_bids_by_keyword, and recommend_bids_for_dept. The naming convention is uniform throughout the set, making it easy to understand each tool's function at a glance.

    Tool Count4/5

    Three tools is appropriate for this government procurement domain, covering search, analysis, and recommendation workflows. While slightly minimal, each tool serves a distinct and valuable purpose, and the count aligns well with the server's focused scope on Korean procurement notices.

    Completeness4/5

    The toolset covers core workflows effectively: searching bids, analyzing bid details, and providing personalized recommendations. Minor gaps exist, such as the lack of tools for bid submission or tracking, but the available tools enable agents to perform essential procurement analysis tasks without significant dead ends.

  • Average 4.2/5 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

  • This repository is licensed under MIT License.

  • This repository includes a README.md file.

  • No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.

    Tip: use the "Try in Browser" feature on the server page to seed initial usage.

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    If the server belongs to an organization, first add glama.json to the root of your repository:

    {
  "$schema": "https://glama.ai/mcp/schemas/server.json",
  "maintainers": [
    "your-github-username"
  ]
}

    Then . Browse examples.

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How to sync the server with GitHub?

Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.

To manually sync the server, click the "Sync Server" button in the MCP server admin interface.

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?

    Discloses result limits (60 items max), budget prioritization logic, and flexible presentation options, compensating for missing annotations.

    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?

    Well-structured with Args/Returns sections, though the sentence about LLM flexibility ('LLM can flexibly present...') is slightly redundant.

    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?

    Comprehensive for the domain, including department profile examples and output format description, though it could briefly clarify what 'pre-specs' means.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    With 0% schema description coverage, the Args section provides crucial examples for all parameters (e.g., 'AI/ML 개발팀', 'Database Migration Unit') and usage guidance for the days parameter.

    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?

    Clearly states the tool searches procurement notices with department-specific personalization, distinguishing it from generic keyword search (get_bids_by_keyword) and single-bid analysis (analyze_bid_detail).

    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?

    Provides behavioral guidance (prioritizes non-zero budgets, returns 30+30 results) but lacks explicit guidance on when to use this versus sibling tools.

    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?

    Discloses specific file format support (HWP, HWPX, etc.) and ZIP extraction priority logic (제안요청서 > 과업지시서 > .hwp > .pdf) not inferable from schema; explains conditional analysis prompts triggered by department_profile.

    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?

    Well-structured with Args/Returns sections; front-loaded with core purpose; file priority logic and parameter mappings are high-value details that earn their place; could be slightly more compact but appropriately detailed.

    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?

    Sufficient for medium complexity: covers file handling behaviors, optional analysis triggers, and references output schema existence; minor gap regarding error handling for unsupported formats or large files.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Excellent compensation for 0% schema description coverage: maps file_url and filename to specific API response fields (ntceSpecDocUrl1, ntceSpecFileNm1) and clarifies department_profile's effect on output (generates analysis prompts).

    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?

    Clearly states the tool downloads/extracts text from bid attachments (RFPs) for strategic analysis, distinguishing it from sibling search/recommendation tools by focusing on document analysis rather than bid discovery.

    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?

    Implies workflow context by referencing 'ntceSpecDocUrl1 from search results' in parameter descriptions, but lacks explicit when-to-use guidance contrasting it with get_bids_by_keyword or recommend_bids_for_dept.

    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?

    Since no annotations provided, description carries full burden; it successfully discloses return type (formatted string), scope limitation (service-type/용역 projects only), and dual result types (bid notices + preliminary specifications).

    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?

    Uses efficient docstring structure (Args/Returns), front-loaded with clear purpose statement, every sentence adds value including Korean translations and specific project examples, appropriate length for complexity.

    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 simple 2-parameter schema with output schema available, description adequately covers tool scope, parameter semantics, and return format; mentions important service-type constraint but could note behavior when no bids found.

    Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

    Parameters5/5

    Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

    Schema has 0% description coverage, so description provides essential compensation: explains keyword maps to bid title (공고명) with 4 examples, and days is search window from today with default value context.

    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?

    Clear specific verb (Search) + resource (Korean government procurement notices/나라장터), and explicitly distinguishes from siblings by stating it returns BOTH regular bid notices AND preliminary specifications for service-type projects only.

    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?

    Provides concrete examples for keyword parameter ('인공지능', 'AI', etc.) and explicit guidance on days parameter usage (increase for older bids: 30, 60, 90), though lacks explicit comparison to sibling tools analyze_bid_detail and recommend_bids_for_dept.

    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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