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Datajang

mcp-narajangteo

recommend_bids_for_dept

Find government procurement bids tailored to your department. Search by keyword and team profile to receive prioritized recommendations with strategic analysis and budget insights.

Instructions

    Search government procurement notices with department context for personalized recommendations.
    Returns up to 60 results (30 regular bids + 30 pre-specs) with analysis instructions.
    LLM can flexibly present Top N items or all relevant items based on user's request.
    Prioritizes items with non-zero budgets.

    Args:
        keyword: Search keyword (e.g., 'AI', 'Cloud', '플랫폼')
        department_profile: Description of your team/department.
                           Examples: 'UI/UX 디자인팀', 'Database Migration Unit',
                                    'AI/ML 개발팀', '클라우드 인프라팀'
        days: Search window in days from today (default: 7).
              Increase for older bids, e.g. 30, 60, 90.

    Returns:
        Formatted recommendations with strategic analysis
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordYes
department_profileYes
daysNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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.

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