mcp-dart

keyword_search

Perform Korean keyword search on parsed DART reports to find matching snippets with section codes and titles.

Instructions

Korean full-text keyword search across a parsed DART report.

Uses substring matching (no \b word boundaries — meaningless for Korean) and character-count TF normalization. Results include section_code / section_title context for each hit.

<critical_rules>

Pass 1–5 Korean keywords (or English loanwords used in filings such as "EBITDA", "ROE"). Do NOT pass natural-language sentences.
For Korean nouns, include common morphological variants when possible (e.g. ["매출", "매출액", "매출액의"]). Substring matching handles most derivations but not all.
Stop words / particles (을/를/이/가/은/는) — omit. </critical_rules>

Args: rcept_no: 14-digit DART receipt number keywords: 1–5 search keywords (Korean or ASCII) match_mode: "ANY" (any match) or "ALL" (all must match), default ANY max_results: Max matching snippets to return (default 5, max 50) stock_code: optional 6-digit stock code for cache hit rate

Input Schema

Name	Required	Default
`keywords`	Yes
`rcept_no`	Yes
`match_mode`	No	ANY
`stock_code`	No
`max_results`	No

Output Schema

Name	Required	Description	Default
`result`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Jul 7, 2026

TDQS

A5/5.0

Behavior5/5

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

With no annotations, the description fully discloses behavior: substring matching, character-count TF normalization, no word boundaries, result context, error fallback strategy. No contradictions or gaps.

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?

Well-structured with distinct sections (description, strategy, critical rules, examples, args). Every sentence adds value; no redundancy or verbosity.

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?

Given the complexity (5 params, no annotations, but output schema exists), the description is comprehensive. It covers usage, rules, examples, parameter details, and even a fallback strategy, ensuring an AI agent can use the tool correctly.

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?

Despite 0% schema coverage, the description thoroughly explains each parameter: rcept_no (14-digit), keywords (1-5, include variants, omit particles), match_mode (ANY/ALL), max_results (default 5, max 50), stock_code (optional 6-digit). Adds significant value 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?

The description clearly states the tool performs 'Korean full-text keyword search across a parsed DART report', specifying the action, resource, and scope. It effectively distinguishes from siblings like download_dart_report or get_report_toc, which serve different purposes.

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?

The <strategy> section explicitly instructs to use this for targeted fact-finding and provides fallback guidance. <critical_rules> and <examples> offer detailed, actionable usage criteria, including keyword selection and morphological variants.

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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