DART 재무제표 분석 MCP 서버

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation3/5
There is significant overlap in purpose between tools like generate_financial_chart, generate_financial_dashboard, and generate_html_dashboard, all focused on visualization outputs. However, analyze_financial_data and get_company_list have clearer distinct roles in analysis and data retrieval, preventing complete confusion.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as analyze_financial_data, generate_comparison_table, and get_company_list. This predictability makes the set easy to navigate and understand at a glance.
Tool Count5/5
With 6 tools, this server is well-scoped for financial statement analysis, covering key operations like data analysis, visualization, and company listing. Each tool appears to serve a specific function without unnecessary bloat or missing essentials.
Completeness3/5
The tool set covers analysis and visualization well but lacks obvious CRUD operations for financial data, such as fetching raw financial statements or updating data. This gap might limit agents in performing comprehensive financial workflows beyond the provided tools.
Average 2.8/5 across 6 of 6 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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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?
With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'generates' implying a creation or write operation, but doesn't specify if this is a read-only analysis, requires authentication, has side effects, or details output format. For a tool with three parameters and no annotations, this lack of behavioral context is a significant gap.
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 a single, efficient sentence in Korean that directly states the tool's purpose without unnecessary words. It's appropriately sized for a basic tool description, though it could be more structured if it included usage notes or differentiation from siblings.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has three parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the dashboard output entails (e.g., format, content), behavioral traits, or how it differs from siblings. For a tool named 'generate_financial_dashboard', this leaves critical gaps in understanding its function and use.
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 schema description coverage is 100%, with clear descriptions for all three parameters (corp_codes, report_code, year). The description adds no additional meaning beyond the schema, such as explaining relationships between parameters or usage examples. Baseline score of 3 is appropriate as the schema handles the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool 'generates a comprehensive financial analysis dashboard', which provides a clear verb ('generate') and resource ('dashboard'). However, it's vague about what makes it 'comprehensive' and doesn't differentiate from sibling tools like 'generate_html_dashboard' or 'generate_financial_chart', leaving ambiguity about its specific role.
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 offers no guidance on when to use this tool versus alternatives. It doesn't mention prerequisites, context for use, or exclusions, and fails to reference sibling tools like 'analyze_financial_data' or 'generate_comparison_table' that might serve similar purposes, leaving the agent without direction on tool selection.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It states the action ('가져옵니다' - get/fetch) but doesn't disclose behavioral traits like whether this is a read-only operation, requires authentication, has rate limits, returns paginated results, or what format the list is in (e.g., IDs, names, metadata). For a tool with zero annotation coverage, this leaves significant gaps in understanding how it behaves.
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 a single, efficient sentence in Korean that directly states the purpose. It's appropriately sized for a simple tool with no parameters, with no wasted words. However, it could be slightly more front-loaded if it included key behavioral details, but given the simplicity, it's well-structured.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity (0 parameters, no output schema, no annotations), the description is minimal but incomplete. It states what the tool does but lacks context on behavior, output format, or usage scenarios. For even a simple tool, more detail on what '분석 가능한' means or the return structure would improve completeness, as the agent must guess these aspects.
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?
The tool has 0 parameters, and schema description coverage is 100% (since there are no parameters to describe). The description doesn't need to add parameter semantics, and it correctly doesn't mention any. Baseline for 0 parameters is 4, as there's no parameter information to compensate for or clarify.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose3/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description '분석 가능한 회사 목록을 가져옵니다' (Get a list of companies that can be analyzed) states a clear verb ('가져옵니다' - get/fetch) and resource ('회사 목록' - company list), but lacks specificity about scope or format. It distinguishes from siblings by focusing on listing companies rather than analyzing or visualizing data, but doesn't explicitly differentiate from potential similar list operations.
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?
No explicit guidance on when to use this tool versus alternatives. The description implies this fetches companies for analysis, but doesn't specify prerequisites (e.g., authentication needed), when to use this versus other data-fetching tools, or what makes a company '분석 가능한' (analyzable). Without context, the agent must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. While 'analyzes and visualizes' suggests both computation and presentation, it doesn't specify what kind of analysis (e.g., ratios, trends), visualization format (e.g., charts, tables), whether it's read-only or creates new data, or any performance/rate limit considerations. For a tool with 3 parameters and no annotation coverage, this is insufficient behavioral context.
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?
The description is a single, efficient sentence in Korean that directly states the tool's purpose without unnecessary words. It's appropriately sized for a tool with 3 parameters and gets straight to the point with zero wasted text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the analysis entails, what visualization outputs are produced, or how results are returned. For a financial analysis tool that presumably produces complex outputs, the description should provide more context about what the agent can expect.
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, providing clear documentation for all 3 parameters (corp_codes, report_code, year). The tool description adds no additional parameter information beyond what's already in the schema. According to scoring rules, when schema_description_coverage is high (>80%), the baseline is 3 even with no param info in the description.
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: 'analyzes and visualizes financial statement data for multiple companies.' It specifies both the action (analyze and visualize) and the resource (financial statement data for multiple companies). However, it doesn't explicitly differentiate from siblings like 'generate_financial_chart' or 'generate_financial_dashboard' which also involve financial data visualization.
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. There are no explicit instructions about when/when-not to use it, nor any mention of sibling tools like 'generate_financial_chart' or 'generate_financial_dashboard' that might serve similar purposes. The agent must infer usage from the description alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden for behavioral disclosure. It only states what the tool does ('생성합니다' - generates) but doesn't describe what format the comparison table takes (CSV, HTML, visual?), whether it's a read-only operation, what permissions might be needed, or any rate limits. For a tool with no annotation coverage, this is insufficient behavioral context.
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?
The description is extremely concise - a single Korean sentence that directly states the tool's purpose. There's zero waste or unnecessary verbiage. It's appropriately sized for what it communicates, though it could benefit from additional context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has 3 parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what the comparison table contains, what format it returns, how to interpret the results, or any prerequisites for use. For a financial comparison tool with multiple parameters, this minimal description leaves significant gaps in understanding.
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 schema description coverage is 100%, so all parameters are documented in the schema. The description doesn't add any additional parameter semantics beyond what's already in the schema (corp_codes, report_code, year). It doesn't explain relationships between parameters or provide examples of valid values. Baseline 3 is appropriate when the schema does the heavy lifting.
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: '기업별 재무 지표 비교표를 생성합니다' (Generates a comparison table of financial indicators by company). It specifies the action ('생성합니다' - generates) and resource ('비교표' - comparison table), but doesn't distinguish it from sibling tools like 'generate_financial_chart' or 'generate_financial_dashboard' which might create similar outputs.
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. There's no mention of when this comparison table generation is appropriate versus using 'analyze_financial_data', 'generate_financial_chart', or other sibling tools. The user must infer usage from the tool name alone.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It mentions generating a 'text chart' but doesn't specify output format, size limits, error handling, or performance characteristics. For a tool with 4 required parameters and no output schema, this lack of behavioral detail is a significant gap.
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?
The description is a single, efficient sentence in Korean that directly states the tool's function without unnecessary words. It's appropriately sized and front-loaded, making it easy to parse quickly.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of 4 required parameters, no annotations, and no output schema, the description is incomplete. It doesn't explain what a 'text chart' entails, how results are returned, or any prerequisites, leaving critical gaps for effective tool use in a financial context.
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 input schema fully documents all parameters. The description adds no additional semantic context beyond implying that parameters define the chart's content. This meets the baseline for high schema coverage but doesn't enhance understanding of parameter interactions or constraints.
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 action ('generate') and resource ('text chart for specific financial metrics'), making the purpose understandable. However, it doesn't distinguish this tool from its siblings like 'generate_financial_dashboard' or 'generate_comparison_table', which likely serve similar visualization purposes.
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. With siblings like 'analyze_financial_data' and 'generate_comparison_table', there's no indication of whether this is for quick summaries, detailed analysis, or specific contexts, leaving usage ambiguous.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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 of behavioral disclosure. It states the tool generates an HTML dashboard but does not cover critical aspects such as whether this is a read-only or mutation operation, authentication requirements, rate limits, error handling, or output format details. For a tool with no annotations, this is a significant gap in transparency.
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 a single, efficient sentence that directly states the tool's purpose without unnecessary words. It is front-loaded with the core action and resource. However, it could be more structured by including brief usage hints or distinguishing features to enhance clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of generating a financial dashboard with three parameters and no output schema, the description is incomplete. It lacks details on the output (e.g., HTML content structure, file generation), behavioral traits, and usage context. Without annotations or an output schema, the description does not provide sufficient information for effective tool invocation.
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, with clear documentation for all three parameters ('corp_codes', 'report_code', 'year'). The description does not add any additional semantic details beyond what the schema provides, such as example values or constraints. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate but also does not detract.
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: 'HTML 형식의 재무 분석 대시보드를 생성합니다' (Generates a financial analysis dashboard in HTML format). It specifies the verb '생성합니다' (generates) and the resource '재무 분석 대시보드' (financial analysis dashboard), with the format 'HTML 형식' (HTML format). However, it does not distinguish from sibling tools like 'generate_financial_dashboard' or 'generate_financial_chart', which may have overlapping purposes, so it lacks explicit differentiation.
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 does not mention sibling tools such as 'analyze_financial_data', 'generate_comparison_table', or 'generate_financial_chart', nor does it specify contexts or exclusions for usage. This leaves the agent without clear direction on tool selection.
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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