Korea Stock Insight MCP

Server Quality Checklist

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Latest release: v0.1.3
Disambiguation5/5
Each tool has a clearly distinct purpose: company code resolution, filing metadata retrieval, filing listing, financial statements, stock quotes, and date utility. No overlap or ambiguity.
Naming Consistency5/5
All tool names follow a consistent 'verb_noun' pattern in snake_case (e.g., get_corp_code, get_disclosure). This makes the tool set predictable and easy to navigate.
Tool Count5/5
With 6 tools, the server is well-scoped for its purpose. Each tool addresses a core aspect of Korean stock data access without unnecessary bloat or missing essentials.
Completeness4/5
The tool set covers key workflows: company lookup, disclosure browsing and retrieval, financial statements, stock quotes, and a date utility. Minor gaps like missing a dedicated search tool are mitigated by existing partial-match capabilities.
Average 4.1/5 across 6 of 6 tools scored. Lowest: 3.4/5.
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
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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?
No annotations are provided, so the description must disclose behavior. It mentions versioning and the nature of filings (zipped XBRL) but does not state read-only nature, auth needs, or error handling.
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 sentences, front-loaded with purpose, version details in subsequent sentences. No fluff; every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With only one parameter, no output schema, and no annotations, the description is adequate but misses details about return value structure and prerequisites (e.g., needing a disclosure list first).
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 0% because the description does not explain the required parameter 'rcept_no' (format, example, or source). It fails to add meaning 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 action 'fetch' and the resource: metadata + viewer URL for a specific DART filing. It distinguishes from siblings like get_disclosure_list, which retrieves multiple filings.
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?
The description implies usage through version info but does not explicitly state when to use this tool versus alternatives (e.g., get_disclosure_list for lists). No when-not or exclusion guidance.
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?
With no annotations, the description carries full burden. It discloses auto-detection logic (tries .KS then .KQ), data source (yfinance), no API key requirement, version info, and limitations (no intraday yet). This is good transparency for a simple tool, though rate limits and error handling are not mentioned.
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 concise (3 sentences) but includes version history and future plans, which add utility. The key information (market, price, source) is front-loaded. Could be slightly tighter by omitting version details, but still good.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity and lack of output schema, the description covers purpose, data source, and limitations. However, it does not describe the output structure (OHLCV fields) or clarify parameter usage. It is adequate but not complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters2/5
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description must compensate. It does not explain the expected format of 'stock_code' (e.g., 6-digit number) or the meaning of 'days'. The auto-detection mention is indirect. Parameter details are insufficient.
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 it retrieves daily OHLCV bars for Korean-listed stocks on KOSPI/KOSDAQ, with price in KRW. It specifies the verb 'get' and resource 'stock quote', and distinguishes from sibling tools which handle corporate data, disclosures, and financial statements.
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?
The description provides context about auto-detecting market and version limitations (v0.1 uses yfinance, v0.2 may add intraday), but does not explicitly state when to use this tool vs alternatives or when not to use it. Usage is implied rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided. The description does not mention side effects, permissions, or limitations. While it implies a read-only listing, it lacks explicit confirmation or details on pagination, ordering, or return limits.
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 three sentences, front-loaded with purpose, and contains no redundant information. Every sentence adds value.
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?
With no output schema, the description covers returned fields (metadata). However, it could mention whether results are a list, any default ordering, or maximum items. Still sufficient for a simple listing tool.
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 0%. The description explains the 'days' parameter (lookback period) but does not detail 'stock_code' format or constraints. Given only two parameters, the description adds partial value but not enough to fully compensate.
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 lists recent DART disclosures for a Korean-listed company and specifies the returned metadata (date, title, type, receipt number). It distinguishes from sibling 'get_disclosure' by mentioning how to use the returned receipt number.
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?
It provides guidance on using the result with 'get_disclosure', but does not explicitly state when to avoid this tool or contrast with other siblings like 'get_corp_code' or 'get_financial_statement'. However, the implicit differentiation is clear.
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?
No annotations provided, so description carries burden. Describes as a resolution/lookup tool, implicitly non-destructive. Adds context about input format and case-insensitivity. No contradictions.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness3/5
Is the description appropriately sized, front-loaded, and free of redundancy?
Reasonably concise two-paragraph structure, but second paragraph could be more compact. First sentence clearly states purpose. Some redundancy in explaining it is an 8-digit identifier.
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?
Complete for a simple tool with one parameter and no output schema. Covers input format, behavior, and provides guidance for non-Korean speakers. No missing details for the agent to use it correctly.
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?
With 0% schema coverage, description compensates by explaining the 'query' parameter accepts either a 6-digit stock code or Korean company name, and is partial and case-insensitive. Adds significant meaning 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?
Clearly states it resolves Korean company name or stock code to DART corp_code, specifying the unique identifier's role. Distinguishes from sibling tools like get_disclosure which likely require corp_code as input.
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 explicit guidance: accepts stock code (exact) or Korean name (partial, case-insensitive). Includes a workflow hint for non-Korean speakers. However, does not explicitly state when not to use or list alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
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 that the tool returns every account line, implying a read operation, but does not mention authorization needs, rate limits, or any side effects. This is adequate for a straightforward data retrieval tool but lacks deeper 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 concise: a one-line summary followed by a bulleted list of parameters with defaults. Every sentence is necessary and contributes to understanding the tool without redundancy.
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?
For a tool with 4 parameters (2 required) and no output schema, the description covers the purpose, parameters, and a prerequisite step. It could mention the format or structure of the returned data more explicitly, but 'Full XBRL financial statements' gives adequate context. Overall, it is nearly complete.
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?
The input schema has 0% description coverage (no parameter descriptions), but the tool description fully compensates by explaining each parameter: corp_code as '8-digit DART code', year as 'Business year (e.g. 2025)', quarter with default 'FY' and valid values, and fs_div with defaults and meaning ('CFS' vs 'OFS'). This adds significant meaning 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 it returns 'Full XBRL financial statements for a Korean company' and 'every account line from the periodic report', which is a specific verb-resource combination. It distinguishes itself from siblings by referencing `get_corp_code` as a prerequisite, setting it apart from other disclosure or quote tools.
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?
The description explicitly instructs to 'Use `get_corp_code` first to resolve a name/ticker to the 8-digit corp_code', providing clear context for when to use the tool. However, it does not discuss when not to use it or mention alternative tools beyond this prerequisite.
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?
No annotations are provided, so the description carries full burden. It discloses the output includes both KST and UTC dates in YYYYMMDD format. However, it does not specify the exact output structure (e.g., string or object) which is a minor gap. For such a simple tool, this is sufficient.
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 two sentences long, front-loading the purpose and then providing usage guidance. Every sentence adds value without redundancy.
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 the tool has no parameters, no output schema, and the description covers purpose and usage, it is mostly complete. However, the phrasing 'Return today's date in KST and UTC' is slightly ambiguous regarding whether it returns two separate values or one. A bit more clarity on the output structure would improve completeness.
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 input schema has zero parameters, and schema description coverage is 100% by default. The description does not need to add parameter information. Baseline is 4 for zero-parameter tools.
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 returns today's date in KST and UTC in YYYYMMDD format. It is distinct from sibling tools which focus on corporate data, disclosures, financials, and stock quotes, making its purpose as a date utility clear.
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 description explicitly advises use 'before any date-range query' and explains the rationale: Korean market APIs require YYYYMMDD format and KST is the trading-day timezone. This provides clear context for when to use the tool.
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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