korea-stock-mcp

Server Quality Checklist

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

Latest release: v1.4.1
Disambiguation5/5
Each tool targets a distinct operation: corporate code lookup, disclosure retrieval, disclosure listing, financial statements, market type, stock base info, stock trade info, and date. No overlap or ambiguity between tools.
Naming Consistency5/5
All tools follow a consistent 'get_' prefix with snake_case noun phrases (e.g., get_corp_code, get_discovery_list). Uniform naming pattern enhances predictability.
Tool Count5/5
With 8 tools covering corporate info, disclosures, financials, and stock data, the count is well-scoped for a Korean stock market server. Each tool serves a clear purpose without bloat.
Completeness4/5
Covers essential operations: company identification, disclosure listing/content, financial statements, market type, and daily stock info. Missing historical price or index data but adequate for core domain needs.
Average 3.9/5 across 8 of 8 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- 2 of 2 community issues answered or closed in the last 6 months
- 5 commits in the last 12 weeks
- Last stable release on July 12, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under ISC 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.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
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.
Add related servers to improve discoverability.

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

Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not disclose behavioral traits such as read-only nature, authentication requirements, rate limits, or whether the data is cached. The description only states what data is provided, lacking transparency on side effects or prerequisites.
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 sentence that is concise and front-loaded with '재무제표:'. It contains all necessary information without redundancy. However, it could be broken into multiple sentences for 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 financial data and the absence of an output schema, the description is too brief. It does not explain what the returned data contains (e.g., structure of all account items) or how to interpret the response. More context on output format is needed for an agent to use this tool effectively.
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%, with all parameters having detailed descriptions including enum values. The tool description adds minimal value beyond the schema; it does not explain parameter interactions or usage patterns. Baseline 3 is appropriate due to high schema coverage.
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 that the tool provides all account items of XBRL financial statements from periodic reports for listed and major non-listed companies. It uses a specific verb ('제공합니다') and resource ('XBRL재무제표의 모든계정과목'), and the scope ('상장법인 및 주요 비상장법인') distinguishes it from sibling tools like get_stock_base_info or get_disclosure.
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 only explains what the tool does but does not provide context about when it is appropriate or when to use other tools like get_disclosure or get_corp_code.
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 provided, so description must disclose behaviors. It only states the function without mentioning rate limits, authentication, or side effects. The description is minimal.
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?
Two sentences, front-loaded with purpose, no redundant information. Every sentence earns its place.
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?
With 11 parameters, no output schema, and no annotations, the description should provide more context about return format, pagination, or error handling. It only covers basic purpose and one use case.
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?
Schema coverage is 100%, baseline 3. The description adds semantic value by stating that bgn_de is mandatory for recent searches, which goes beyond the schema's optional default.
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 searches for disclosure reports by various conditions. It is distinct from siblings like get_financial_statement or get_stock_base_info, but does not differentiate from the similarly named get_disclosure tool.
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 one specific guideline: 'When searching recent disclosures, you must specify bgn_de.' This is helpful but does not cover when not to use or 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 must disclose all behavioral traits. It reveals that the tool makes one KRX API call per base date, implying rate limits and potential latency. However, it does not mention whether data is real-time, delayed, or historical, nor does it discuss authentication, error handling, or idempotency. For a read operation, the per-date call is useful, but other behaviors are missing, making the transparency mediocre.
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 long, with the main purpose stated first, followed by filtering behavior, then the API call pattern. Every sentence adds value without redundancy or unnecessary detail. It is concise and well-structured for quick comprehension.
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 has three required parameters, no output schema, and no annotations, the description explains inputs and a key behavioral note (per-date API call), but it does not describe the output format or return structure. For a data-retrieval tool, missing output information (e.g., structure of the response) is a gap. However, the tool name implies the returned data fields, so it is minimally complete but not robust.
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 baseline is 3. The description adds that only stocks in codeList are extracted and that basDdList drives per-day API calls, which provides context beyond the schema's field definitions. However, the schema already describes the parameters' purposes adequately (market type, array of codes, array of dates), so the added value is modest.
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 provides trade information (closing price, change rate, open, high, low, volume, amount, market cap, listed shares) for stocks listed on KOSPI, KOSDAQ, KONEX. The verb 'provides' and specific data fields make the purpose unambiguous. Although it doesn't explicitly contrast with siblings, the sibling tools have distinct purposes (e.g., get_stock_base_info likely provides basic stock info, not trade data), so the tool differentiates itself in context.
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 implicitly tells the agent to use this tool when needing trade data for specific dates and stock codes, and that a separate API call is made per date. However, it lacks explicit guidance on when not to use it or alternatives; for example, it doesn't mention that get_stock_base_info might be better for non-trade data like listing dates. Without clear when-to-use vs. when-not-to-use, the guidelines are adequate but not strong.
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 must carry the burden. It explains the return values (Y, K, N, E) which is helpful, but does not disclose error behavior, authentication needs, or whether it is read-only. The behavior is minimally transparent.
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 with two sentences: one stating the purpose and one listing the possible return values. No unnecessary words, and the key information is front-loaded.
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 simplicity of the tool (one param, simple output), the description is fairly complete. It explains the output values. However, it lacks details on error handling or prerequisites like registration with DART, which could be helpful for agents unfamiliar with the API.
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 only parameter (corp_code) has a schema description that fully explains its purpose and format. The tool description does not add additional semantic meaning beyond what the schema already provides, so the baseline score of 3 is appropriate.
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 provides listing market information for stocks registered in DART and specifies the possible return codes (Y, K, N, E). This distinguishes it from sibling tools that deal with disclosures, financial statements, or stock base info.
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 when market type is needed, but provides no explicit guidance on when to use this tool vs alternatives (e.g., get_stock_base_info might also have market info). No conditional usage or prerequisites are mentioned.
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. It discloses the output format and timezones but does not detail whether the output is a single string or multiple values. Adequate for a simple tool.
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?
Single sentence, no unnecessary words. Perfectly concise and front-loaded.
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?
The description is complete given no parameters and no output schema. It specifies the format and timezones, covering all necessary information.
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?
No parameters exist in the schema (100% coverage). The description adds no param info, which is appropriate. Baseline 4 for zero parameters.
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 provides today's date in KST and UTC in YYYYMMDD format. It is a specific verb+resource and distinguishes from sibling tools focused on financial data.
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?
No explicit guidance on when to use or not. However, the tool is self-explanatory and siblings are unrelated, so usage context is implied but not explicit.
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 partial matching, support for unlisted companies, and the need for exact Korean name via web search. It does not mention rate limits or read-only nature, but the behavior is well-covered.
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 brief (4 sentences) and front-loaded with the main purpose. Every sentence adds value, including the important usage note. No redundant or irrelevant information.
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 no output schema, the description appropriately states what the tool returns (unique number, company name, stock code). It covers key query methods and constraints. Could optionally mention output format but not necessary.
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?
Schema coverage is 100%, but the description adds extra meaning: partial matching is explicitly highlighted, stock_code is tied to listed companies, and an important note clarifies input requirements. This exceeds the baseline of 3.
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 provides unique number, company name, and stock code for DART-registered companies, including unlisted ones. It distinguishes from siblings like get_disclosure or get_stock_base_info by focusing on corporate identification data.
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 gives clear input guidance: use corp_name (partial match) or stock_code, and advises web searching for exact Korean name when unsure. It implicitly contrasts with other tools via sibling context, but lacks explicit when-not-to-use or alternative tool comparisons.
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 full burden. Discloses behavior: returns TOC for large docs, section_id retrieves subsections, and sub-TOC for very large sections. Lacks details on error handling or auth, but sufficient for the agent.
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?
Three sentences, well-structured with clear progression: purpose, behavior on large docs, then action steps. Front-loaded with main verb. Could be slightly tighter but very informative.
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 no output schema or annotations, description sufficiently covers key behaviors: large doc handling, section navigation, and user guidance. Lacks details on TOC format but enough for an agent to use 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?
Schema coverage is 100%, so baseline is 3. Description adds significant meaning for section_id: explains its behavior (full doc vs TOC vs section retrieval). This goes beyond the schema description.
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 'parses and fetches original disclosure report files' via DART API, with specific verb and resource. It distinguishes itself from siblings like get_disclosure_list (list) and get_financial_statement (financial data).
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?
Explicitly describes when to use section_id for large documents (>1MB) and provides a complete workflow: get TOC, query sections, then inform user of remaining sections. This guides the agent on proper usage vs alternatives.
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 full burden. Describes API call per basDd and filtering by codeList, but does not mention idempotency or authentication. However, the behavior is transparent enough for an agent to understand usage.
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 concise sentences with no wasted words. Front-loaded with purpose, then filtering rule, then API behavior. Ideal length.
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?
No output schema, but description enumerates key return fields (한글 종목명, 영문 종목명, 상장일, 주식종류, 액면가, 상장주식수). Explains filtering and API call behavior. Fully sufficient for a data retrieval tool given complexity and sibling context.
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?
Schema describes all parameters fully (100% coverage), so description adds marginal value. It reinforces that codeList filters results and explains API call frequency per basDd, which is helpful beyond 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?
Description clearly states it provides base info (name, listing date, etc.) for stocks on given base dates, filtered by market and code list. Unambiguously distinguishes from sibling tools which deal with corporation codes, disclosures, financial statements, etc.
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?
Explicitly notes that only stocks in codeList are extracted and that each basDd triggers an API call, implying rate limit considerations. Does not explicitly state when not to use or compare to siblings, but context is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

GitHub Badge

Glama performs regular codebase and documentation scans to:

Confirm that the MCP server is working as expected.
Confirm that there are no obvious security issues.
Evaluate tool definition quality.

Our badge communicates server capabilities, safety, and installation instructions.

Card Badge

Copy to your README.md:

[![korea-stock-mcp MCP server](https://glama.ai/mcp/servers/jjlabsio/korea-stock-mcp/badges/card.svg)](https://glama.ai/mcp/servers/jjlabsio/korea-stock-mcp)

Score Badge

Copy to your README.md:

[![korea-stock-mcp MCP server](https://glama.ai/mcp/servers/jjlabsio/korea-stock-mcp/badges/score.svg)](https://glama.ai/mcp/servers/jjlabsio/korea-stock-mcp)

Latest Blog Posts

Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)
By Om-Shree-0709 on July 25, 2026.
mcp
Agent Identity
OAuth 2.1
Your AI Chatbot Just Exposed Your CEO's Salary to an Intern
By Om-Shree-0709 on July 2, 2026.
Agent Identity
MCP Security
OAuth Delegation
Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)
By Om-Shree-0709 on June 30, 2026.
Agentic Ai
Prompt Injection
WebAssembly

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jjlabsio/korea-stock-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server