koreanpulse

by whdrnr2583-cmd

Overview Schema Related Servers Score Discussions

Python

Local

Server Quality Checklist

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

Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: server intro, company/code resolution, filings retrieval, news search, and specialized classification for activists and foreign holders. No overlapping functionality.
Naming Consistency4/5
Tool names follow a consistent verb_noun pattern (lookup_, monitor_, resolve_, search_, track_), except 'koreanpulse_about' which is a server self-description. Minor deviation but overall predictable.
Tool Count5/5
7 tools is well-scoped for a Korean financial data server covering company IDs, filings, news, and two specialized classification tools. Not too many or too few.
Completeness4/5
The tool set covers the core workflow: company lookup, filings, news, and key classifications. Minor gap in general company financial info, but the domain focus on DART filings and activism is well served.
Average 4.5/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- 0 of 1 community issues answered or closed in the last 6 months
- 39 commits in the last 12 weeks
- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under AGPL 3.0.
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

Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true, and openWorldHint=false. The description adds 'Free tier', which hints at rate limits or accessibility, providing extra context beyond annotations for a simple lookup 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?
The description is two sentences: the first concisely defines the tool, the second gives usage guidance with examples. It is front-loaded, every sentence adds value, and no words are wasted.
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 simple resolver with an output schema, the description covers the main purpose, usage context, and downstream applications. It does not detail error handling or rate limits, but overall it is sufficient for the tool's simplicity.
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%, and the description does not explain the two parameters (stock_code and license_key). The example values for stock_code are helpful, but license_key is not mentioned, and no parameter details are added 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 resolves a KRX 6-digit ticker to a DART corp entry, provides examples, and explains the output (company name + corp_code). It distinguishes itself from siblings like lookup_corp_code and search_korean_industry_news by specifying the purpose and downstream use.
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 says 'Use this tool when the user provides a 6-digit Korean stock code... and you need the company name + corp_code'. This gives clear context for when to use it, though it does not mention 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.
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations declare readOnlyHint true, destructiveHint false, idempotentHint true. The description adds critical behavioral context: requires a license key, returns a paywall message without a valid license, and does not generate trading advice. It also explains that the allowlist is not derivable from raw DART filings.
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 lengthy but well-structured with clear paragraphs. It front-loads the core purpose, then details usage, error handling, and distinctions. Some repetition exists (allowlist listed twice), but overall it is organized and informative.
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 tool's complexity, the description covers all necessary aspects: purpose, use cases, parameter implications, error handling, and relationship to siblings. The presence of an output schema and clear annotations complement the description well.
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 coverage is 100%, so the baseline is 3. The description does not add significant detail about parameters beyond what the schema provides, except emphasizing the `license_key` requirement. It confirms the purpose of each parameter indirectly.
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 monitors foreign 5%-rule holders on KOSPI/KOSDAQ and lists 20 specific entities. It distinguishes itself from the sibling `monitor_activist_investors` by explaining the difference in intent (allocation vs governance pressure).
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 provides explicit when-to-use scenarios (e.g., foreign capital flow, specific entity holdings) and when-not-to-use (e.g., not for activist). It also gives clear instructions on handling license errors and warns against using a fallback tool.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Adds key behavioral context beyond annotations: requires license key, returns paywall message without valid license, and instructs to surface activation URL. No contradiction with annotations.
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?
Description is verbose with extensive instructions on license handling and fallback behavior. While important, it could be more streamlined.
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 complexity (license gate, fallback behavior, specific filer list), the description is complete. Output schema exists, so return values need not be explained.
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 coverage is 100% with all parameters well-described. Description adds no extra parameter information, meeting baseline for high 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?
Description clearly states the tool monitors Korean activist investor disclosures on DART and tags specific named filers, distinguishing it from siblings like track_korean_filings which lack activist tagging.
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 tells when to use (e.g., user asks about Korean shareholder activism) and when not to (e.g., do not silently retry with track_korean_filings because activist match is not derivable from raw feed).
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?
Annotations already indicate read-only, non-destructive, idempotent behavior. Description adds valuable context: free tier, aggressive caching for translations, and limit cap of 50.
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?
Concise paragraph front-loads core purpose and usage. Every sentence adds value—no redundancy or fluff.
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 rich annotations and output schema, description covers all necessary aspects: when to use, parameters, sources, translation behavior, and industry list. No gaps.
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 3. Description adds meaning by clarifying default nulls, industry list (though schema already has it), source keys, limit max, and the condition for license_key.
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 searches Korean industry news across 16 sectors with English translation. It names specific sources and explicitly lists query scenarios, distinguishing it from unrelated siblings.
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 on when to use, listing example queries and industry tags. Does not mention when not to use, but siblings are distinct enough that exclusions are not critical.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate read-only, idempotent, and non-destructive behavior. The description adds significant behavioral context: it returns raw filings without classification, does not require a license for free tier, and explicitly warns about paywall activation for paid tools. No contradiction with annotations.
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?
The description is lengthy (multiple paragraphs) and includes repetitive warnings. While front-loaded with purpose, it lacks conciseness. It is structured but could be more efficient.
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 tool's complexity (multiple parameters, sibling tools, and an output schema), the description is thorough. It covers purpose, usage, limitations, and links to other tools. Output schema exists, so return values are handled separately.
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 coverage is 100% with all parameters described. The description does not add substantial parameter details beyond the schema, but it does provide high-level context about the license_key and free tier. Baseline 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 it retrieves Korean DART filings for KOSPI/KOSDAQ/KONEX/KRX companies, listing specific filing types. It distinguishes from siblings like monitor_activist_investors and monitor_foreign_holders by explicitly stating when to use each.
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 provides usage scenarios ('Use this tool when...') and warns against using it for activist or foreign holder queries, directing users to alternative tools. It also explains that the free tier provides raw data without classification.
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?
Annotations already declare readOnlyHint, idempotentHint, and openWorldHint. The description adds context about free tier and that the output is a structured dict, providing additional behavioral insight beyond annotations.
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 with no wasted words. Front-loads the core purpose ('Server self-description') and immediately follows with usage guidance. Every sentence earns its place.
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 no parameters, rich annotations, and existence of output schema (implied), the description is fully complete. It covers purpose, usage, return format, and limitations (free tier).
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, so schema coverage is 100%. The description adds meaning by explaining what the output contains (capability matrix, tool catalog, classifier counts, supported query patterns) and the use case, going beyond the schema's emptiness.
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 provides a server self-description with capability matrix, tool catalog, classifier counts, and primary sources. It distinguishes from sibling tools by being the 'about' tool that describes the server's overall capabilities.
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 mentions when to use: when an agent first connects to decide if the server can answer, or when the user asks about the server's capabilities. Also describes the return format as a structured dict for downstream ingestion.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already provide readOnlyHint, idempotentHint, and non-destructive. Description adds free tier, 117K+ entity coverage across multiple exchanges, and disambiguation capability – context beyond annotations.
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 concise paragraphs: first states purpose and coverage, second gives usage guidance. No redundant sentences.
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?
Output schema exists so return values need not be described. Covers purpose, scope, prerequisites, disambiguation – fully addresses complexity.
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 coverage is 100% with detailed parameter descriptions (examples for query, default values). Description does not add extra information beyond schema, so baseline score of 3 applies.
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?
Clear verb-resource pair: resolves Korean company name to DART corp_code. Distinct from sibling tools like track_korean_filings and monitor_activist_investors, which use the corp_code.
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 states when to use: when a Korean company name is mentioned and corp_code is needed as precondition for other tools. Also mentions disambiguation for same-name entities.
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:

[![koreanpulse MCP server](https://glama.ai/mcp/servers/whdrnr2583-cmd/koreanpulse/badges/card.svg)](https://glama.ai/mcp/servers/whdrnr2583-cmd/koreanpulse)

Score Badge

Copy to your README.md:

[![koreanpulse MCP server](https://glama.ai/mcp/servers/whdrnr2583-cmd/koreanpulse/badges/score.svg)](https://glama.ai/mcp/servers/whdrnr2583-cmd/koreanpulse)

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/whdrnr2583-cmd/koreanpulse'

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