독립유공자 공훈록 MCP 서버

by kokoa-tools

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: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose: cache management, code lookups for hunkuk and workout affiliation, and two types of merit record retrieval (list and public report). No ambiguity or overlap exists between these functions.
Naming Consistency5/5
All tools follow a consistent verb_noun pattern with 'get_' or 'clear_' prefixes, using snake_case throughout. The naming is predictable and readable across all five tools.
Tool Count5/5
With 5 tools, this server is well-scoped for its domain of Korean independence merit records. Each tool serves a specific, necessary function without bloat or redundancy.
Completeness4/5
The toolset covers core read operations (list, report, code lookups) and cache management well. A minor gap exists in write/update capabilities (e.g., no create or modify tools), but this is reasonable for a likely read-only public data service.
Average 2.9/5 across 5 of 5 tools scored.
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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
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?
With no annotations provided, the description carries full burden for behavioral disclosure. While 'retrieves' implies a read operation, it doesn't specify whether this requires authentication, has rate limits, returns paginated results, or what format the information comes in. For a retrieval tool with zero 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.
Conciseness4/5
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise - a single sentence that directly states the tool's purpose. There's no wasted language or unnecessary elaboration. However, the extreme brevity borders on under-specification rather than optimal conciseness, preventing a perfect score.
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 no annotations, no output schema, and a retrieval operation that likely returns structured data, the description is incomplete. It doesn't explain what 'hunkuk codes' are, what format the information returns in, or any behavioral characteristics. For a data retrieval tool without structured output documentation, this leaves significant 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?
The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation (none). The description doesn't need to compensate for any parameter gaps. The baseline for 0 parameters with complete schema coverage is 4, as there's no parameter information to add beyond what's already clear from the schema.
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 purpose as 'retrieves hunkuk code information' which is a clear verb+resource combination. However, it doesn't distinguish this tool from its siblings like 'get_merit_list' or 'get_workout_affil_codes' - all appear to be retrieval operations for different data types. The purpose is understandable but lacks sibling 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. There's no mention of prerequisites, when this tool is appropriate versus other retrieval tools in the sibling list, or any context about what 'hunkuk codes' represent. 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?
With no annotations provided, the description carries full burden but only states it retrieves information, implying a read-only operation. It lacks details on behavioral traits like rate limits, authentication needs, error handling, or what '정보' (information) entails in terms of format or scope, which is insufficient for a tool with zero annotation coverage.
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 action and resource. It's front-loaded with the core purpose, though it could be slightly more structured if it included minor usage hints without adding bulk.
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), the description is minimal but incomplete. It doesn't explain the return values or what 'code information' includes, and with no annotations, it fails to provide necessary context like data format or typical use cases, leaving gaps for an agent to understand full functionality.
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 0 parameters with 100% coverage, so no parameter documentation is needed. The description doesn't add parameter details, but this is acceptable as there are no parameters to explain. A baseline of 4 is appropriate since the schema fully covers the absence of parameters.
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 purpose ('조회합니다' meaning 'retrieves' or 'looks up') and resource ('운동계열 코드 정보' meaning 'exercise series code information'), which is clear but basic. It doesn't differentiate from sibling tools like 'get_hunkuk_codes' or 'get_merit_list', leaving ambiguity about what makes this specific code type distinct.
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 guidance is provided on when to use this tool versus alternatives. It doesn't mention context, prerequisites, or exclusions, such as whether it's for reference data, filtering, or if other tools handle related codes. This leaves the agent without direction on appropriate usage scenarios.
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 but provides minimal information. It doesn't mention that this is a read-only operation (implied by 'get' but not explicit), doesn't discuss pagination behavior beyond what's in the schema, doesn't mention rate limits, authentication requirements, or what happens when no filters are applied. For a tool with 15 parameters and no annotations, 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. There's no wasted language or unnecessary elaboration. It's appropriately sized for a data retrieval tool and gets straight to the point without preamble.
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?
For a tool with 15 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the returned data looks like, how results are structured, whether there's pagination beyond the two pagination parameters, or what happens when multiple filters are applied. The description fails to provide the necessary context for an agent to understand the complete behavior of this data retrieval operation.
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 input schema. The description adds no additional parameter information beyond what's already in the schema - it doesn't explain how filtering works, whether parameters are AND/OR combined, or provide examples of parameter usage. With complete schema coverage, 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.
Purpose4/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('조회합니다' - retrieve/lookup) and resource ('독립유공자 공훈록 목록' - list of independence merit records). It's specific about what data is being accessed. However, it doesn't distinguish this tool from its siblings like 'get_public_report' or explain how this list differs from other data retrieval tools in the server.
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 list retrieval is appropriate compared to other sibling tools like 'get_public_report' or when to use the filtering parameters versus retrieving all records. The agent receives no contextual usage instructions.
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 but only states the basic action. It doesn't mention whether this is a read-only operation, whether it requires authentication, what format the results come in, whether there are rate limits, or how pagination works despite having pagination parameters. For a tool with 16 parameters and no annotation coverage, this is insufficient.
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 any unnecessary words or structural complexity. It's perfectly front-loaded with the essential information.
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?
For a complex tool with 16 parameters, no annotations, and no output schema, the description is inadequate. It doesn't explain what the tool returns, how results are structured, whether it's a search or lookup operation, or how the various filtering parameters interact. The agent would struggle to use this tool effectively without trial and error.
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 description adds no parameter information beyond what's already in the schema, which has 100% coverage with detailed descriptions for all 16 parameters including formats, constraints, and enum values. The baseline score of 3 is appropriate since the schema does all the heavy lifting for parameter documentation.
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 verb ('조회합니다' - retrieves/looks up) and resource ('독립유공자 공적조서' - independence activist merit records), making the purpose immediately understandable. It doesn't specifically differentiate from sibling tools like 'get_merit_list' which might retrieve similar data, so it doesn't reach the highest score for sibling 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 like 'get_merit_list' or other sibling tools. There's no mention of prerequisites, appropriate contexts, or comparison with other data retrieval methods available in the server.
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. '초기화합니다' (initializes/clears) implies a destructive mutation, but the description doesn't disclose important behavioral traits: whether this requires special permissions, whether the operation is reversible, what '모두' (all) means in terms of scope, or any side effects. For a destructive tool with zero annotation coverage, this is inadequate.
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 with zero wasted words. It's appropriately sized for a simple tool and front-loads the core action. Every word 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?
Given this is a destructive mutation tool with no annotations and no output schema, the description is incomplete. It doesn't explain what '초기화' entails operationally, what data is affected, whether there are confirmation prompts, what the return value might be, or error conditions. For a tool that presumably modifies system state, this leaves critical 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?
The tool has 0 parameters with 100% schema description coverage, so the baseline is 4. The description appropriately doesn't discuss parameters since none exist, and it doesn't need to compensate for any schema gaps.
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 ('초기화합니다' - initializes/clears) and the target ('캐시된 데이터' - cached data), providing a specific verb+resource combination. However, it doesn't differentiate from sibling tools, which appear to be unrelated read operations (get_* tools), so it doesn't need sibling differentiation but doesn't explicitly state this distinction.
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 doesn't mention prerequisites, timing considerations, or when not to use it. Given that siblings are get_* tools, the distinction is implied (this is a write operation vs their read operations), but this isn't explicitly stated in the description.
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:

[![e-gonghun-mcp MCP server](https://glama.ai/mcp/servers/kokoa-tools/e-gonghun-mcp/badges/card.svg)](https://glama.ai/mcp/servers/kokoa-tools/e-gonghun-mcp)

Score Badge

Copy to your README.md:

[![e-gonghun-mcp MCP server](https://glama.ai/mcp/servers/kokoa-tools/e-gonghun-mcp/badges/score.svg)](https://glama.ai/mcp/servers/kokoa-tools/e-gonghun-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/kokoa-tools/e-gonghun-mcp'

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