nhplug-mcp
OfficialServer Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool has a clearly distinct purpose: list_apis for discovery, describe_api for schema, call_api for execution, and get_stock_balance/price for common queries. No ambiguity.
Naming Consistency4/5Mostly consistent verb_noun pattern (call_api, describe_api, list_apis) but stock shortcuts use 'get_stock_' prefix, which is a minor deviation. Overall readable and predictable.
Tool Count5/5Five tools is well-scoped for a financial API wrapper: listing, schema, generic call, plus two convenience shortcuts. Not excessive or insufficient.
Completeness5/5Covers the full workflow: discover APIs (list_apis), inspect input schema (describe_api), execute any API (call_api), and provides shortcuts for common balance and price queries. No dead ends.
Average 3.7/5 across 5 of 5 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 20 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
This repository is licensed under MIT License.
This repository includes a README.md file.
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How is the quality score calculated?
The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).
Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.
Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).
Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.
Tool Scores
- Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description provides minimal behavioral details. It does not mention authentication, side effects, or that it is a read-only operation. The description adds little beyond the tool name.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with a single sentence and an alias note. It is efficient but could be more structured for clarity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple balance inquiry tool, the description lacks completeness. It does not mention what the response contains, error handling, or any contextual information beyond the basic function.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Input schema has 100% coverage with a parameter description. The tool description does not add extra meaning beyond the schema, meeting the baseline for parameter semantics.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool inquires domestic stock account balance. It does not explicitly differentiate from sibling tools like get_stock_price, but the purpose is specific and understandable.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance on when to use this tool versus alternatives such as get_stock_price or call_api. The description lacks context on prerequisites or 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?
No annotations are provided, so the description carries the full burden. It declares the tool is read-only by implication (retrieving prices) and mentions it is a shortcut, but fails to disclose error handling, rate limits, or response format, leaving significant behavioral ambiguity.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is extremely concise with two sentences, front-loading the purpose and adding a useful comparison. No redundant or unnecessary text.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness2/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the low complexity (single parameter, no output schema), the description should cover what the agent receives. It omits the return format (e.g., price, currency, timestamp), making it incomplete for an agent to confidently use the tool without additional inference.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100% with a clear description of the stock_code parameter. The tool description adds no additional semantic value beyond the schema, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool retrieves current domestic stock prices, using a specific verb ('조회합니다') and resource ('현재가 시세'). It distinguishes itself from siblings by noting it is a shortcut equivalent to calling krstockQuoteCurrentPrice via call_api.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines2/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description does not provide explicit guidance on when to use this tool versus alternatives like call_api or other sibling tools. The hint about being a shortcut implies a use case but lacks clear context or exclusions.
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?
With no annotations, the description carries the full burden. It discloses automatic processing of auth/headers/envelope and the trading mode condition for orders. However, it does not specify whether calls are read-only or write, rate limits, error handling, or idempotency, leaving significant behavioral gaps.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two sentences, front-loaded with the core action, followed by a critical condition. No wasted words. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, yet the description does not explain what the return value is (e.g., raw API response) or error handling. While it covers authentication and a key condition, it omits pagination (cts parameter) and response format, leaving the agent underinformed for a generic API caller.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters3/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and each parameter has a description. The tool description adds no extra meaning beyond the schema, referencing operationId and input only in passing and omitting cts entirely. Thus it meets the baseline for high-coverage schema.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states that the tool calls the NH Open API with operationId and input, automatically handling authentication, headers, and envelope. It distinguishes itself from siblings by being the actual execution tool, while siblings like describe_api or list_apis provide metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides a conditional guideline for order APIs requiring trading mode, but does not explicitly advise when to use this tool vs its siblings (e.g., to first get operationId from describe_api or list_apis). The context for using alternatives is implied but not stated.
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 provided, so description carries full burden. Discloses that order APIs are conditionally displayed based on server mode, which is useful. Does not mention read-only nature, error handling, or response format, but for a listing endpoint this is adequate.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Three sentences with no redundancy. Front-loaded with purpose, then filtering options, then workflow advice. Every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness3/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
No output schema exists, but description does not mention what the response contains (expected list of endpoints). However, the workflow with sibling tools (describe_api, call_api) provides implicit context. Adequate for a simple listing tool, but could be more explicit about return structure.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema covers all 3 parameters (100% coverage). Description adds context: explains domain as asset class filter, keyword as partial match on summary/operationId/path, and category as partial match. This clarifies the matching behavior beyond the schema descriptions.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states it lists available API endpoints from NH Investment & Securities Open API, specifies filtering dimensions (domain, category, keyword), and explicitly distinguishes it from sibling tools by outlining the workflow: use this to find operationId, then invoke describe_api or call_api.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly advises using this tool first to locate operationId before calling describe_api/call_api. Also notes that order/trade APIs are only shown when server is in trade active mode. Does not explicitly state when not to use, but the sequential guidance 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?
Describes read-only behavior of returning schema with no side effects. No annotations provided, but description adequately covers behavioral traits.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness5/5Is the description appropriately sized, front-loaded, and free of redundancy?
Two concise sentences, front-loaded with purpose, no redundant information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Description covers essential purpose and usage. Without output schema, return format could be more detailed, but sufficient for a simple schema-query tool.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters4/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Single parameter described with example (krstockQuoteCurrentPrice) and source (list_apis). Adds value beyond schema's description.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5Does the description clearly state what the tool does and how it differs from similar tools?
Description clearly states returns schema of input fields for a specific operationId. Distinct from sibling tools like list_apis (lists APIs) and call_api (makes calls).
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly says 'check what parameters are needed before calling call_api', giving clear when-to-use guidance. Could mention alternatives but not necessary.
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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