Stock MCP Server

Server Quality Checklist

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Latest release: v0.1.0
Disambiguation5/5
Each tool has a clearly distinct purpose: diagnosis, analysis (KR/US), screening by strategy and market, and ticker search. There is no overlap between tools; even the screening tools are differentiated by market and strategy type.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., diagnose_stock, get_stock_analysis, screen_kr_breakout). The naming scheme is predictable and uniform across all tools.
Tool Count5/5
With 8 tools, the server is well-scoped for stock analysis and screening. It covers core functionalities without being overly numerous or too sparse, providing a focused yet comprehensive set.
Completeness4/5
The tool set covers analysis, diagnosis, screening for both markets, and ticker search. Minor gaps exist, such as the absence of a dedicated quote tool, but the analysis tools include OHLCV. Overall, the surface is thorough for its domain.
Average 4/5 across 8 of 8 tools scored. Lowest: 2.9/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- No stable releases found
- No critical vulnerability alerts
- No high-severity vulnerability alerts
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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 carries the full burden. It only states 'analyzes' without clarifying whether it is read-only, what side effects exist, or any behavioral traits beyond basic analysis. It does not describe the nature of the analysis or error conditions.
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 very concise with two sentences plus a parameter list. Every piece of information is essential, and it is front-loaded with the main purpose.
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?
Despite having an output schema, the description does not explain what the tool returns or how to interpret results. It also fails to mention any usage context relative to siblings, making it incomplete for a tool with multiple similar alternatives.
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%, but the description adds value by providing examples for 'symbol' (e.g., 'AAPL') and clarifying 'days' as the inquiry period in days with default 120. This is adequate but not extensive; no constraints or allowed values are given.
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 it analyzes US stock tickers, using the verb 'analyzes' and specifying 'US stock'. This distinguishes it from the sibling 'get_stock_analysis' which likely covers other markets, though not explicitly stated.
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 on when to use this tool versus alternatives like 'get_stock_analysis' or 'screen_us_momentum'. No when-not scenarios 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden. It only states the diagnosis purpose without disclosing behavioral traits like read-only nature, rate limits, or side effects. The agent cannot infer safety or performance characteristics.
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 well-structured with a clear purpose statement, enumerated strategy lists, and parameter documentation. It is front-loaded and avoids redundancy, though it could be slightly shorter by consolidating the list format.
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 presence of an output schema to describe return values, the description adequately covers the tool's logic (10 strategies) and input parameters. No critical gaps are apparent for a diagnostic tool with simple inputs.
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 description coverage, the description compensates by providing clear examples and defaults for both parameters (ticker and market), adding practical meaning beyond the schema's type-only definitions.
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 identifies the tool as diagnosing a stock against 10 specific short-term strategies, with a detailed list of those strategies. This distinguishes it from sibling tools like screen_kr_breakout or get_stock_analysis, which focus on screening or broader analysis.
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 checking strategy conditions for a specific stock, but does not explicitly state when to use this tool versus alternatives or provide exclusion criteria. Guidance is present only through context and inference.
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. It discloses the basic operation but lacks details on error handling, rate limits, or whether it is a read-only operation.
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 very concise, with two sentences and an args block. It is front-loaded and contains no unnecessary information.
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 an output schema, return values need not be explained. However, the description lacks details on search behavior (e.g., partial matches, case sensitivity) and error conditions, making it marginally adequate.
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 description coverage is 0%, so the description adds necessary meaning for the keyword parameter with examples. It improves understanding beyond the raw 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's function: searching for a stock code by stock name. It provides examples and distinguishes from sibling tools that focus on analysis and screening.
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 needing a stock code from a name, but does not provide explicit guidance on when to use this tool versus alternatives, nor any exclusion criteria.
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 must cover behaviors. It states the tool analyzes OHLCV + technical indicators but does not disclose specific indicators, error handling, or data source. Output schema exists, reducing need for return details, but some traits remain opaque.
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: two sentences plus a compact Args list. Every sentence serves a purpose, with no fluff. The main action 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 presence of an output schema (which covers return structure), the description sufficiently explains input parameters and core functionality. It could mention error handling or data freshness, but overall it is complete for a straightforward analysis tool.
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 description coverage is 0%, and the description compensates with clear explanations: ticker with examples (e.g., '347860' for Alchera) and days with default value (120). This adds meaning beyond the schema's basic type/title.
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 analyzes OHLCV and technical indicators for a given stock code. It provides specific ticker examples for Korean stocks, distinguishing it from sibling tools like get_us_stock_analysis for US stocks.
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 implies usage for Korean stocks via examples (e.g., '005930' for Samsung) and sibling names suggest alternatives, but it does not explicitly state when to use this tool versus others or any prerequisites.
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 provided, the description carries the full burden of behavioral disclosure. It specifies filtering logic and optional filters, but does not explicitly state side effects (e.g., read-only, no modifications), rate limits, or error conditions. The phrase '추격 매수 방지' hints at safety but is not fully transparent. The output schema exists, so return value details are covered, but behavior beyond filtering is unclear.
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 detailed but well-structured with section headers ([시드], [사전필터], etc.) and a bulleted list for parameters. It is slightly lengthy but every section contributes to understanding. The front-loading could be improved by placing the main purpose more prominently, but overall it is organized and efficient.
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 complexity (13 parameters, multi-step filtering) and presence of an output schema, the description provides strong context. It explains the algorithm flow and option details. However, it mentions '10가지 기술적 전략' without elaboration, leaving a gap. No mention of error handling or performance considerations. Still, it is largely complete for an experienced agent.
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 13 parameters with 0% description coverage, so the description fully compensates. It provides Korean explanations for each parameter, including allowed values for 'market', defaults, and purpose (e.g., 'top_n: 시드 후보 상한'). This adds significant meaning beyond the bare schema properties, making parameter semantics very clear.
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 filters Korean stocks for volume surge and trend alignment over recent N days. It uses specific verbs and resource description ('필터링합니다') and is distinct from siblings like 'screen_kr_breakout' which focuses on breakouts. The purpose is unambiguous and specific.
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 details a step-by-step filtering pipeline with seed, pre-filters, and optional filters, providing clear context on how the tool operates. However, it does not explicitly compare with sibling tools (e.g., when to use this over screen_kr_breakout or screen_us_momentum) or state when not to use it. The sequential logic is well explained, but exclusions are missing.
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 data sources (Yahoo screeners), pre-filter, and strategy (7 breakout-specific). However, it does not mention any limitations, rate limits, or mutation behavior; the tool appears read-only but this is not confirmed.
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 well-structured with a clear purpose statement, technical details, and an Args section. It is moderately long but avoids fluff. Every sentence adds value, though some minor redundancy could be trimmed.
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's complexity (10 parameters, no annotations, output schema exists), the description adequately covers purpose, usage, seed logic, and parameter meanings. It references a sibling for comparison, but does not elaborate on output fields (acceptable since output schema exists).
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, but the description lists all 10 parameters with concise explanations (e.g., 'symbols: None uses Yahoo', 'exclude_if_up_pct: exclude stocks up more than this % today'). This fully compensates for the schema's lack of descriptions.
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 filters US stocks about to break out, explicitly differentiating itself from the sibling 'screen_us_momentum' by stating it is the opposite concept. It provides specific verbs and resource identification.
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 specifies when to use this tool (for accumulation/breakout stocks) and contrasts it with 'screen_us_momentum' for momentum stocks. It also explains default seed behavior, but does not explicitly state when not to use it beyond this comparison.
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 details the step-by-step filtering logic (exclusion, volume spike, trend template, etc.) and mentions 10 technical strategies, but omits specifics about the strategies and does not disclose if the tool has any side effects (e.g., rate limits).
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 well-structured with a summary, subsections, and parameter list. It is fairly long but every section adds value. Could be slightly more concise without losing info.
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?
Covers the filtering logic and all 12 parameters comprehensively. Output is not described but output schema exists. Missing details on the 10 technical strategies, but overall adequate for the complexity.
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?
Schema coverage is 0%, but the description thoroughly explains each parameter with defaults and meaning (e.g., exclude_if_up_pct, spike_threshold). This adds substantial value beyond the raw 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 explicitly states it filters US stocks based on volume surge and trend alignment, with specific references to Yahoo data sources. It clearly differentiates from sibling tools like screen_kr_momentum and screen_us_breakout by specifying 'US' and 'momentum' criteria.
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 clear context on when to use (filtering momentum stocks from Yahoo) and details the sequential pre-filters. However, lacks explicit 'when not to use' or direct comparison to siblings like diagnose_stock.
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 bears full burden. It details the filtering pipeline and logic extensively, but does not explicitly state that the tool is read-only, nor mention any rate limits or side effects. Adequate but not fully covering runtime behavior.
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 quite long due to detailed logic and parameter explanations. It is well-structured with sections (seed, pre-filters, strategy) and bullet points, but could be slightly more concise. Still, every sentence earns its place given the tool's complexity.
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 11 optional parameters, no annotations, and presence of output schema, the description is comprehensive. It covers input purpose, filtering steps, strategy types, and sibling differentiation. The output schema is present so return values need not be described.
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?
Schema description coverage is 0%, so the description compensates fully. Every parameter, including market, top_n, thresholds, and periods, is explained with typical values and purpose in the 'Args' section, adding significant meaning beyond the bare 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 filters Korean stocks in the 'breakout-ready' phase, contrasting with screen_kr_momentum. It details the seed sources, pre-filters, and strategies, making the purpose specific and distinct from siblings.
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 contrasts with screen_kr_momentum ('반대 컨셉'), explicitly excludes already-strong stocks, and lists dedicated breakout strategies. Provides clear context for when to use this tool over alternatives.
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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