Multi-Purpose MCP Server

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: calculator for arithmetic, code_review for code analysis, current_time for time retrieval, generate_image for image generation, and greeting for personalized salutations. The descriptions clearly differentiate their domains, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent snake_case pattern with descriptive noun-based naming (e.g., calculator, code_review, current_time). There are no deviations in style or verb usage, making the set predictable and easy to parse.
Tool Count3/5
With 5 tools, the count is reasonable but feels thin for a 'Multi-Purpose' server, as it covers only a few unrelated domains without depth in any one area. It's borderline for the stated scope, lacking the breadth implied by the server name.
Completeness2/5
The server claims to be multi-purpose but has significant gaps: tools are isolated with no clear domain coverage (e.g., no CRUD operations, limited utility functions), and the set doesn't support cohesive workflows. This will likely cause agent failures when trying to accomplish broader tasks.
Average 3.1/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
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- 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 of behavioral disclosure. It states the tool performs arithmetic operations but doesn't mention error handling (e.g., division by zero), output format, or any behavioral traits like rate limits or side effects. This is a significant gap for a tool with no 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.
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 function without unnecessary words. It is appropriately sized and front-loaded, with zero waste, making it highly concise and well-structured.
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 (a calculator with 3 parameters) and no annotations or output schema, the description is incomplete. It lacks details on behavioral aspects, error handling, and return values, which are crucial for an AI agent to use the tool correctly. The description does not compensate for the absence of structured data.
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%, with clear descriptions for all parameters (num1, num2, operation with enum values). The description adds no additional meaning beyond the schema, such as explaining parameter interactions or constraints. Baseline 3 is appropriate when the schema does the heavy lifting.
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's purpose: performing four basic arithmetic operations on two numbers. It uses specific verbs ('사칙연산을 수행하는') and identifies the resource ('두 숫자'). However, it doesn't differentiate from sibling tools like 'code_review' or 'generate_image', which are unrelated, so it doesn't need sibling differentiation for a 5.
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 any context, exclusions, or prerequisites. While sibling tools are unrelated, the description lacks any usage instructions, such as when arithmetic calculations are needed over other tools.
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 of behavioral disclosure. The description only states that it 'creates a detailed code review prompt', but doesn't explain what this entails—e.g., whether it generates text, requires specific permissions, has rate limits, or what the output format looks like. For a tool with no annotations and no output schema, this is a significant gap in transparency.
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 function without unnecessary words. It's front-loaded with the core purpose and avoids redundancy. Every part of the sentence contributes to understanding what the tool does, making it highly concise and well-structured.
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 complexity (3 parameters, no annotations, no output schema), the description is incomplete. It doesn't address behavioral aspects like what the tool outputs (e.g., a text prompt), any constraints, or how it interacts with the code review process. Without annotations or an output schema, the description should provide more context to be fully helpful, but it falls short.
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%, meaning all parameters are documented in the input schema. The description doesn't add any additional semantic information about the parameters beyond what's already in the schema (e.g., it doesn't explain the purpose of 'reviewType' or provide examples beyond the enum). According to the rules, with high schema coverage, the baseline is 3 even without param info in the description.
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's purpose: '생성하는 도구' (creates) '상세한 코드 리뷰 프롬프트' (detailed code review prompt) for '사용자가 제공한 코드' (user-provided code). It specifies the verb (creates), resource (code review prompt), and target (user-provided code). However, it doesn't explicitly differentiate from sibling tools like 'generate_image' or 'calculator', which is why it doesn't reach a 5.
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 any prerequisites, context for use, or exclusions. For example, it doesn't specify if this is for generating prompts versus executing reviews, or how it differs from general-purpose tools like 'greeting'. This leaves the agent without clear usage direction.
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 for behavioral disclosure. It only states the basic function without details on traits like rate limits, quality of output, processing time, or error conditions. For a generative tool with zero annotation coverage, this is a significant gap in transparency.
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 that directly states the tool's purpose without any wasted words. It is appropriately sized and front-loaded, making it easy to understand quickly.
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 complexity (generative AI with potential for varied outputs) and lack of annotations and output schema, the description is incomplete. It doesn't cover behavioral aspects, return values, or usage context, leaving gaps for an AI agent to understand how to invoke it 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?
The schema description coverage is 100%, with the parameter 'prompt' fully documented in the schema. The description adds no additional meaning beyond what the schema provides, such as prompt formatting tips or examples. Baseline 3 is appropriate since the schema does the heavy lifting.
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's purpose: '텍스트 프롬프트를 사용하여 이미지를 생성하는 도구' translates to 'A tool that generates images using text prompts.' This specifies the verb (generate images) and resource (images) with the mechanism (text prompts). However, it doesn't distinguish from siblings since none are image-related tools, so it lacks explicit 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. It states what the tool does but offers no context about scenarios, prerequisites, or comparisons with other tools. Since siblings include unrelated tools like calculator and code_review, there's no implied usage for choosing this one.
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 of behavioral disclosure. The description only states what the tool does (greets with name and language) but doesn't disclose any behavioral traits like whether it's read-only, what the output format looks like, error handling, rate limits, or authentication needs. For a tool with no annotation coverage, this is a significant gap.
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 clearly states the tool's function. It's appropriately sized and front-loaded with the essential information. There's no wasted text or unnecessary elaboration.
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's low complexity (simple greeting function), 2 parameters with 100% schema coverage, and no output schema, the description is minimally adequate. However, without annotations or output schema, the description doesn't provide enough context about what the tool returns or its behavioral characteristics. It meets the minimum viable threshold but has clear gaps.
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%, with both parameters ('name' and 'language') fully documented in the schema. The description adds no additional parameter semantics beyond what's already in the schema (it just repeats that it takes name and language). According to the rules, when schema coverage is high (>80%), the baseline is 3 even with no param info in the description.
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's purpose: '사용자의 이름과 언어를 입력받아 해당 언어로 인사하는 도구' (A tool that receives a user's name and language and greets in that language). It specifies the verb ('인사하는' - greets) and resources (name, language), but doesn't differentiate from sibling tools like 'calculator' or 'current_time' which serve completely different purposes, so it's not a perfect 5.
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 any specific contexts, prerequisites, or exclusions. While the purpose is clear, there's no explicit when/when-not usage advice, leaving the agent to infer based on the tool's name and description alone.
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 describes the default behavior (uses Asia/Seoul if no timezone is given) and implies it's a read-only query operation. However, it doesn't disclose other behavioral traits such as error handling for invalid timezones, rate limits, authentication needs, or the format of the returned time. For a tool with no annotations, this leaves gaps in understanding its full behavior.
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 appropriately sized and front-loaded: it states the core purpose in the first clause and adds the default behavior in the second. Both sentences earn their place by providing essential information without redundancy. The structure is clear and efficient, with no wasted words.
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's low complexity (one optional parameter) and no output schema, the description is somewhat complete but has gaps. It explains the purpose and default behavior, but without annotations or output schema, it doesn't cover error cases, return format, or other operational details. For a simple query tool, this is adequate but not fully comprehensive, aligning with a minimum viable score.
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 input schema has 100% description coverage, with the parameter 'timezone' fully documented in the schema. The description adds minimal value beyond the schema by restating the default behavior (uses Asia/Seoul if not input). Since the schema already covers this, the description doesn't provide significant additional semantic context, meeting the baseline of 3 for high schema coverage.
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's purpose: '현재 시간을 지정된 시간대에서 조회하는 도구' (a tool to query the current time in a specified timezone). It specifies the verb '조회' (query) and resource '현재 시간' (current time). However, it doesn't explicitly differentiate from sibling tools like 'greeting', which might also involve time-related functions, though the distinction is reasonably implied.
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 provides clear context for usage: '시간대를 입력하지 않으면 한국 시간대(Asia/Seoul)를 사용합니다' (if no timezone is input, uses Korean timezone Asia/Seoul). This gives guidance on when to use the parameter. However, it doesn't explicitly state when to use this tool versus alternatives like 'greeting' or other time-related tools, nor does it mention any exclusions or prerequisites.
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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