MGC_MCP

Server Quality Checklist

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Latest release: v1.0.0
Disambiguation4/5
Most tools have distinct purposes: stock checking split by scope (public site vs app, regional vs nationwide) and store listing split by search method (lat/lng vs region from cache). However, 'check_mgc_stock' and 'check_mgc_stock_by_region' could be confused if users don't read descriptions carefully, and 'find_mgc_stores' vs 'list_mgc_stores_by_region' have overlapping outputs.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern in snake_case (e.g., 'check_mgc_stock', 'find_mgc_stores', 'search_mgc_menu'). The naming is predictable and easy to understand.
Tool Count5/5
With 7 tools, the set is well-scoped for the domain of checking stock and finding stores for a coffee chain. Each tool serves a clear purpose without unnecessary duplication.
Completeness4/5
The tool set covers core operations: stock checking via multiple methods, store finding, menu search, and cache refresh. Minor gaps exist, such as no tool to retrieve store details (hours, address) or update stock, but these are not essential for a read-only consumer service.
Average 3.4/5 across 7 of 7 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 1 commit 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
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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 does not disclose behavioral traits such as whether the operation is read-only, return format, authentication requirements, or any side effects. This is insufficient for a tool with no output schema.
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 very short and front-loaded, but it lacks structure. It could be more concise by avoiding redundancy with the title, and it misses important information that could be added without increasing length significantly.
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 output schema and the presence of sibling tools, the description is incomplete. It does not explain return values, ordering, or how this tool differs from 'list_mgc_stores_by_region'. More context is needed for effective use.
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 75% (3 of 4 parameters have descriptions), but the tool description adds no additional meaning beyond the schema. It does not mention the 'limit' parameter or provide context for how the parameters interact. The description fails to compensate for the missing schema description.
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 specifies the action 'Find' and the resource 'Mega MGC Coffee stores', and identifies two distinct search methods (latitude/longitude or sigungu name). This distinguishes it from sibling tools like 'check_mgc_stock' or 'list_mgc_stores_by_region'.
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 when to use the tool (when searching by location coordinates or sigungu name) but does not explicitly provide when-not-to-use guidance or contrast with sibling tools. This 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.
Behavior2/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations provided, so description carries full burden. Only mentions 'sold_out values' and 'app API' but does not disclose caching behavior, concurrency limits, read-only nature, or potential side effects. Key behavioral traits are omitted.
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?
Two sentences, no fluff, front-loaded with main function. Could be improved by structuring hints into a list, but overall efficient and clear.
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?
With 14 parameters and no output schema, description fails to explain return format or how to interpret results. Also does not clarify when this tool is preferred over sibling tools like check_mgc_stock_by_region. Information is incomplete for an AI to use confidently.
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 100%, so baseline is 3. Description adds minimal extra meaning beyond schema for itemCode, but does not enhance understanding of other parameters like regionQuery or cache options. No significant added value.
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 uses specific verb 'Check' and resource 'Mega MGC Coffee app API sold_out values across stores' and distinguishes from siblings by mentioning nationwide or region filter. It also hints at exact itemCode lookup, making purpose clear.
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?
Gives one hint ('Use itemCode for exact lookup when known') but does not explicitly state when to use this tool vs siblings like check_mgc_stock_by_region or find_mgc_stores. Implies general usage but lacks 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?
The description discloses a behavioral aspect: without MGC_STOCK_API_URL, it returns public-site evidence with unknown stock status. No annotations are provided, so the description carries the full burden, but it does not cover other behaviors like rate limits or multiple store handling.
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 consists of two short sentences, front-loading the purpose and adding a key behavioral note. Every sentence is necessary and no words are wasted.
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?
With 7 parameters (only 1 required) and no output schema, the description is too brief. It does not explain how parameters interact (e.g., storeId vs sigungu), what the return value looks like, or error conditions.
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 provides 100% description coverage for all 7 parameters. The description does not add additional meaning beyond the schema, so it meets the baseline.
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 'Check stock for a Mega MGC Coffee menu item', specifying the verb and resource. However, it does not differentiate from sibling tools like check_mgc_stock_by_region or check_mgc_stock_nationwide.
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 when to use the tool (to check stock) but provides no explicit guidance on when not to use it or alternatives. The caveat about MGC_STOCK_API_URL is useful but not sufficient.
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 must convey behavioral traits. It mentions automatic cache refresh, which is a key side effect, but omits details like potential network I/O, whether the cache is persisted, or the impact of the 'refreshStores' parameter. The disclosure is partial.
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 with two short, front-loaded sentences. Every word adds value, and there is no redundant or extraneous 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?
Despite 10 parameters and no output schema, the description is only 12 words long. It fails to provide an overview of filtering logic, parameter relationships, or expected output format, leaving the agent underinformed for effective use.
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 100%, so the baseline is 3. The description adds no additional parameter meaning beyond what is already in the schema; it does not explain how to combine parameters like regionQuery and district, nor does it clarify default behaviors.
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 action ('List stores') and specifies the source ('from the local cache') and scope ('by region'). This distinguishes it from sibling tools like check_mgc_stock (stock checking) and refresh_mgc_stores (cache refresh), making its purpose unambiguous.
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 implies use for listing cached stores by region but does not provide explicit guidance on when to use this tool versus alternatives (e.g., find_mgc_stores for non-cached search, or check_mgc_stock for stock data). No 'when-not-to-use' or exclusions 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?
Without annotations, the description carries full burden. It mentions fetching and saving to cache but omits behavioral details like whether the cache is overwritten, idempotency, or side effects. The tool likely writes data, yet no disclosure of that.
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 concise sentence that efficiently conveys the core action. It is front-loaded but could include more structure or detail without becoming verbose.
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 no output schema and 2 well-documented parameters, the description is adequate for a simple refresh. However, it lacks information on return values, success indicators, or failure behavior, which would help an agent.
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 baseline 3. The description adds no extra meaning beyond what the schema provides for 'cachePath' and 'maxStores'. It does not explain parameter semantics or defaults.
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 action ('Fetch all Mega MGC Coffee app stores and save them to the local JSON cache') with a specific verb and resource. It distinguishes from sibling tools like checking stock or finding stores, which focus on different operations.
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 for cache refreshing but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or context for selecting among siblings are provided.
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 fails to disclose any behavioral traits such as rate limits, result format, or that it is a read-only operation. Only states it searches public pages.
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 concise sentence of 13 words, front-loaded with the key purpose. It earns its place, though it could include limit detail without adding much length.
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?
The tool has no output schema and the description does not explain what the output contains (e.g., list of items, pages, or details). This leaves the agent uncertain about the return value, making it incomplete for a search tool.
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 coverage is 50% (query has description, limit does not). The description adds no additional meaning beyond the schema; it does not mention the limit parameter or provide further context for query. With low coverage, description should compensate but does not.
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 searches public Mega MGC Coffee menu pages by Korean or English text, using specific verbs and resource. It distinguishes from sibling tools which focus on stock and store operations.
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 does not provide explicit when-to-use or alternatives, but the sibling tools are clearly for stock and store purposes, making the menu search use case obvious. Slight lack of explicit guidance reduces to 4.
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. The description indicates it checks sold_out values via the app API, implying a read operation, but does not disclose caching behavior, network dependencies, or potential performance implications. The parameter descriptions somewhat compensate.
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: first states purpose, second provides examples. No redundant information, front-loaded, and efficient.
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?
With 12 parameters and no output schema, the description could elaborate on return values or behavioral details like caching. However, the schema covers parameters, and the description provides useful examples, making it adequate but not thorough.
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 is 3. The description adds value by providing usage examples (e.g., '서울 종로구') that clarify region query format and purpose, going beyond schema 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 states the tool checks sold_out values for stores matching a region query, with examples. It distinguishes from sibling tools (check_mgc_stock, check_mgc_stock_nationwide, etc.) by focusing on regional filtering.
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 use when a region query is available but does not explicitly contrast with sibling tools or provide when-not-to-use guidance. No alternatives are named.
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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