KPIC MCP Server
Server Quality Checklist
Latest release: v1.0.1
- Disambiguation5/5
The two tools have clearly distinct purposes: search_drugs_by_name retrieves preliminary information by drug name, while get_drug_detail_by_id fetches detailed information by drug code. Their descriptions explicitly reference each other to clarify when to use which, eliminating any ambiguity.
Naming Consistency5/5Both tools follow a consistent verb_noun pattern (search_drugs_by_name and get_drug_detail_by_id), using snake_case and descriptive names that clearly indicate their functions and parameters. There are no deviations in naming style.
Tool Count2/5With only 2 tools, the server feels under-scoped for a drug information domain. While the tools cover basic search and detail retrieval, typical drug information systems might include additional operations like filtering by category, checking interactions, or updating data, making this set appear thin and limited.
Completeness2/5The tool set is severely incomplete for a drug information server. It lacks essential operations such as creating, updating, or deleting drug records, and does not cover common needs like drug interactions, dosage guidelines, or category-based searches. This will likely cause agent failures when more comprehensive tasks are required.
Average 4/5 across 2 of 2 tools scored.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 9 commits in the last 12 weeks
- Last stable release on
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
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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
- 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 usefully adds that '이름이 유사한 의약품 여러개가 나올 수 있습니다' (multiple drugs with similar names may appear), which is important behavioral context about fuzzy matching. However, it doesn't disclose other behavioral traits like rate limits, authentication needs, error conditions, or what '대략적인 정보' (approximate information) specifically entails.
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 appropriately concise with two sentences that each serve a clear purpose: the first states the tool's function, and the second provides important behavioral context and alternative guidance. It's front-loaded with the core purpose and wastes no words, though it could be slightly more structured.
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?
For a single-parameter search tool with no annotations and no output schema, the description provides adequate but incomplete context. It covers the purpose, behavioral nuance about fuzzy matching, and alternative tool, but doesn't explain what information is returned or any limitations. Given the complexity is low (one parameter), it's minimally viable but could better address output expectations.
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?
The schema description coverage is 100%, with the single parameter 'drugname' well-documented in the schema as '검색할 의약품의 이름 (영문 또는 한글)' (drug name to search, English or Korean). The description doesn't add any parameter-specific information beyond what the schema provides, so it meets 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.
Purpose4/5Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: '약학정보원에서 의약품 이름으로 대략적인 정보들을 가져옵니다' (retrieves approximate drug information by name from the Pharmaceutical Information Center). It specifies the action (retrieve), resource (drug information), and source (Pharmaceutical Information Center). However, it doesn't explicitly differentiate from its sibling tool beyond mentioning it as an alternative for detailed information.
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?
The description provides clear context for usage: it's for approximate information retrieval by drug name, with the note that similar-named drugs may appear. It explicitly mentions the alternative tool 'get_drug_detail_by_id()' for detailed information, giving good guidance on when to use each. However, it doesn't specify when NOT to use this tool 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 indicates this is a read operation (fetching information) but doesn't specify authentication requirements, rate limits, error conditions, or what constitutes 'detailed information' beyond what search_drugs_by_name provides. The description adds some context about the data source but lacks comprehensive behavioral details.
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 consists of just two sentences that are perfectly front-loaded and efficient. The first sentence establishes the core purpose, and the second provides crucial usage guidance. Every word earns its place with no redundancy or unnecessary elaboration.
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?
For a single-parameter read tool with no annotations and no output schema, the description provides adequate purpose and usage guidance but lacks details about what 'detailed information' includes, error handling, or response format. The description compensates somewhat by linking to the sibling tool, but more behavioral context would be helpful given the absence of structured metadata.
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?
With 100% schema description coverage, the schema already fully documents the single parameter. The description adds valuable context by explaining the relationship between this parameter and the sibling tool: 'search_drugs_by_name()의 결과 값에 기재된 의약품의 drug_code 값' (drug_code value from search_drugs_by_name() results). This provides practical guidance on parameter sourcing beyond what the schema alone offers.
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 specific action ('가져옵니다' - fetches/retrieves), resource ('의약품의 상세정보' - detailed drug information), and source ('약학정보원의 의약품 코드로' - using drug code from Pharmaceutical Information Service). It explicitly distinguishes from its sibling tool search_drugs_by_name by mentioning when this tool should be used instead.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines5/5Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit guidance on when to use this tool versus alternatives: 'search_drugs_by_name()로 조회한 정보가 부족할 때 사용할 수 있습니다' (can be used when information retrieved from search_drugs_by_name() is insufficient). This clearly establishes the relationship between the two tools and provides specific context for tool selection.
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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