kci-mcp-server
Server Quality Checklist
Latest release: v0.1.0
- Disambiguation5/5
Each tool targets a distinct operation: searching articles, getting article details, journal citation details, journal citation lists, and reference searching. Descriptions clearly differentiate them.
Naming Consistency5/5All tools follow the consistent pattern 'kci_<verb>_<noun>' (e.g., kci_search_articles, kci_get_article_detail) with all lowercase and underscores.
Tool Count5/55 tools appropriately cover the core functionalities of a citation index server: search, article details, journal citation details, citation lists, and reference searching. Neither too few nor too many.
Completeness4/5The tool set covers key research workflows (search, article details, citation info). A minor gap might be a dedicated journal search tool, but kci_get_journal_citations partially fills that role. Overall well-scoped.
Average 3.7/5 across 5 of 5 tools scored. Lowest: 3.1/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 4 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
Add a LICENSE file by following GitHub's guide. Once GitHub recognizes the license, the system will automatically detect it within a few hours.
If the license does not appear after some time, you can manually trigger a new scan using the MCP server admin interface.
MCP servers without a LICENSE cannot be installed.
This repository includes a README.md file.
No tool usage detected in the last 30 days. Usage tracking helps demonstrate server value.
Tip: use the "Try in Browser" feature on the server page to seed initial usage.
Add a glama.json file to provide metadata about your server.
If you are the author, simply .
If the server belongs to an organization, first add
glama.jsonto the root of your repository:{ "$schema": "https://glama.ai/mcp/schemas/server.json", "maintainers": [ "your-github-username" ] }Then . Browse examples.
Add related servers to improve discoverability.
How to sync the server with GitHub?
Servers are automatically synced at least once per day, but you can also sync manually at any time to instantly update the server profile.
To manually sync the server, click the "Sync Server" button in the MCP server admin interface.
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 provided, so the description carries full burden. It only states 'retrieves basic information' without disclosing traits like pagination, rate limits, or read-only nature. The presence of 'displayCount' parameter implies pagination, but not explained.
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?
Single sentence that effectively communicates the tool's purpose and scope. No unnecessary words, front-loaded with action verb and target resource.
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?
Adequate but incomplete: describes general functionality but lacks details on output format, pagination behavior, or handling of large result sets. For a search tool with 18 parameters, more contextual framing would help.
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%, so baseline is 3. The description mentions key parameters (title, author, journal) but adds no new semantics beyond the schema definitions. No enrichment of meaning.
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 function: searching KCI-registered articles by various fields (title, author, journal) and retrieving basic info. It implies a search-and-list capability, distinguishing it from sibling tools like kci_get_article_detail which likely provides full details on a single article.
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 explicit guidance on when to use this tool versus alternatives. The description is generic and does not specify use cases, prerequisites, or exclusions, leaving the agent to infer context from sibling names.
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 the full burden of disclosing behavioral traits. It does not mention whether the operation is read-only, destructive, or has side effects. Also missing are details on pagination, rate limits, or any constraints beyond the input schema. The description only states what the tool does, not its behavioral characteristics.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Conciseness4/5Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, well-formed sentence that covers the essential purpose. It is front-loaded and avoids unnecessary verbosity. However, it could be slightly improved by adding structure (e.g., separating search criteria from output description) without increasing length significantly.
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 tool has 7 parameters, no output schema, and no annotations, the description is incomplete. It does not explain the return format, pagination behavior, or how results correspond to input parameters. A more complete description would provide an overview of the response structure and any limitations.
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%, so the baseline is 3. The description adds context that parameters are used to search for papers whose references are retrieved, but it does not provide additional semantics beyond what the schema descriptions already specify (e.g., format constraints, default values). Thus, it meets the baseline without significant added value.
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's action ('조회합니다' - retrieves) and resource ('참고문헌 목록' - reference list) with specific search criteria (title, author, institution, year). It effectively distinguishes from sibling tools like kci_search_articles (which searches articles) and kci_get_citation_detail (which gets citation details).
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 provides no explicit guidance on when to use this tool versus alternatives. While the purpose implies its use for retrieving references, there is no mention of when not to use it, prerequisites, or comparison with sibling tools. This lack of contextual guidance reduces its helpfulness for an AI agent deciding between 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, and the description does not disclose behavioral traits such as read-only nature, side effects, or permissions. It only states what the tool retrieves, leaving behavior largely unspecified.
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 a single concise sentence in Korean. It front-loads the key parameters and purpose without any redundant or unclear phrasing.
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?
The description mentions examples of returned data but does not explain pagination, sorting, or other parameters' roles. For a tool with 11 parameters and no output schema, more detail would improve completeness.
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 description coverage is 100%, so baseline is 3. The description adds value by listing specific citation indices (영향력 지수, 즉시성 지수, 자기인용 비율 등) that are returned, which is beyond what input schema provides.
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 citation indices (impact factor, immediacy index, self-citation rate) for journals based on year and years. It distinguishes from sibling tools which focus on article details, citation details, or search.
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 implies use when needing journal citation indices by year range, but does not explicitly state when to use this tool versus alternatives or provide exclusions. No usage guidance beyond implied context.
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 clearly states this is a read-only retrieval operation (상세 조회) and lists the types of data returned. It does not mention side effects, authentication, or rate limits, but the absence is acceptable for a simple read tool. The disclosure of return content is sufficient.
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 a single sentence that front-loads the core action and then lists specific data categories. Every word adds value, and there is no redundant or off-topic content. It is highly efficient.
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?
The description covers the input (control number) and enumerates the types of output (registration, institution, change history, citation history). Given the simple schema (1 parameter, no output schema), this is largely complete. Lacking only is mention of output format or potential errors, which are minor omissions.
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 input schema already provides a description for the only parameter (id: control number). The tool description merely restates 'journal control number' without adding new semantics, format constraints, or examples. Since schema coverage is 100%, a 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 specifies the verb (상세 조회, retrieve detailed information), the resource (저널/학술지, journal), and the method (by 제어번호, control number). It lists specific data types (registration info, citation history) that clearly differentiate it from sibling tools like kci_get_article_detail or kci_get_journal_citations.
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 implies usage when the user has a journal control number and needs detailed citation and registration data. However, it does not provide explicit guidance on when not to use it or how it compares to siblings (e.g., ‘for article-level data, use kci_get_article_detail’). The context is clear but lacks exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
- Behavior5/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description fully compensates by stating that it reads data (조회) and listing returned fields like author affiliations, URL, DOI, etc. There is no contradiction.
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 that front-load the purpose, provide a concrete example, and explain how to obtain the required input. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple single-parameter tool and no output schema, the description fully covers the needed context: what the input is, how to get it, and what information will be returned.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Parameters5/5Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The description adds significant meaning beyond the schema: it explains the parameter as 'KCI 논문 제어번호' with an example, and crucially tells the agent that the value should come from another tool's result. Schema coverage is 100% but the description enriches it.
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's purpose: retrieve article details from KCI using a control number, listing specific fields. It differentiates from siblings by specifying the input (control number) and output (detailed info), and explains the relationship with kci_search_articles.
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 explicitly indicates when to use the tool (after obtaining a control number from kci_search_articles) and provides context for the input. However, it does not explicitly state when not to use it or mention alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
GitHub Badge
Glama performs regular codebase and documentation scans to:
- Confirm that the MCP server is working as expected.
- Confirm that there are no obvious security issues.
- Evaluate tool definition quality.
Our badge communicates server capabilities, safety, and installation instructions.
Card Badge
Copy to your README.md:
Score Badge
Copy to your README.md:
Latest Blog Posts
- Who's Calling? MCP Hosts Are an Identity Blind Spot (And the Spec Knows It)By Om-Shree-0709 on .mcpAgent IdentityOAuth 2.1
- Your AI Chatbot Just Exposed Your CEO's Salary to an InternBy Om-Shree-0709 on .Agent IdentityMCP SecurityOAuth Delegation
- Why MCP Servers Need Execution Sandboxing (And Why Your Current Stack Isn't Enough)By Om-Shree-0709 on .Agentic AiPrompt InjectionWebAssembly
MCP directory API
We provide all the information about MCP servers via our MCP API.
curl -X GET 'https://glama.ai/api/mcp/v1/servers/iapke486-arch/mcp-server'
If you have feedback or need assistance with the MCP directory API, please join our Discord server