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TaehongKim

classScopusMCP

by TaehongKim
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

Server Quality Checklist

58%
Profile completionA complete profile improves this server's visibility in search results.

  • Latest release: v1.0.0

  • Disambiguation5/5

    The two tools are clearly distinct: one searches by keyword, the other retrieves a specific paper by DOI. There is no ambiguity in purpose, as each targets a different lookup method.

    Naming Consistency5/5

    Both tool names follow a consistent verb_noun pattern with snake_case: 'search_papers' and 'get_abstract_by_doi'. The naming is predictable and descriptive.

    Tool Count4/5

    While only two tools are provided, they are well-scoped for the stated purpose of searching papers and fetching abstracts by DOI. The count is slightly lean but not unreasonable for a focused utility.

    Completeness4/5

    The tool set covers the essential operations for finding papers and obtaining abstracts. Minor gaps exist, such as lack of citation data or full metadata retrieval, but the core workflow is complete.

  • Average 3.5/5 across 2 of 2 tools scored.

    See the Tool Scores section below for per-tool breakdowns.

    • No community issues in the last 6 months
    • 0 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

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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?

    With no annotations provided, the description carries the full burden of behavioral disclosure. It only states that it fetches an abstract, but does not disclose error handling, return format, rate limits, or access requirements. This is a significant transparency gap for a tool with no other safety metadata.

    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, concise sentence with no redundant words. It is front-loaded with the core function and contains zero filler.

    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?

    For a simple single-parameter fetch tool, the description is largely complete: it specifies the input (DOI) and implies the output (abstract). However, it omits details on error cases (e.g., invalid DOI) or what to do if no abstract exists. Given the tool's simplicity, this is a minor gap.

    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 already describes the 'doi' parameter with 100% coverage, so the baseline is 3. The description simply says 'by DOI', which adds no new semantic meaning beyond the schema. No additional detail about the parameter format or requirements is provided.

    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 uses a specific verb ('가져옵니다' - fetches) and clearly specifies the resource ('초록' - abstract) and the identifier ('DOI'). It distinguishes itself from the sibling tool 'search_papers' by focusing on retrieval by DOI rather than searching.

    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 like 'search_papers'. There is no mention of prerequisites (e.g., having a DOI) or exclusions. The implied usage is 'when you have a DOI', but this is not explicit and no alternative is named.

    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. It states the core search-and-abstract function but omits details like output format, result ordering, pagination, or access restrictions. For a read-only search tool, this minimal description offers limited insight.

    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, clear sentence that immediately conveys the tool's purpose. It contains no redundant information or filler.

    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?

    The tool has no output schema and no annotations, so the description must cover return behavior. It partially does by mentioning 'abstracts', but it does not describe the full result structure, count behavior, or possible errors. Given the tool's simplicity, this is a moderate gap.

    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% with descriptions for both 'query' and 'count', so the baseline is 3. The tool description merely says 'keyword' and adds no extra semantic detail, such as how 'count' affects results or query syntax, thus failing to add value beyond the 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 searches for papers by keyword and retrieves abstracts. This distinguishes it from the sibling tool get_abstract_by_doi, which operates by DOI, so the purpose is well-defined.

    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 keyword-based search but does not explicitly state when to use this tool versus the DOI-based sibling, nor does it mention any exclusions or alternative scenarios. Usage context is only inferred from the keyword parameter.

    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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