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cholhwanjung

Claude Desktop Research MCP Server

by cholhwanjung

get_references_by_citations

Retrieves and sorts references cited by a paper, ordered by citation count or velocity, to identify the most influential works.

Instructions

논문이 참조(reference)한 논문들을 정렬해 반환합니다.

Args: paper_id: arXiv ID (예: "2301.12597"), DOI, 또는 Semantic Scholar ID. top_k: 반환할 상위 논문 수 (기본 20). max_fetch: 최대 수집 reference 수 (기본 500). 대부분 논문은 references가 100 이내라 한 번에 다 가져온다. sort: "velocity"(기본, ADR-004) 또는 "count"(인용수 순). current_year: velocity 계산 기준 연도. None이면 현재. min_velocity: velocity 모드일 때 최소 velocity 임계값 (기본 10). 신생 인용수 낮은 노이즈 논문을 제거한다.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
paper_idYes
top_kNo
max_fetchNo
sortNovelocity
current_yearNo
min_velocityNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It discloses sorting modes (velocity with ADR-004 reference), threshold for noise removal (min_velocity), and typical reference counts (most papers < 100). This provides good behavioral insight beyond the schema.

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 well-structured with an args list, but slightly verbose with repeated default indicators. It could be more compact while retaining clarity. However, it remains efficient and front-loaded with the main purpose.

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?

Given the output schema exists, the description covers all input parameters and key behavioral traits. It explains velocity computation and practical limits. The absence of error handling or return format details is acceptable given output schema, but some context on expected output could elevate completeness.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, but the description adds extensive detail for all 6 parameters: paper_id formats (arXiv, DOI, Semantic Scholar ID), defaults, sort options (velocity vs count), and the significance of min_velocity. This far exceeds baseline compensation.

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 returns sorted papers referenced by a given paper. It uses a specific verb ('returns') and resource ('referenced papers'), and is distinct from sibling tools like get_citations_by_citations which likely returns citing papers.

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 explains parameters and behavior but does not explicitly state when to use this tool versus alternatives like get_citations_by_citations or search_papers. Usage context is implied but not directly addressed with when-not or alternative suggestions.

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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