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h-lu

CNKI MCP Server

by h-lu

search_cnki

Search academic papers from China National Knowledge Infrastructure (CNKI) by query, author, title, DOI, or other criteria to retrieve relevant research articles with metadata.

Instructions

搜索 CNKI 论文,返回论文列表。

Args: query: 搜索关键词 ctx: MCP 上下文(自动注入) search_type: 搜索类型,支持: - 中文:主题、关键词、作者、篇名、作者单位、全文、DOI、基金、摘要 - 英文:subject, keyword, author, title, affiliation, fulltext, doi pages: 搜索页数(1-10),每页约20条结果 sort: 排序方式,支持: - 中文:相关度、发表时间、被引、下载、综合 - 英文:relevance, date, cited, download, composite

Returns: 包含论文列表的字典,每篇论文包含:title, url, authors, source, date, cited_count

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes搜索关键词(必填)
search_typeNo搜索类型: 主题、关键词、作者、篇名、作者单位、全文、DOI、基金、摘要主题
pagesNo搜索页数(每页约20条结果)
sortNo排序方式: 相关度、发表时间、被引、下载、综合 (英文: relevance, date, cited, download, composite)相关度

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

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 mentions that pages parameter has a range (1-10) and each page returns about 20 results, which adds some behavioral context. However, it doesn't cover important aspects like rate limits, authentication requirements, error conditions, or whether this is a read-only operation. For a search tool with no annotation coverage, this is insufficient.

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 clear sections (Args, Returns) and uses bullet points for enumerations. It's appropriately sized for a tool with 4 parameters. However, the first sentence could be more front-loaded with key information, and some parameter details are redundant with the schema.

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 that there's an output schema (the Returns section describes the response structure), the description doesn't need to explain return values in detail. It covers the basic purpose and parameters adequately. However, for a search tool with no annotations, it could provide more context about limitations, performance characteristics, or error handling.

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 schema already documents all parameters thoroughly. The description repeats some parameter information (search_type options, pages range, sort options) but doesn't add significant meaning beyond what's in the schema. The baseline of 3 is appropriate when the schema does the heavy lifting, though the description does provide some additional context about page size ('每页约20条结果').

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 the tool's purpose: '搜索 CNKI 论文,返回论文列表' (Search CNKI papers, return paper list). It specifies the verb ('搜索' - search) and resource ('CNKI 论文' - CNKI papers). However, it doesn't explicitly differentiate from sibling tools like 'find_best_match' or 'get_paper_detail', which prevents a perfect score.

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 its siblings ('find_best_match' and 'get_paper_detail'). It doesn't mention any prerequisites, exclusions, or alternative scenarios. The only usage context is implied by the tool name and description, but no explicit guidelines are given.

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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