Europe PMC Literature Search MCP Server

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 clearly explains the two-stage behavior: first checking local cache (journal_info.json), then optionally calling the EasyScholar API if a secret key is provided. It also describes the fallback mechanism when data isn't available locally. However, it doesn't mention potential rate limits, error handling beyond the error field, or authentication requirements for the API.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (功能说明, 参数说明, 返回值说明, 使用场景), but contains some redundancy. The initial statement '获取期刊质量评估信息（影响因子、分区等）' is partially repeated in the functional description. The return value section is quite detailed but could be more concise given that an output schema exists.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's moderate complexity (2 parameters, caching+API behavior), no annotations, but with an output schema, the description provides good completeness. It explains the dual data source approach, parameter purposes, and usage scenarios. The main gap is lack of information about error conditions beyond the error field, and no mention of what happens when neither local cache nor API provides data.

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

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

With 0% schema description coverage, the description must compensate. It provides excellent parameter semantics: 'journal_name: 必需，期刊名称' (required, journal name) and 'secret_key: 可选，EasyScholar API密钥（可从环境变量EASYSCHOLAR_SECRET_KEY获取）' (optional, EasyScholar API key - can be obtained from environment variable EASYSCHOLAR_SECRET_KEY). This adds crucial meaning beyond the bare schema, explaining purpose, requirements, and even environment variable sourcing.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose as '获取期刊质量评估信息（影响因子、分区等）' (get journal quality assessment information - impact factor, quartile, etc.), which is a specific verb+resource combination. It distinguishes itself from sibling tools like 'evaluate_articles_quality' (which evaluates articles) and 'get_article_details' (which gets article details) by focusing specifically on journal-level quality metrics.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description explicitly provides usage scenarios in the '使用场景' section: '评估期刊质量' (evaluate journal quality), '选择投稿期刊' (select journals for submission), and '文献质量评估' (literature quality assessment). These give clear guidance on when to use this tool versus alternatives like article-focused tools or search tools.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.