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renyumeng1

mcp-scholar

by renyumeng1
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python

adaptive_search

Read-onlyIdempotent

Searches Google Scholar using exact keywords and automatically switches to fuzzy matching when fewer than your minimum results are found, returning filtered, sorted papers.

Instructions

自适应搜索谷歌学术，先尝试精确搜索，如果结果太少则自动切换到模糊搜索

Args:
    keywords: 搜索关键词
    count: 返回结果数量，默认为5
    min_results: 最少需要返回的结果数量，少于此数量会触发模糊搜索，默认为3
    sort_by: 排序方式，可选值:
        - "relevance": 按相关性排序（默认）
        - "citations": 按引用量排序
        - "date": 按发表日期排序（新到旧）
        - "title": 按标题字母顺序排序
    year_start: 开始年份，可选
    year_end: 结束年份，可选

Returns:
    Dict: 包含论文列表和搜索模式的字典

Input Schema

NameRequiredDescriptionDefault
countNo
sort_byNorelevance
keywordsYes
year_endNo
year_startNo
min_resultsNo

Output Schema

NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv0.2.2
    • changedOutput schema / (root)
      Previous value: -nullNew value: +{
+  "properties": {
+    "result": {
+      "additionalProperties": true,
+      "title": "Result",
+      "type": "object"
+    }
+  },
+  "required": [
+    "result"
+  ],
+  "title": "adaptive_searchOutput",
+  "type": "object"
+}
  2. First observedv1.0.0

TDQS

A4.1/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnlyHint, idempotentHint, openWorldHint), and the description adds the non-obvious adaptive fallback mechanism plus the fact that the response reports which search mode was used. It does not mention rate limiting, scraping fragility, or how the fallback broadens the query, but the core behavior is disclosed.

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 purpose sentence is front-loaded and the args list follows logically. Given zero schema descriptions, enumerating each parameter earns its place, though the Args/Returns formatting is slightly verbose for a six-parameter tool.

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

Completeness5/5

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

With an output schema present, the brief Returns note suffices, and all six input parameters are documented. For a read-only search tool, an agent has everything needed to invoke it and interpret the search-mode field.

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%, so the description carries the full burden and does: it explains every parameter, including the operational meaning of min_results, defaults for count and min_results, and all four sort_by values with their semantics. This is meaningfully more than the schema's bare titles and defaults.

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?

States a specific verb and resource: an adaptive Google Scholar search that tries precise matching first and falls back to fuzzy matching when results are sparse. The two-stage behavior is clear, but it never names the siblings it competes with (scholar_search, search_papers), so differentiation is left to inference.

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 the internal trigger condition (results below min_results switch to fuzzy), which is real usage guidance for tuning behavior. However, it gives no guidance on when to pick this over the many sibling search tools, nor any exclusions or prerequisites.

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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