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Nehra-Amaterasu

paper-search-mcp

search_base

Search academic papers from BASE database using keywords. Returns paper metadata including title, authors, and source.

Instructions

Search academic papers from BASE (Bielefeld Academic Search Engine).

Args: query: Search query string (e.g., 'machine learning'). max_results: Maximum number of papers to return (default: 10). Returns: List of paper metadata in dictionary format.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
max_resultsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior2/5

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

No annotations are provided, and the description does not explicitly state the tool is read-only or describe any behavioral traits like idempotency, rate limits, or side effects. It only describes input and output, leaving the agent to infer safety.

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 extremely concise, consisting of a single opening sentence and a structured Args/Returns list. Every sentence serves a purpose, and the most important information (what the tool does) is front-loaded.

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 two simple parameters and the presence of an output schema, the description is fairly complete. It covers the purpose, input parameters, and return format. It does not discuss pagination or errors, but these are reasonable to omit for a simple search tool.

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

Parameters4/5

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

Despite 0% schema description coverage, the description compensates by providing clear semantics for both parameters: the query parameter includes an example ('machine learning'), and max_results is explained with its default value. This adds meaningful context beyond the bare 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 academic papers from a specific source (BASE), immediately distinguishing it from sibling search tools for other databases. The verb 'search' and resource 'academic papers from BASE' are specific and unambiguous.

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 BASE queries but provides no explicit guidance on when to use this tool versus alternatives (e.g., search_arxiv, search_pubmed). No when-not or alternative tool names are mentioned.

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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