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dam2452

unpaywall-mcp

by dam2452

search_articles

Search scholarly article titles and retrieve DOI objects, scores, and matching snippets. Filter by Open Access status and use boolean operators for precise results.

Instructions

Search Unpaywall article titles and return matching DOI objects.

Search terms are whitespace-split and AND-ed by default. Modifiers inside query:

  • "quoted phrase" : words must appear as a contiguous phrase

  • OR : match either term (replaces default AND)

  • -term : exclude titles containing this term

Returns up to 50 results per page, sorted by match score. Each result contains the full DOI object, a numeric score, and an HTML snippet showing where the query matched the title.

Examples: search_articles(query="cell thermometry") search_articles(query='single "cell thermometry"', is_oa=True) search_articles(query="machine learning OR deep learning", is_oa=False, page=2)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNo
is_oaNo
queryYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

With no annotations, the description carries full burden. It explains search behavior (whitespace-split, AND by default, modifiers), pagination, sorting, and result structure (DOI object, score, snippet). It does not mention rate limits or auth, but covers core behavior well.

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 well-structured: purpose, syntax, pagination/results, examples. No unnecessary sentences. Examples are concise and illustrative. Every part adds value.

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 3 parameters, no annotations, and output schema existing, the description covers search syntax, pagination, result fields, and examples. It could mention the default value for is_oa or page limit, but overall is informative enough for an agent.

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?

Schema coverage is 0%, so description must compensate. It explains query syntax in detail (modifiers), shows is_oa filter in examples, and mentions page parameter. It does not explicitly define is_oa's default or allowed values, but examples clarify usage.

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 states 'Search Unpaywall article titles and return matching DOI objects,' clearly specifying the verb, resource, and return type. It distinguishes from sibling get_doi by focusing on search rather than single DOI retrieval.

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

Usage Guidelines4/5

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

The description details search syntax (AND, quoted phrases, OR, exclusion), pagination (up to 50 per page), and sorting by score. Examples demonstrate usage with modifiers and filters. However, it does not explicitly state when to prefer this tool over get_doi, but the purpose implies differentiation.

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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