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h-lu

Paper Search MCP Server

by h-lu

search_semantic

Search academic papers across all disciplines with citation metrics and year filters. Retrieve paper details, abstracts, and open-access PDF links from Semantic Scholar's index of 200M+ papers.

Instructions

Search papers on Semantic Scholar - general-purpose academic search engine.

USE THIS TOOL WHEN:
- You want to search across ALL academic disciplines
- You need citation counts and influence metrics
- You want to filter by publication year
- You need open-access PDF links when available

COVERAGE: ALL academic fields - sciences, humanities, medicine, etc.
Indexes 200M+ papers from journals, conferences, and preprints.

WORKFLOW:
1. search_semantic(query) -> get paper_id or DOI
2. download_semantic(paper_id) -> get PDF (if open-access)
3. If no PDF: use download_scihub(doi) for older papers

Args:
    query: Search terms (any topic, any field).
    year: Optional year filter: '2023', '2020-2023', '2020-', '-2019'.
    max_results: Number of results (default: 10).

Returns:
    List of paper dicts with: paper_id, title, authors, abstract,
    published_date, doi, citations, url, pdf_url (if available).

Example:
    search_semantic("climate change impact agriculture", year="2020-", max_results=5)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYes
yearNo
max_resultsNo
Behavior4/5

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

With no annotations, the description carries full burden. It conveys read-only search behavior implicitly through 'Search papers' and return format. It lacks explicit statements about safety (e.g., no side effects) but is clear enough. Adding 'This tool does not modify any data' would justify a 5.

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 with headers (USE THIS TOOL WHEN, COVERAGE, WORKFLOW, Args, Returns, Example). It is front-loaded with purpose, each section is concise and value-adding, with no redundant sentences.

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?

Given the tool has 3 params, no output schema, and many sibling tools, the description covers purpose, usage guidance, parameters with examples, return format, and integration workflow. It is complete enough for an agent to select and use correctly.

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 must compensate fully. It explains 'query' as 'any topic, any field', 'year' with format examples like '2020-2023', and 'max_results' with default value. This adds crucial meaning beyond the bare schema types.

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 explicitly states 'Search papers on Semantic Scholar - general-purpose academic search engine.' It contrasts with sibling tools by emphasizing cross-discipline coverage ('ALL academic fields'), distinguishing it from domain-specific searches like search_arxiv or search_pubmed.

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

Usage Guidelines5/5

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

The 'USE THIS TOOL WHEN' section lists specific use cases (cross-discipline search, citation metrics, year filtering, open-access PDFs). It also provides a workflow linking to download_semantic and download_scihub, giving clear decision criteria versus sibling download tools.

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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