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supuni9622

Research Intelligence MCP

by supuni9622

search_papers

Search for academic papers across Semantic Scholar, arXiv, or both. Filter results by year, field, open access, and more to find relevant literature.

Instructions

Search for academic papers across Semantic Scholar, arXiv, or both providers.

Use this tool when the user wants to:

  • discover papers about a research topic;

  • find literature related to a research question;

  • search for a known paper title;

  • find papers by keywords or an author name;

  • restrict results by publication year;

  • restrict results by academic field or arXiv category;

  • request papers with known open-access metadata;

  • compare results from Semantic Scholar and arXiv.

The tool executes selected providers concurrently, preserves successful results when another provider fails, merges duplicate papers, preserves provider provenance, and returns deterministic provider-neutral paper records.

The response includes:

  • canonical paper metadata;

  • normalized paper identifiers;

  • author and publication metadata;

  • known access URLs;

  • provider attribution;

  • pagination metadata;

  • non-fatal warnings;

  • normalized partial-provider failures.

This tool performs academic paper discovery only. It does not summarize papers, answer research questions, synthesize evidence, or generate research reports.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
sortNorelevance
limitNo
queryYes
offsetNo
year_toNo
providersNo
year_fromNo
fields_of_studyNo
open_access_onlyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
queryYesNormalized search query.
papersNoCanonical search results.
failuresNoNormalized partial provider failures.
warningsNoNon-fatal result warnings.
paginationYesPagination information.
providers_requestedYesProviders requested by the caller.
providers_succeededNoProviders that completed successfully.
Behavior5/5

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

No annotations are provided, so the description carries the full burden. It transparently discloses concurrent execution, error handling, merging duplicates, and provider provenance. It also details what the response includes, providing comprehensive behavioral context.

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 description is moderately long but well-structured with bullet points and clear sections. Every sentence adds value, though some sections could be more concise. It front-loads the core purpose and provides detailed behavioral notes.

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 the tool's complexity (9 parameters, output schema exists), the description covers key aspects: purpose, when to use, behavioral traits, and response contents. It provides sufficient context for an AI agent to select and invoke correctly, though it could detail parameter format more.

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

Parameters3/5

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

With 0% schema coverage, the description must compensate. It mentions filtering by year, field, and open access, which correspond to parameters. However, it does not explain sort, limit, offset, or providers, leaving some parameters unclear. The description adds value but is incomplete.

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 it searches for academic papers across Semantic Scholar and arXiv, with specific use cases listed. It distinguishes from sibling tools like get_paper or get_paper_citations, which focus on individual paper details or citations.

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 explicitly lists when to use the tool (e.g., discover papers on a topic, find literature, search by title/author) and what it does not do (e.g., summarize papers, answer research questions). However, it does not explicitly compare to sibling tools for when not to use it.

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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