paper-search
paper-search
학술 논문 검색 MCP 및 Skills를 위한 에이전틱 도구
설치
uv를 사용하여 paper-search CLI와 MCP 서버를 전역으로 설치할 수 있습니다:
uv tool install paper-searchRelated MCP server: mcp-openalex
구성
자격 증명과 선택적 연락처 이메일은 ~/.config/paper-search/config.toml에서 읽습니다. config.toml.example을 해당 위치에 복사하고 자리 표시자 값을 바꾸세요. [api] 값은 선택 사항입니다. 값이 없거나 비어 있으면 해당 소스는 키 없이 사용됩니다.
에이전트 통합
paper-search를 MCP 서버로 사용하려면 에이전트의 MCP 구성에 다음을 추가하세요:
{
"mcpServers": {
"paper-search": {
"command": "paper-search-mcp"
}
}
}또는 uvx를 사용하는 경우(사전 설치 없이):
{
"mcpServers": {
"paper-search": {
"command": "uvx",
"args": ["--from", "paper-search", "paper-search-mcp"]
}
}
}사용법
OpenAlex는 2026년 2월에 mailto 폴라이트 풀을 폐지했습니다. OpenAlex 키가 없으면 서비스는 429 또는 409 응답을 반환하기 전에 하루 약 100개의 무료 크레딧을 제공합니다.
Available Tools
1 toolsearch_papersA
Search academic literature across multiple sources, returning deduplicated and ranked results as JSON.
Args: query: Free-text search query, e.g. "Hamiltonian Monte Carlo protein design". sources: Comma-separated source names: semantic-scholar, arxiv, pubmed, crossref, europepmc, openreview, dblp, openalex. Defaults to all keyless free sources. max_results_per_source: Maximum number of results fetched per source. year_from: Only include papers published in or after this year. year_to: Only include papers published in or before this year.
| Name | Required | Description | Default |
|---|---|---|---|
| query | Yes | ||
| sources | No | ||
| year_to | No | ||
| year_from | No | ||
| max_results_per_source | No |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full responsibility for behavioral disclosure. It mentions 'deduplicated and ranked results', 'JSON' output, and the concept of keyless sources, which hints at API key requirements. It does not explicitly mention error handling or latency, but for a search tool, this is adequate. The behavior is sufficiently described.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is concise and well-structured: an opening one-sentence overview followed by a clean Args section. Each parameter is given a clear explanation. There is no extraneous information, and it is front-loaded with the key purpose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
With 5 parametersaren and an output schema present, the description fully explains what the tool does, what each parameter means (including defaults and examples), and the output format (JSON, deduplicated, ranked). It covers behavioral aspects like source selection and default behavior. Given the output schema exists, no further details on return structure are needed.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The schema provides only names and types (0% coverage). The description compensates fully by explaining each parameter: query (with example), sources (with list of allowed values), max_results_per_source, year_from, and year_to. It adds meaning beyond the schema, including defaults and examples, making it highly instructive for the agent.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Search academic literature across multiple sources, returning deduplicated and ranked results as JSON.' It specifies the action (search), the resource (academic literature), and the output format (JSON). Since there are no sibling tools, it doesn't need to differentiate, but it is 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.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides usage context by explaining defaults (e.g., 'Defaults to all keyless free sources') and listing sources, which helps the agent decide when to call this tool. However, it lacks explicit when-not-to-use guidance or exclusion criteria, which would elevate it to 5. Still, it clearly implies the intended use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
1 tool update
v0.1.0- First observed
search_papers
TDQS
Scored across 1 tool
Only one tool exists, so there is no possibility of confusion or overlap with other tools. The tool's purpose is clearly defined as searching academic literature.
With a single tool, naming consistency is trivially maintained. The name 'search_papers' follows a clear verb_noun pattern, which is a good convention.
A single tool for a paper search server is extremely thin. While the tool is comprehensive in its search capabilities, the server lacks any other functionality such as fetching paper details, managing libraries, or retrieving citations, making the count feel insufficient for a full-featured server.
The server only provides search functionality, which is a core feature but leaves obvious gaps. For a paper search server, one would expect at least a tool to fetch details of a specific paper, and possibly tools for saving or managing papers. The current surface is incomplete for a comprehensive academic literature tool.
Maintenance
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Academic research MCP server for paper search, citation checks, graphs, and deep research.
Rivul is a free, read-only remote MCP server for searching scholarly literature. It runs semantic search over more than 300 million paper records from a dated OpenAlex snapshot, so an agent can find papers by meaning rather than exact keywords, then open the full record for any result. Tools: - search_papers: semantic search with optional filters for publication year range, minimum citation count, open access only, topic and field. Sort by relevance, most cited, newest or oldest. - get_paper_details: the full record for one work by its OpenAlex ID: title, every author, venue, work type, topic, field, publication date, citation count, DOI and open-access links. - usage_status: the workspace's MCP usage for the current day. Results come only from the index, never from a model. Each result carries a stable OpenAlex ID so the source can be checked, and every response states the snapshot date. Useful for literature reviews, scoping reviews, finding highly cited or open-access papers on a topic, citation chasing, and checking that a cited paper exists. Free for every account, with no plans or payment. Sign in with Google through OAuth 2.1 (PKCE, dynamic client registration), or use an MCP key from rivul.ai. Endpoint: https://rivul.ai/mcp (Streamable HTTP). Setup and docs: https://rivul.ai/features/atlas
Web search API and MCP server for developers building AI agents.
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