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paper-search

paper-search

by ChenchaoZhao

paper-search

Agentische Werkzeuge für die akademische Literatursuche als MCP- und Skills-Integration

Installation

Sie können die paper-search-CLI und den MCP-Server global mit uv installieren:

uv tool install paper-search

Related MCP server: mcp-openalex

Konfiguration

Anmeldedaten und die optionale Kontakt-E-Mail werden aus ~/.config/paper-search/config.toml gelesen. Kopieren Sie config.toml.example an diesen Speicherort und ersetzen Sie die Platzhalterwerte. Die [api]-Werte sind optional; fehlende oder leere Werte lassen die entsprechende Quelle ohne Schlüssel.

Agent-Integration

Um paper-search als MCP-Server zu verwenden, fügen Sie dies zur MCP-Konfiguration Ihres Agents hinzu:

{
  "mcpServers": {
    "paper-search": {
      "command": "paper-search-mcp"
    }
  }
}

Oder mit uvx (ohne vorherige Installation):

{
  "mcpServers": {
    "paper-search": {
      "command": "uvx",
      "args": ["--from", "paper-search", "paper-search-mcp"]
    }
  }
}

Verwendung

OpenAlex hat seinen mailto-Polite-Pool im Februar 2026 eingestellt. Ohne einen OpenAlex-Schlüssel bietet der Dienst etwa 100 kostenlose Credits pro Tag, bevor er 429- oder 409-Antworten zurückgibt.

Available Tools

1 tool
search_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.

NameRequiredDescriptionDefault
queryYes
sourcesNo
year_toNo
year_fromNo
max_results_per_sourceNo

Output Schema

NameRequiredDescription
resultYes

TDQS

A4.6/5.0
Behavior4/5

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.

Conciseness5/5

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.

Completeness5/5

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.

Parameters5/5

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.

Purpose5/5

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.

Usage Guidelines4/5

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. 1 tool updatev0.1.0
    • First observedsearch_papers

TDQS

A4.2/5.0

Scored across 1 tool

Disambiguation5/5

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.

Naming Consistency4/5

With a single tool, naming consistency is trivially maintained. The name 'search_papers' follows a clear verb_noun pattern, which is a good convention.

Tool Count2/5

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.

Completeness2/5

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

ActivityStale
ResponsivenessNo issues

Related MCP Connectors

  • Academic research MCP server for paper search, citation checks, graphs, and deep research.

    https://doxa.papersflow.ai/mcp
  • 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

    https://rivul.ai/mcp
  • Web search API and MCP server for developers building AI agents.

    https://searchpipe.tech/mcp/
  • Public MCP server for the LLM Search Engine

    https://llmse.ai/mcp

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