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federicodeponte

openpaper-mcp

by federicodeponte
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Remote

Server Configuration

Describes the environment variables required to run the server.

NameRequiredDescriptionDefault
OPENPAPER_API_TOKENYesYour API token from openpaper.dev, obtained from the Authorization header after 'Bearer ' in DevTools

Instructions

Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.

This server publishes no instructions, or was last inspected before Glama recorded them.

Capabilities

Features and capabilities supported by this server

Protocol revision2025-11-25

CapabilityDetails
tools
{
  "listChanged": false
}
prompts
{
  "listChanged": false
}
resources
{
  "subscribe": false,
  "listChanged": false
}
experimental
{}

Tools

Functions exposed to the LLM to take actions

NameDescription
start_paper_generationA

Start generating an academic research paper with verified citations.

OpenPaper uses 18 specialized AI agents to search 500M+ academic sources (OpenAlex, Crossref, Semantic Scholar) and write a fully-cited paper.

Args: topic: Research topic or question (max 500 chars). Be specific for better results. Examples: "The impact of microplastics on marine ecosystems", "CRISPR applications in treating genetic disorders", "Transformer architectures in natural language processing" level: Academic level. Options: "High School", "Undergraduate", "Graduate", "PhD" pages: Target page range. Options: "5-10", "10-15", "15-20", "20-30", "30-40", "40-50", "50-60" Note: 5-30 pages = 1 credit, 30-50 = 2 credits, 50-60 = 3 credits citation_style: Citation format. Options: "APA 7th", "MLA 9th", "Chicago 17th", "Harvard", "IEEE", "Vancouver" language: Output language (e.g., "English", "Spanish", "French", "German") context: Additional context or instructions for the paper (optional, max 50,000 chars)

Returns: dict with generation_id to track progress via check_paper_status()

check_paper_statusA

Check the status and progress of a paper generation.

Args: generation_id: The ID returned by start_paper_generation()

Returns: dict with status, progress percentage, current phase, and download URLs when complete.

Status values:
- "processing": Paper is being generated. Check back in a few minutes.
- "completed": Paper is ready! Download URLs are included.
- "failed": Generation failed. Start a new one.

When completed, the response includes:
- pdf_url: Download the formatted PDF
- docx_url: Download the Word document
- zip_url: Download everything (PDF + DOCX + citations JSON)
- citations_url: Download citations in JSON format
list_my_papersA

List your previously generated papers.

Args: limit: Maximum number of papers to return (1-50)

Returns: dict with list of papers including their topics, status, and download URLs.

Prompts

Interactive templates invoked by user choice

NameDescription

No prompts

Resources

Contextual data attached and managed by the client

NameDescription

No resources

Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4/5.0

Scored across 3 tools

Disambiguation5/5

Each tool has a clearly distinct purpose: starting generation, checking status, and listing papers. No overlap or ambiguity.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern with underscores, making them predictable and easy to understand.

Tool Count4/5

With only 3 tools, the set is slightly minimal but still covers the core workflow of generating and retrieving papers.

Completeness2/5

Missing obvious operations like deleting or canceling a paper generation, which agents would likely need for full lifecycle management.

Maintenance

ActivityMaintained
ResponsivenessUnresponsive

Resources