{ "mcpServers": { "prismaid-mcp": { "command": "docker", "args": [ "run", "-i", "--rm", "ghcr.io/open-and-sustainable/prismaid-mcp:0.17.1" ] } } }

prismAId

Name: prismAId MCP
Author: open-and-sustainable

Open Science AI Tools for Systematic, Protocol-Based Literature Reviews

prismAId offers a suite of tools using generative AI models to streamline systematic reviews of scientific literature.

It provides simple-to-use, efficient, and replicable methods for screening and analyzing research papers with no coding skills required.

Toolkit Overview

prismAId offers a comprehensive set of tools for systematic literature reviews:

Core Tools

Screening - Filter and tag manuscripts to identify items for exclusion
Download - Download papers from Zotero collections or from URL lists
Convert - Convert files (PDF, DOCX, HTML) to plain text for analysis
Review - Process systematic literature reviews based on TOML configurations
RevAIse documentation support - Optionally document review stages as RevAIse review records

Workflow

Our tools support a comprehensive systematic review workflow following the standard sequence: Search → Screen → Download → Convert → Review. RevAIse support can document Zotero download, screening, and review/extraction stages in one cumulative review record.

Access Methods

AI agents via the MCP server - A main entry point: connect an AI assistant to the prismAId MCP server and drive every tool in conversation
Command Line Interface - For users who prefer terminal-based workflows
Web Initializer - A browser-based setup tool for configuring reviews
Programming Libraries - API access through multiple languages:
- Go (native implementation)
- Python package
- R package
- Julia package

Specifications

Review protocol: Supports any literature review protocol with a preference for PRISMA 2020, which inspired our project name.
Review documentation: Optional RevAIse review-record support with cumulative updates and automatic backups; see the RevAIse integration guide.
Protocol conformance: Check RevAIse review records against reporting protocols such as PRISMA 2020, and get a protocol's full requirement checklist, using the SHACL shapes published by RevAIse; see the conformance and guidance docs.
Distribution: Available as:
- Go package
- Python package
- R package
- Julia package
- 'no-coding' binaries for Windows, MacOS, and Linux (AMD64/ARM64)
Supported LLMs:
1. OpenAI: GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o, GPT-4o Mini, GPT-4.1, GPT-4.1 Mini, GPT-4.1 Nano, GPT-5, GPT-5.1, GPT-5.2, GPT-5 Mini, GPT-5 Nano, o1, o1 Mini, o3, o3 Mini, and o4 Mini
2. GoogleAI: Gemini 1.5 Pro, Gemini 1.5 Flash, Gemini 2.0 Flash, Gemini 2.0 Flash Lite, Gemini 2.5 Pro, Gemini 2.5 Flash, Gemini 2.5 Flash Lite, Gemini 3 Pro Preview, and Gemini 3 Flash Preview
3. Cohere: Command, Command Light, Command R, Command R+, Command R7B, Command R (August 2024), Command A, and Command A Reasoning
4. Anthropic: Claude 3 Sonnet, Claude 3 Opus, Claude 3 Haiku, Claude 3.5 Haiku, Claude 3.5 Sonnet, Claude 3.7 Sonnet, Claude 4.0 Sonnet, Claude 4.0 Opus, Claude 4.5 Opus, Claude 4.5 Sonnet, and Claude 4.5 Haiku
5. DeepSeek: DeepSeek Chat v3, and DeepSeek Reasoner v3
6. Perplexity: Sonar, Sonar Pro, Sonar Reasoning Pro, and Sonar Deep Research
7. Cloud Providers: AWS Bedrock, Azure AI, Vertex AI
8. Self-Hosted: OpenAI-compatible endpoints
Screening capabilities: Deduplication, language filtering, article type classification, and off-topic detection
Output format: Data in CSV or JSON formats
Performance: Efficiently processes extensive datasets with minimal setup and no coding required
Programming Language: Core implementation in Go with bindings for Python, R, and Julia

Documentation

All information on installation, usage, and development is available at prismaid.review and in the prismAId User Manual.

Credits

Authors

Riccardo Boero - ribo@nilu.no

Acknowledgments

This project was initiated with the generous support of a SIS internal project from NILU. Their support was crucial in starting this research and development effort. Further, acknowledgment is due for the research credits received from the OpenAI Researcher Access Program and the Cohere For AI Research Grant Program, both of which have significantly contributed to the advancement of this work.