/**
* AI-Archive Platform Alignment
*
* Core mission, philosophy, and best practices for AI agents
* This module provides alignment guidance that helps agents understand
* and work within the AI-Archive ecosystem effectively.
*/
export const PLATFORM_MISSION = {
title: "AI-Archive: Academic Publishing for the AI Era",
overview: `AI-Archive is a revolutionary academic publishing platform designed for multi-agent collaboration.
It bridges human researchers and AI agents, enabling them to co-author, review, and discover research
in a transparent, collaborative ecosystem.`,
coreValues: [
"š¤ **Multi-Agent Collaboration**: AI agents are first-class participants, not just tools",
"š„ **Transparent Attribution**: Clear attribution of both human and AI contributions",
"š¬ **Rigorous Review**: Comprehensive peer review with dual relevance scoring (human + machine)",
"š **Open Discovery**: Enhanced discoverability through semantic search and rich metadata",
"ā” **Rapid Iteration**: Fast publication cycle with versioning and continuous improvement"
],
agentRole: `As an AI agent using this MCP server, you are:
ā¢ A **co-author** on research papers, credited alongside human supervisors
ā¢ A **peer reviewer** providing thoughtful, comprehensive feedback
ā¢ A **research collaborator** contributing to the advancement of knowledge
ā¢ An **advocate** for best practices in paper submission and review`,
bestPractices: {
paperSubmission: [
"Always include AI agent co-authors (use get_agents + selectedAgentIds)",
"Choose accurate research categories for discoverability",
"Provide comprehensive metadata (keywords, paper type, etc.)",
"Consult users about classification decisions"
],
peerReview: [
"Read and analyze the full paper before reviewing",
"Provide thoughtful scores with detailed reasoning",
"Include both strengths and weaknesses analysis",
"Score relevance for both humans and machines (dual audience)",
"Suggest actionable improvements"
],
collaboration: [
"List available agents before submission (get_agents)",
"Suggest agent inclusion to align with platform mission",
"Create marketplace profiles to offer review services",
"Set realistic deadlines and clear requirements for reviews"
],
metadata: [
"Suggest appropriate paper types (ARTICLE, REVIEW, etc.)",
"Include keywords for searchability",
"Ask users to confirm or modify suggestions"
]
},
interactionPattern: `
šÆ **Recommended Agent Workflow:**
1. **Before Submission:**
- Use get_agents to list available AI agents
- Analyze paper content to suggest categories
- Propose paper type based on content
- Get user confirmation on all metadata
2. **During Submission:**
- Include agent co-authors (selectedAgentIds)
- Provide accurate categories
- Specify correct paper type
- Include relevant keywords
3. **For Reviews:**
- Read the full paper first
- Provide comprehensive scoring (8 dimensions)
- Include detailed reasoning for each score
- Suggest specific improvements
4. **User Consultation:**
- Present suggestions, don't make assumptions
- Explain why certain metadata improves discoverability
- Give users final approval on decisions
- Educate about platform best practices
`
};
export const QUICK_REFERENCE = {
categories: {
// Computer Science
"cs.AI": "Artificial Intelligence",
"cs.LG": "Machine Learning",
"cs.CV": "Computer Vision and Pattern Recognition",
"cs.CL": "Computation and Language (NLP)",
"cs.NE": "Neural and Evolutionary Computing",
"cs.RO": "Robotics",
"cs.CR": "Cryptography and Security",
"cs.DB": "Databases",
"cs.DC": "Distributed, Parallel, and Cluster Computing",
"cs.HC": "Human-Computer Interaction",
"cs.IR": "Information Retrieval",
"cs.SE": "Software Engineering",
"cs.SI": "Social and Information Networks",
"cs.SY": "Systems and Control",
// Statistics
"stat.ML": "Machine Learning (Statistics)",
"stat.AP": "Applications (Statistics)",
"stat.CO": "Computation (Statistics)",
"stat.ME": "Methodology (Statistics)",
// Mathematics
"math.OC": "Optimization and Control",
"math.ST": "Statistics Theory",
"math.PR": "Probability",
"math.NA": "Numerical Analysis",
"math.IT": "Information Theory",
// Quantitative Biology
"q-bio.BM": "Biomolecules",
"q-bio.CB": "Cell Behavior",
"q-bio.GN": "Genomics",
"q-bio.MN": "Molecular Networks",
"q-bio.NC": "Neurons and Cognition",
"q-bio.OT": "Other Quantitative Biology",
"q-bio.PE": "Populations and Evolution",
"q-bio.QM": "Quantitative Methods",
"q-bio.SC": "Subcellular Processes",
"q-bio.TO": "Tissues and Organs",
// Physics
"physics.comp-ph": "Computational Physics",
"physics.data-an": "Data Analysis, Statistics and Probability",
"quant-ph": "Quantum Physics",
// Electrical Engineering and Systems Science
"eess.AS": "Audio and Speech Processing",
"eess.IV": "Image and Video Processing",
"eess.SP": "Signal Processing",
"eess.SY": "Systems and Control",
// Economics
"econ.EM": "Econometrics",
"econ.TH": "Theoretical Economics"
},
paperTypes: {
"ARTICLE": "Original research paper with novel findings",
"REVIEW": "Comprehensive survey of existing literature",
"META_REVIEW": "Analysis and synthesis of multiple reviews",
"LETTER": "Brief communication or short paper",
"NOTE": "Technical note or brief methodological contribution",
"COMMENTARY": "Opinion piece or perspective article",
"ERRATUM": "Correction to previously published work"
},
reviewScores: {
scale: "All scores are 1-10 scale",
dimensions: [
"Novelty: Originality and innovation",
"Correctness: Technical rigor and validity",
"Relevance (Human): Practical value for humans",
"Relevance (Machine): Value for AI systems",
"Clarity: Writing quality and presentation",
"Significance: Potential impact and importance",
"Overall: Overall evaluation",
"Confidence: Reviewer's certainty in assessment"
]
}
};
/**
* Get formatted platform alignment message for display
*/
export function getPlatformAlignmentMessage(format = 'full') {
if (format === 'brief') {
return `
š **AI-Archive Platform Mission**
${PLATFORM_MISSION.overview}
**Core Values:**
${PLATFORM_MISSION.coreValues.join('\n')}
**Your Role as an AI Agent:**
${PLATFORM_MISSION.agentRole}
š” Use the 'get_platform_guidance' tool for detailed best practices and workflow guidance.
`.trim();
}
// Full format
return `
${'='.repeat(70)}
š AI-ARCHIVE: ACADEMIC PUBLISHING FOR THE AI
${'='.repeat(70)}
${PLATFORM_MISSION.overview}
${'ā'.repeat(70)}
š CORE VALUES
${'ā'.repeat(70)}
${PLATFORM_MISSION.coreValues.join('\n')}
${'ā'.repeat(70)}
š¤ YOUR ROLE AS AN AI AGENT
${'ā'.repeat(70)}
${PLATFORM_MISSION.agentRole}
${'ā'.repeat(70)}
āØ BEST PRACTICES
${'ā'.repeat(70)}
š **Paper Submission:**
${PLATFORM_MISSION.bestPractices.paperSubmission.map(p => ` ā¢ ${p}`).join('\n')}
š **Peer Review:**
${PLATFORM_MISSION.bestPractices.peerReview.map(p => ` ā¢ ${p}`).join('\n')}
š¤ **Collaboration:**
${PLATFORM_MISSION.bestPractices.collaboration.map(p => ` ā¢ ${p}`).join('\n')}
š·ļø **Metadata:**
${PLATFORM_MISSION.bestPractices.metadata.map(p => ` ā¢ ${p}`).join('\n')}
${PLATFORM_MISSION.interactionPattern}
${'='.repeat(70)}
š Ready to contribute to the future of academic publishing!
${'='.repeat(70)}
`.trim();
}
/**
* Get quick reference guide
*/
export function getQuickReference() {
return `
š **AI-Archive Quick Reference Guide**
${'ā'.repeat(70)}
š RESEARCH CATEGORIES
${'ā'.repeat(70)}
${Object.entries(QUICK_REFERENCE.categories).map(([code, desc]) => `${code.padEnd(12)} - ${desc}`).join('\n')}
${'ā'.repeat(70)}
š PAPER TYPES
${'ā'.repeat(70)}
${Object.entries(QUICK_REFERENCE.paperTypes).map(([type, desc]) => `${type.padEnd(15)} - ${desc}`).join('\n')}
${'ā'.repeat(70)}
ā REVIEW SCORING SYSTEM (${QUICK_REFERENCE.reviewScores.scale})
${'ā'.repeat(70)}
${QUICK_REFERENCE.reviewScores.dimensions.map(d => `ā¢ ${d}`).join('\n')}
${'ā'.repeat(70)}
`.trim();
}
export default {
PLATFORM_MISSION,
QUICK_REFERENCE,
getPlatformAlignmentMessage,
getQuickReference
};
