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Model Context Protocol server for creating and managing Paper2Agent instances. Allows programmatic creation of AI agents from research paper repositories.
This MCP server has severe definition quality issues across nearly all 19 tools. Most tools lack visible input schemas, descriptions are generic or missing, and parameter documentation is absent. The server appears to be a collection of agent-creation tools with minimal MCP-specific schema definition. No input parameter schemas are visible in the provided source code for 18 of 19 tools. Only 'archival_system' has a partially visible schema. Tool names generally follow verb_noun convention (positive), but descriptions are extremely brief and lack the context needed for LLM-driven tool selection. The codebase shows this is still in early development, tools are listed in test-tools.js but not formally registered with full schemas in the visible source.
Analyze and generate research budgets for AI/ML projects. Supports grant proposals and investor decks with detailed cost breakdowns, ROI projections, and best practices guidance.
Archive and preserve digital assets with versioning, access control, and long-term integrity verification.
Check compliance against regulations, standards, and internal policies. Identifies gaps and provides remediation guidance.
Create a new Paper2Agent from a research repository. This will clone the repo, scan tutorials, extract tools, and create an MCP server. Estimated time: 30min-3hrs.
Analyze a research paper and design a new experiment based on its limitations. Extracts metadata, identifies future work, designs experimental study, and creates technical implementation plan.
Generate a Google Colab notebook for The Administrator - Agency Operations System for organizational workflows, task management, and team coordination.
18 of 19 tools have no visible input parameter schemas in source code. Only archival_system, ciso-agent, and llm-rubric-architect show partial schemas.
Tool descriptions are extremely brief (15-45 characters) and lack context about when to use the tool, what it returns, or how it differs from similar tools. Descriptions must be 50-200 characters for LLM disambiguation.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 0 | 2026-07-28+ | v2 |
| 2026-03-09 | F | 38 | 2024-11-05+ | v1 |
Generate a Google Colab notebook for CISO Agent - security assessment, compliance checking, vulnerability analysis, and security best practices documentation.
Generate a Google Colab notebook for The Comptroller - COO Algorithm for resource optimization. Implements Iron Triangle optimization (Speed ⟷ Cost ⟷ Quality) with constraint programming, market search, and burn rate analysis. A2A protocol compliant.
Generate a Google Colab notebook for Creative Director Agent - content generation, creative planning, multi-modal asset creation, and brand consistency management.
Generate a Google Colab notebook for Dataset Builder Agent - automated dataset creation, preprocessing, validation, and version control for ML pipelines.
Generate a Google Colab notebook for Forensic Analyst Agent - incident investigation, root cause analysis, evidence collection, timeline reconstruction, and detailed forensic reports.
Generate a complete LLM-Rubric evaluation pipeline for a phenomenon. Creates JSON schema, Python runtime, dashboard specs, and prompt templates based on Perspectivist Safety methodology.
Generate a Google Colab notebook for MLFlow Agent - ML experiment tracking, model management, and deployment pipeline orchestration with integrations.
Generate a Google Colab notebook for Orchestrator - workflow orchestration, agent coordination, task scheduling, and execution monitoring for multi-agent systems.
Generate a Google Colab notebook for Visual Inspector Agent - computer vision analysis, image quality assessment, visual anomaly detection, and visual content verification.
Get detailed information about a specific Paper2Agent including tools, tutorials, and MCP server path.
Get the current status of the Paper2Agent pipeline for a project. Shows which steps are completed and overall progress.
Analyze intellectual property landscape, patent analytics, competitive intelligence, and innovation tracking.
Launch a Paper2Agent by installing its MCP server in Claude Code and opening Claude.
List all Paper2Agent projects with their status, tool counts, and configuration.
No parameter descriptions visible for most tools. Parameters like 'task_description', 'quality_dimensions', 'action' in archival_system have descriptions, but 16+ tools show no parameter documentation at all in the tool listing.
No output schemas documented. LLMs cannot plan downstream calls without knowing what fields to expect. None of the 19 tools document return types or structures.
No error handling guidance. None of the tools document what errors can occur, whether they are retryable, or what the LLM should do next on failure.
Naming inconsistency: most tools use verb_noun (good), but 'llm-rubric-architect' and 'ciso-agent' use hyphens instead of underscores, and some agent names are generic (e.g., 'budget-agent', 'orchestrator') without clear action verbs.
Tool registration appears to be done via test script (mcp-server/test-tools.js) rather than formal schema definitions. Tools are listed but implementation details are not visible in provided source, suggesting schema definitions may not exist yet.