An AI-powered system that analyzes research papers, extracts concepts, builds knowledge graphs, and generates research ideas by finding connections between concepts
This server has critical definition gaps. Tool descriptions are present but minimal (10-50 chars, well below the 194-char baseline). Input schemas exist but lack parameter descriptions entirely, every parameter is undescribed, violating the 100% baseline for A+ tools. No output schemas are documented. Error handling is absent. The server is a FastAPI wrapper around tool functions, not a proper MCP server with schema registration. Tool names are reasonable (verb_noun style) but parameter documentation is completely missing.
Add a paper and its concepts to the knowledge graph
Extract important research concepts from a paper abstract using LLM
Analyze the research concept graph and generate research ideas based on hidden relationships between concepts
Retrieve the current knowledge graph data including nodes and edges
Search for research papers on arXiv by topic
No parameter descriptions. All 5 tools have input parameters (topic, abstract, concepts, etc.) with zero documentation. LLMs cannot infer what 'topic' means or what format 'concepts' array should contain.
No output schemas documented. Tools return JSON but LLMs have no schema to understand response structure. search_papers returns papers with 'title' and 'summary' fields, but this is inferred from code, not declared.
Tool descriptions are too short (10-50 chars vs 194-char baseline). 'Search for research papers on arXiv by topic' lacks context on when to use this vs other discovery tools, what format results take, or pagination behavior.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 37 | 2026-07-28+ | v2 |
No error handling or recovery guidance. If search_papers fails (network error, invalid topic), the LLM gets no actionable error message or retry strategy.
Missing pagination for list-like tools. get_graph_data and find_connections return unbounded results. If the knowledge graph grows large, returning all nodes/edges will blow context windows.