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Autonomous knowledge acquisition from academic research papers. Provides tools for arXiv paper search and download, Semantic Scholar API integration, PDF parsing and text extraction, key insight extraction, citation graph analysis, and knowledge integration with enhanced-memory.
The server defines 6 research tools with explicit schemas and descriptions. However, there are significant gaps in schema completeness, parameter validation, output documentation, and error handling. Tool names are clear and action-oriented (search_*, download_*, extract_*, analyze_*, store_*). Descriptions are present and reasonably detailed (100-200 chars), meeting baseline expectations. However, many parameters lack constraints, defaults, and validation guidance. Output schemas are not documented, LLMs cannot predict return structures. Error handling is absent, no guidance on what to do if a search fails, a PDF download times out, or an API key is invalid. The server uses a deprecated logging pattern (logging.basicConfig at module level) rather than per-request logLevel. Security concerns: no visible rate limiting, no permission gates on destructive tools (download_paper, store_paper_knowledge), no secret injection pattern. Composition is reasonable, each tool does one thing, but tools lack idempotency guarantees and pagination support.
Analyze citation relationships and paper influence using Semantic Scholar citation graph.
Download research paper PDF from URL. Saves to local storage and returns file path.
Extract key insights, findings, and techniques from research paper text. Uses AI to identify important contributions.
Search arXiv for research papers by query. Returns paper metadata including title, authors, abstract, PDF URL, and publication date.
Search Semantic Scholar for papers with citation counts and influence metrics. Provides academic impact analysis.
Store extracted paper knowledge in enhanced-memory for AGI learning. Creates structured memory entities.
Output schemas not documented. LLMs cannot predict what search_arxiv, search_semantic_scholar, or analyze_citations return. Fields, types, and structures are invisible.
download_paper description does not explain error conditions (network timeout, invalid URL, PDF corruption). No guidance on recovery or retry logic.
No pagination support. search_arxiv and search_semantic_scholar accept max_results/limit but do not return total_count, next_cursor, or pagination metadata. Large result sets will blow context window.
No rate limiting or request throttling visible. An agent retrying failed searches could spam arXiv/Semantic Scholar APIs rapidly, causing IP blocks or service degradation.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | D | 58 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 37 | - | v1 |
download_paper accepts url and paper_id but does not validate URL format, does not handle SSL errors, and does not document retry behavior or timeout values.
analyze_citations depth parameter (1-3) lacks explicit constraint in schema, no minValue or maxValue. LLM could pass 0 or 100.
store_paper_knowledge is a write operation but has no confirmation step, dry-run option, or permission check. An agent could accidentally store malformed or sensitive data.
No documented idempotency guarantees. If an agent retries download_paper or store_paper_knowledge after a network glitch, will it duplicate files or memory entries?
extract_insights focus_areas parameter is optional with default [], but the tool uses AI to extract insights. No LLM API key or model name is visible in the code snippet, implementation is incomplete or secret injection is missing.
search_arxiv and search_semantic_scholar may return empty results, but error response format is not documented. LLM cannot distinguish 'no results' from 'API error'.