scholar-monitoring

name	scholar-monitoring
description	This skill should be used when the user asks to "monitor a scholar", "check researcher activity", "track publications from author X", "follow author Y", "scholar update for Z", "what has researcher X published recently", or wants to retrieve and synthesize a researcher's recent publication activity using PaperBot MCP tools.
tools	["check_scholar","analyze_trends","save_to_memory"]

Scholar Monitoring Workflow

Monitor a researcher's recent publication activity: fetch their profile and papers, optionally analyze output trends, and save a monitoring note.

Workflow

Step 1: Check scholar activity

Call check_scholar with the researcher's name.

• Parameters: scholar_name (required; use the researcher's full name as commonly published), max_papers (default 10; increase to 20–30 for career-wide coverage)
• Returns: dict with:
  • scholar: profile dict with name, hIndex, citationCount, affiliations, paperCount, url
  • recent_papers: list of paper dicts (title, abstract, year, venue, citation count)
  • candidates: list of top-3 candidate matches (inspect if the top result is wrong)
• If degraded=True, the scholar was not found on Semantic Scholar or the API is unavailable

Step 2: Analyze paper trends (optional)

If recent_papers is non-empty and the user wants thematic analysis, call analyze_trends.

• Parameters: topic (use the scholar's name or primary research area as the topic), papers (the recent_papers list from Step 1)
• Returns: dict with trend_analysis (natural language narrative of the scholar's research focus and evolution)
• Skip this step if the user only needs raw paper metadata (no LLM API key required for Step 1 alone)

Step 3: Save monitoring note

Call save_to_memory with a summary of the scholar's recent activity.

• Parameters: content (monitoring summary — include scholar name, hIndex, recent paper titles, and trend analysis if available), kind="note", user_id (default "default"), scope_type="global", confidence (0.0–1.0; suggest 0.9 for factual publication data)
• Returns: dict with created or skipped status

Note on Scholar Lookup

check_scholar searches Semantic Scholar by name. Common issues:

• Name diacritics: Names with accents (e.g., "Müller", "Bengio") may need the ASCII variant ("Muller", "Yoshua Bengio") if exact-match fails
• New researchers: Very new researchers may have limited or no Semantic Scholar records — check paperCount in the returned profile
• Name ambiguity: The candidates field in the response lists the top 3 matches; inspect these if the top result appears to be the wrong person (wrong affiliation, wrong research area)
• Name format: Use "First Last" format; middle names are generally not needed but can help disambiguate common names

Degraded Mode

analyze_trends (Step 2) requires a configured LLM API key. check_scholar (Step 1) and save_to_memory (Step 3) do not require LLM.

When analyze_trends returns degraded=True:

• Set OPENAI_API_KEY or ANTHROPIC_API_KEY and restart the MCP server
• Skip Step 2 and proceed directly to Step 3 with a summary based on raw paper metadata

When check_scholar itself returns degraded=True:

• This indicates the scholar was not found or the Semantic Scholar API is unavailable
• Try an alternate name spelling or abbreviation
• Check candidates in the response for close matches