Trend Analysis Workflow

Identify research trends across a topic by collecting papers, analyzing patterns, and saving a synthesis of emerging themes.

Workflow

Step 1: Load research context (optional)

If a research track exists for the topic, call get_research_context to retrieve existing memories and previously found papers.

• Parameters: query (the research topic), user_id (default "default"), track_id (optional; pass if a specific track ID is known)
• Returns: dict with papers (list), memories (list), stage (workflow stage string)
• Use the existing memories as context when synthesizing results in Step 4
• Skip this step if no prior research context exists for the topic

Step 2: Search for papers

Call paper_search with the topic. Use a broader corpus for trend analysis.

• Parameters: query (required), max_results (use 20–50 for trend analysis — a larger corpus improves trend signal quality), sources (optional)
• Returns: list of paper dicts with title, abstract, authors, year, venue
• If track_id context was loaded in Step 1, merge the existing papers with new results (deduplicate by arxiv_id or doi)

Step 3: Analyze trends

Call analyze_trends with the topic and the list of papers from Step 2.

• Parameters: topic (the research area string), papers (list of paper dicts from paper_search; pass the full list for best results)
• Returns: dict with trend_analysis (natural language narrative), topic, paper_count
• Check for degraded=True — analyze_trends requires a configured LLM API key

Step 4: Save synthesis

Call save_to_memory with the trend analysis narrative and any additional observations.

• Parameters: content (the trend_analysis text from Step 3, optionally enhanced with your own observations), kind ("note" for factual observations, "hypothesis" for directional predictions), user_id (default "default"), scope_type ("global" for broad field trends, "track" if scoping to a research area), scope_id (track ID if scope_type="track"), confidence (0.0–1.0)
• Returns: dict with created or skipped status

Degraded Mode

analyze_trends requires a configured LLM API key. paper_search and get_research_context work without LLM.

When analyze_trends returns degraded=True:

• The response also contains an error key describing the issue
• Set OPENAI_API_KEY or ANTHROPIC_API_KEY and restart the MCP server
• In degraded mode, present the raw search results grouped by year or venue as a manual trend signal; skip Step 3 or surface the paper list to the user directly

Notes

• For fast trend snapshots, use max_results=20 and skip Step 1
• For deep research landscape maps, use max_results=50 and integrate prior context from get_research_context
• When analyzing sub-field trends (e.g., "sparse attention mechanisms"), narrow the query rather than broadening max_results
• Multiple calls with different topic variants (e.g., "mixture of experts" vs. "sparse expert models") can be combined for a richer landscape view