news-mcp/OUTLOOK.md

News MCP Server — Project Vision & Status

Current version: v0.4.0 — see PROJECT.md for architecture details.

Core Design Principle

Raw news is useless to agents. Processed news is powerful.

• ✅ Clusters are the unit of truth, not raw articles
• ✅ 100 articles → 5–10 clusters, with entities, sentiment, importance
• ✅ SQL-level filtering by time, entity, keyword — no full-table JSON parsing

Architecture (v0.4.0)

See PROJECT.md for full schema and architecture. Key points:

• payload_ts generated column for indexed time-range queries
• cluster_entities and cluster_keywords junction tables for O(log n) entity/keyword search
• MCP tools and Dashboard REST API both query the same SQLite DB
• Docker deployment on thinkcenter-2 (192.168.0.200:8506)

Tool Surface

Tool	Status	Notes
get_latest_events	✅	Time-filtered via payload_ts SQL index
get_events_for_entity	⚠️	MCP tool still uses Python-side entity matching (top-N limit). Dashboard uses SQL junction table. Known design flaw.
get_event_summary	✅	LLM-written narrative
detect_emerging_topics	✅	entity/keyword/phrase signal types, velocity scoring
get_news_sentiment	⚠️	Same Python-side entity matching limitation as get_events_for_entity
get_related_recent_entities	✅	Co-occurrence + Google Trends blend
get_feeds / toggle_feed	✅	Feed management
detect_emerging_topics(around=...)	✅	Scope to entity neighborhood

Known Design Issues

Two Stores (FIXED, May 2026)

DashboardStore was eliminated. All methods moved to SQLiteClusterStore. MCP tools now use SQL-level junction-table entity/keyword search via get_clusters_by_entity_or_keyword() — no row-limit blind spot.

MCP Tool Entity Search (FIXED, May 2026)

get_events_for_entity and get_news_sentiment now use SQLiteClusterStore.get_clusters_by_entity_or_keyword() with proper SQL-level filtering across the full time window via the cluster_entities and cluster_keywords junction tables.

Backfill Scripts

After deploying junction table schema changes:

docker exec -it news-mcp python3 scripts/backfill_junction_tables.py

For timestamp normalization (already run on live server):

docker exec -it news-mcp python3 scripts/normalize_cluster_timestamps.py

Future Directions (v0.5.0+)

"Emerging entity graph over time"

• Collapse detect_emerging_topics() results into canonical entity nodes
• Build weighted edges from co-occurrence in recent clusters
• Infer communities (story neighborhoods)
• Track graph evolution across refresh windows (node momentum, edge strength changes)
• Agent tool: get_emerging_entity_graph(timeframe, limit)