8e8503e8f9
- plugins/consistency.py: find_contradictions, find_anachronisms, find_orphans,
find_ontology_violations — all stub queries returning the
{violations, count} envelope, ready for T5 to populate violation nodes.
Two of the tools accept a severity filter (any|critical|major|minor).
- neo4j/init.cypher: uniqueness constraints on Contradiction, Anachronism,
Orphan, OntologyViolation (id) + severity/status indexes on the
contradiction/anachronism labels.
- README.md: bump plugin list to five, replace 'no consistency engine' copy
with 'consistency engine is a stub', drop the two T3 bullet points from
the next-steps section.
Verification: /healthz lists 18 tools (was 14), all 4 new tools return
{"violations": [], "count": 0}, full test.sh passes.
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
"""
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trade plugin — Postgres-backed operational data.
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Demonstrates the polyglot pattern: a domain type (trade log entry) that
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isn't a core ontology concept, backed by Postgres because it's
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high-volume time-series data, queryable through the same MCP gateway.
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"""
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from server import get_postgres, REGISTRY
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def _q(sql, params=None, fetch=True):
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conn = get_postgres()
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try:
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with conn.cursor() as cur:
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cur.execute(sql, params or ())
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if fetch and cur.description:
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cols = [d[0] for d in cur.description]
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return [dict(zip(cols, r)) for r in cur.fetchall()]
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return []
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finally:
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conn.close()
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@REGISTRY.tool(
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name="log_trade",
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description="Record a trade. Buyer and seller must exist as Person or Faction nodes (call entity_context to verify).",
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input_schema={
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"type": "object",
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"properties": {
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"buyer_id": {"type": "string"},
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"seller_id": {"type": "string"},
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"item_id": {"type": "string"},
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"quantity": {"type": "number"},
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"unit": {"type": "string", "default": "gp"},
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"unit_price": {"type": "number"},
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"location_id": {"type": "string"},
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"in_fiction_time": {"type": "string"},
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"notes": {"type": "string"},
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},
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"required": ["buyer_id", "seller_id", "item_id", "quantity", "unit_price"],
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},
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)
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def log_trade(args):
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total = float(args["quantity"]) * float(args["unit_price"])
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_q("""
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INSERT INTO trade_log
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(buyer_id, seller_id, item_id, quantity, unit, unit_price, total_price, location_id, in_fiction_time, notes)
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VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s)
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RETURNING id
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""", (
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args["buyer_id"], args["seller_id"], args["item_id"],
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args["quantity"], args.get("unit", "gp"), args["unit_price"], total,
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args.get("location_id"), args.get("in_fiction_time"), args.get("notes"),
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), fetch=False)
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return {"logged": True, "total_price": total}
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@REGISTRY.tool(
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name="trades_by_buyer",
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description="List trades where a given entity was the buyer, most recent first.",
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input_schema={
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"type": "object",
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"properties": {
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"buyer_id": {"type": "string"},
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"limit": {"type": "integer", "default": 10},
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},
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"required": ["buyer_id"],
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},
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)
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def trades_by_buyer(args):
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rows = _q("""
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SELECT id, occurred_at, seller_id, item_id, quantity, unit, unit_price, total_price, location_id, notes
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FROM trade_log
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WHERE buyer_id = %s
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ORDER BY occurred_at DESC
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LIMIT %s
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""", (args["buyer_id"], args.get("limit", 10)))
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return {"buyer": args["buyer_id"], "count": len(rows), "trades": rows}
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@REGISTRY.tool(
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name="market_price",
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description="Average price for an item_id over the last N records. Computed from the trade log.",
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input_schema={
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"type": "object",
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"properties": {
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"item_id": {"type": "string"},
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"limit": {"type": "integer", "default": 100, "minimum": 1, "maximum": 1000},
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},
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"required": ["item_id"],
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},
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)
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def market_price(args):
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rows = _q("""
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SELECT unit_price, total_price, occurred_at
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FROM trade_log
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WHERE item_id = %s
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ORDER BY occurred_at DESC
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LIMIT %s
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""", (args["item_id"], args.get("limit", 100)))
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if not rows:
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return {"item_id": args["item_id"], "sample_size": 0}
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prices = [float(r["unit_price"]) for r in rows]
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return {
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"item_id": args["item_id"],
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"sample_size": len(prices),
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"avg_unit_price": round(sum(prices) / len(prices), 2),
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"min_unit_price": min(prices),
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"max_unit_price": max(prices),
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"most_recent": rows[0]["occurred_at"].isoformat() if rows else None,
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}
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def register(registry):
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pass
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