e1d48d3c8f
MCP server at mcp-server/index.ts — 9 tools exposing the full lakehouse to any MCP-compatible model: search_workers (hybrid SQL+vector), query_sql, match_contract, get_worker, rag_question, log_success, get_playbooks, swap_profile, vram_status The "successful playbooks" pattern: log_success writes outcomes back to the lakehouse as a queryable dataset. Small models call get_playbooks to learn what approaches worked for similar tasks — no retraining needed, just data. generate_workers.py scales to 100K+ with realistic distributions: - 20 roles weighted by staffing industry frequency - 44 real Midwest/South cities across 12 states - Per-role skill pools (warehouse/production/machine/maintenance) - 13 certification types with realistic probability - 8 behavioral archetypes with score distributions - SMS communication templates (20 patterns) 100K worker dataset ingested: 70MB CSV → Parquet in 1.1s. Verified: 11K forklift ops, 27K in IL, archetype distribution matches weights. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
16 lines
265 B
JSON
16 lines
265 B
JSON
{
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"name": "mcp-server",
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"module": "index.ts",
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"type": "module",
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"private": true,
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"devDependencies": {
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"@types/bun": "latest"
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},
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"peerDependencies": {
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"typescript": "^5"
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},
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"dependencies": {
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"@modelcontextprotocol/sdk": "^1.29.0"
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}
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}
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