MCP self-orientation: /context + /verify + architecture resources
Any agent (Claude Code via MCP stdio, or sub-agents via HTTP :3700) can now self-orient without human explanation: GET /context returns: - System purpose and name - All datasets with row counts - All vector indexes with backends - Available models and their strengths - Complete tool list with rules - Current VRAM state POST /verify fact-checks any claim about a worker against the golden data. Agent says "worker 1313 is a Forklift Operator in IL with reliability 0.82" → endpoint returns verified=true/false with exact discrepancies. MCP resources (stdio path for Claude Code): - lakehouse://system — live system status - lakehouse://architecture — full PRD - lakehouse://instructions — agent operating manual - lakehouse://playbooks — successful operations database - lakehouse://datasets — dataset listing This is the "command and control" layer J asked for: any agent connecting to this system gets the context it needs to operate independently. No human intermediary required. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
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@ -4,7 +4,8 @@
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"command": "bun",
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"args": ["run", "/home/profit/lakehouse/mcp-server/index.ts"],
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"env": {
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"LAKEHOUSE_URL": "http://localhost:3100"
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"LAKEHOUSE_URL": "http://localhost:3100",
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"MCP_TRANSPORT": "stdio"
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}
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}
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}
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@ -172,7 +172,75 @@ server.tool(
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},
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);
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// Resources
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// Resources — these give any MCP client full context about the system
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server.resource("lakehouse://system", "lakehouse://system", async (uri) => {
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const health = await api("GET", "/health");
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const datasets = await api("GET", "/catalog/datasets") as any[];
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const indexes = await api("GET", "/vectors/indexes") as any[];
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const vram = await api("GET", "/ai/vram");
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const agent = await api("GET", "/vectors/agent/status");
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const buckets = await api("GET", "/storage/buckets");
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const text = `# Lakehouse System Status
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## Health: ${health === "lakehouse ok" ? "OK" : JSON.stringify(health)}
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## Datasets (${datasets.length})
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${datasets.map((d: any) => `- ${d.name}: ${d.row_count || "?"} rows`).join("\n")}
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## Vector Indexes (${indexes.length})
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${(indexes as any[]).map((i: any) => `- ${i.index_name}: ${i.chunk_count} chunks (${i.vector_backend || "parquet"})`).join("\n")}
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## GPU
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- Used: ${vram?.gpu?.used_mib || "?"}/${vram?.gpu?.total_mib || "?"} MiB
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- Models loaded: ${(vram?.ollama_loaded || []).map((m: any) => m.name).join(", ") || "none"}
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## Autotune Agent
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- Running: ${agent?.running}, Trials: ${agent?.trials_run}, Promotions: ${agent?.promotions}
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## Buckets (${(buckets as any[])?.length || 0})
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${(buckets as any[] || []).map((b: any) => `- ${b.name}: ${b.backend} (${b.reachable ? "reachable" : "DOWN"})`).join("\n")}
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## Services
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- Lakehouse Gateway: :3100
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- AI Sidecar: :3200
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- Agent Gateway: :3700
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- Langfuse: :3001
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- MinIO S3: :9000
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- Ollama: :11434
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## Available Models
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- qwen3: 8.2B, 40K context, thinking+tools (best for reasoning)
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- qwen2.5: 7B, 8K context (best for fast SQL generation)
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- mistral: 7B, 8K context (general generation)
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- nomic-embed-text: 137M (embedding, automatic)
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`;
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return { contents: [{ uri: uri.href, mimeType: "text/plain", text }] };
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});
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server.resource("lakehouse://architecture", "lakehouse://architecture", async (uri) => {
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// Read the PRD directly
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const prd = await Bun.file("/home/profit/lakehouse/docs/PRD.md").text().catch(() => "PRD not found");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: prd }] };
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});
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server.resource("lakehouse://instructions", "lakehouse://instructions", async (uri) => {
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const instructions = await Bun.file("/home/profit/lakehouse/mcp-server/AGENT_INSTRUCTIONS.md").text().catch(() => "Instructions not found");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: instructions }] };
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});
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server.resource("lakehouse://playbooks", "lakehouse://playbooks", async (uri) => {
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const r = await api("POST", "/query/sql", {
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sql: "SELECT * FROM successful_playbooks ORDER BY timestamp DESC LIMIT 20"
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});
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const rows = r?.rows || [];
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const text = rows.length === 0
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? "No playbooks yet. Log successful operations with the log_success tool."
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: rows.map((p: any) => `## ${p.operation}\n- Approach: ${p.approach}\n- Result: ${p.result}\n- Context: ${p.context || "—"}\n`).join("\n");
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return { contents: [{ uri: uri.href, mimeType: "text/markdown", text: `# Successful Playbooks\n\n${text}` }] };
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});
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server.resource("lakehouse://datasets", "lakehouse://datasets", async (uri) => {
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const r = await api("GET", "/catalog/datasets") as any[];
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const text = r.map(d => `${d.name}: ${d.row_count || "?"} rows`).join("\n");
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@ -204,7 +272,67 @@ async function main() {
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try {
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// Health
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if (url.pathname === "/health") return ok({ status: "ok", lakehouse: BASE, tools: 9 });
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if (url.pathname === "/health") return ok({ status: "ok", lakehouse: BASE, tools: 11 });
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// Self-orientation: any agent calls this first to understand the system
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if (url.pathname === "/context") {
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const instructions = await Bun.file("/home/profit/lakehouse/mcp-server/AGENT_INSTRUCTIONS.md").text().catch(() => "");
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const datasets = await api("GET", "/catalog/datasets") as any[];
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const indexes = await api("GET", "/vectors/indexes") as any[];
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const vram = await api("GET", "/ai/vram");
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return ok({
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system: "Lakehouse Staffing Co-Pilot",
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purpose: "AI anticipates staffing coordinator needs — pre-matches workers to contracts, surfaces alerts, builds playbooks from successful operations",
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instructions: instructions.slice(0, 3000),
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datasets: (datasets || []).map((d: any) => ({ name: d.name, rows: d.row_count })),
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indexes: (indexes || []).map((i: any) => ({ name: i.index_name, chunks: i.chunk_count, backend: i.vector_backend })),
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models: { qwen3: "8.2B reasoning+tools", qwen2_5: "7B fast SQL", mistral: "7B generation", nomic: "137M embedding" },
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vram: vram?.gpu,
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tools: ["/search","/sql","/match","/worker/:id","/ask","/log","/playbooks","/profile/:id","/vram","/context","/verify"],
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rules: [
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"Never hallucinate — only state facts from tool responses",
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"SQL for counts/aggregations, hybrid /search for matching",
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"Log every successful operation to /log",
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"Check /playbooks before complex tasks",
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"Verify worker details via /worker/:id before communicating",
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],
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});
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}
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// Verification endpoint — agent can check any claim against SQL
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if (url.pathname === "/verify") {
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const b = await json();
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// b.claim: "worker 4925 is a Forklift Operator in IL with reliability 0.82"
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// b.worker_id: 4925
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// b.checks: { role: "Forklift Operator", state: "IL", reliability: 0.82 }
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if (!b.worker_id) return err("worker_id required");
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const r = await api("POST", "/query/sql", {
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sql: `SELECT * FROM ethereal_workers WHERE worker_id = ${b.worker_id}`
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});
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const worker = r?.rows?.[0];
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if (!worker) return ok({ verified: false, reason: `worker ${b.worker_id} not found` });
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const checks = b.checks || {};
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const failures: string[] = [];
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for (const [field, expected] of Object.entries(checks)) {
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const actual = worker[field];
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if (actual === undefined) continue;
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if (typeof expected === "number") {
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if (Math.abs(Number(actual) - expected) > 0.05) {
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failures.push(`${field}: claimed=${expected} actual=${actual}`);
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}
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} else if (String(actual).toLowerCase() !== String(expected).toLowerCase()) {
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failures.push(`${field}: claimed=${expected} actual=${actual}`);
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}
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}
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return ok({
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verified: failures.length === 0,
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worker_id: b.worker_id,
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worker_name: worker.name,
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failures,
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actual: worker,
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});
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}
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// Tool: hybrid search
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if (url.pathname === "/search") {
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