diff --git a/src/McpServer/autopilot-monitor-mcp/package-lock.json b/src/McpServer/autopilot-monitor-mcp/package-lock.json
index b8c831a6..41862f41 100644
--- a/src/McpServer/autopilot-monitor-mcp/package-lock.json
+++ b/src/McpServer/autopilot-monitor-mcp/package-lock.json
@@ -1,15 +1,16 @@
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+          "url": "https://github.com/sponsors/feross"
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@@ -624,6 +853,30 @@
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+          "url": "https://github.com/sponsors/feross"
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@@ -662,6 +915,53 @@
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     },
+    "node_modules/semver": {
+      "version": "7.7.4",
+      "resolved": "https://registry.npmjs.org/semver/-/semver-7.7.4.tgz",
+      "integrity": "sha512-vFKC2IEtQnVhpT78h1Yp8wzwrf8CM+MzKMHGJZfBtzhZNycRFnXsHk6E5TxIkkMsgNS7mdX3AGB7x2QM2di4lA==",
+      "license": "ISC",
+      "bin": {
+        "semver": "bin/semver.js"
+      },
+      "engines": {
+        "node": ">=10"
+      }
+    },
     "node_modules/send": {
       "version": "1.2.1",
       "resolved": "https://registry.npmjs.org/send/-/send-1.2.1.tgz",
@@ -1536,6 +2217,29 @@
       "integrity": "sha512-E5LDX7Wrp85Kil5bhZv46j8jOeboKq5JMmYM3gVGdGH8xFpPWXUMsNrlODCrkoxMEeNi/XZIwuRvY4XNwYMJpw==",
       "license": "ISC"
     },
+    "node_modules/sharp": {
+      "version": "0.32.6",
+      "resolved": "https://registry.npmjs.org/sharp/-/sharp-0.32.6.tgz",
+      "integrity": "sha512-KyLTWwgcR9Oe4d9HwCwNM2l7+J0dUQwn/yf7S0EnTtb0eVS4RxO0eUSvxPtzT4F3SY+C4K6fqdv/DO27sJ/v/w==",
+      "hasInstallScript": true,
+      "license": "Apache-2.0",
+      "dependencies": {
+        "color": "^4.2.3",
+        "detect-libc": "^2.0.2",
+        "node-addon-api": "^6.1.0",
+        "prebuild-install": "^7.1.1",
+        "semver": "^7.5.4",
+        "simple-get": "^4.0.1",
+        "tar-fs": "^3.0.4",
+        "tunnel-agent": "^0.6.0"
+      },
+      "engines": {
+        "node": ">=14.15.0"
+      },
+      "funding": {
+        "url": "https://opencollective.com/libvips"
+      }
+    },
     "node_modules/shebang-command": {
       "version": "2.0.0",
       "resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz",
@@ -1629,6 +2333,60 @@
         "url": "https://github.com/sponsors/ljharb"
       }
     },
+    "node_modules/simple-concat": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/simple-concat/-/simple-concat-1.0.1.tgz",
+      "integrity": "sha512-cSFtAPtRhljv69IK0hTVZQ+OfE9nePi/rtJmw5UjHeVyVroEqJXP1sFztKUy1qU+xvz3u/sfYJLa947b7nAN2Q==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ],
+      "license": "MIT"
+    },
+    "node_modules/simple-get": {
+      "version": "4.0.1",
+      "resolved": "https://registry.npmjs.org/simple-get/-/simple-get-4.0.1.tgz",
+      "integrity": "sha512-brv7p5WgH0jmQJr1ZDDfKDOSeWWg+OVypG99A/5vYGPqJ6pxiaHLy8nxtFjBA7oMa01ebA9gfh1uMCFqOuXxvA==",
+      "funding": [
+        {
+          "type": "github",
+          "url": "https://github.com/sponsors/feross"
+        },
+        {
+          "type": "patreon",
+          "url": "https://www.patreon.com/feross"
+        },
+        {
+          "type": "consulting",
+          "url": "https://feross.org/support"
+        }
+      ],
+      "license": "MIT",
+      "dependencies": {
+        "decompress-response": "^6.0.0",
+        "once": "^1.3.1",
+        "simple-concat": "^1.0.0"
+      }
+    },
+    "node_modules/simple-swizzle": {
+      "version": "0.2.4",
+      "resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.4.tgz",
+      "integrity": "sha512-nAu1WFPQSMNr2Zn9PGSZK9AGn4t/y97lEm+MXTtUDwfP0ksAIX4nO+6ruD9Jwut4C49SB1Ws+fbXsm/yScWOHw==",
+      "license": "MIT",
+      "dependencies": {
+        "is-arrayish": "^0.3.1"
+      }
+    },
     "node_modules/statuses": {
       "version": "2.0.2",
       "resolved": "https://registry.npmjs.org/statuses/-/statuses-2.0.2.tgz",
@@ -1638,6 +2396,79 @@
         "node": ">= 0.8"
       }
     },
+    "node_modules/streamx": {
+      "version": "2.25.0",
+      "resolved": "https://registry.npmjs.org/streamx/-/streamx-2.25.0.tgz",
+      "integrity": "sha512-0nQuG6jf1w+wddNEEXCF4nTg3LtufWINB5eFEN+5TNZW7KWJp6x87+JFL43vaAUPyCfH1wID+mNVyW6OHtFamg==",
+      "license": "MIT",
+      "dependencies": {
+        "events-universal": "^1.0.0",
+        "fast-fifo": "^1.3.2",
+        "text-decoder": "^1.1.0"
+      }
+    },
+    "node_modules/string_decoder": {
+      "version": "1.3.0",
+      "resolved": "https://registry.npmjs.org/string_decoder/-/string_decoder-1.3.0.tgz",
+      "integrity": "sha512-hkRX8U1WjJFd8LsDJ2yQ/wWWxaopEsABU1XfkM8A+j0+85JAGppt16cr1Whg6KIbb4okU6Mql6BOj+uup/wKeA==",
+      "license": "MIT",
+      "dependencies": {
+        "safe-buffer": "~5.2.0"
+      }
+    },
+    "node_modules/strip-json-comments": {
+      "version": "2.0.1",
+      "resolved": "https://registry.npmjs.org/strip-json-comments/-/strip-json-comments-2.0.1.tgz",
+      "integrity": "sha512-4gB8na07fecVVkOI6Rs4e7T6NOTki5EmL7TUduTs6bu3EdnSycntVJ4re8kgZA+wx9IueI2Y11bfbgwtzuE0KQ==",
+      "license": "MIT",
+      "engines": {
+        "node": ">=0.10.0"
+      }
+    },
+    "node_modules/tar-fs": {
+      "version": "3.1.2",
+      "resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-3.1.2.tgz",
+      "integrity": "sha512-QGxxTxxyleAdyM3kpFs14ymbYmNFrfY+pHj7Z8FgtbZ7w2//VAgLMac7sT6nRpIHjppXO2AwwEOg0bPFVRcmXw==",
+      "license": "MIT",
+      "dependencies": {
+        "pump": "^3.0.0",
+        "tar-stream": "^3.1.5"
+      },
+      "optionalDependencies": {
+        "bare-fs": "^4.0.1",
+        "bare-path": "^3.0.0"
+      }
+    },
+    "node_modules/tar-stream": {
+      "version": "3.1.8",
+      "resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-3.1.8.tgz",
+      "integrity": "sha512-U6QpVRyCGHva435KoNWy9PRoi2IFYCgtEhq9nmrPPpbRacPs9IH4aJ3gbrFC8dPcXvdSZ4XXfXT5Fshbp2MtlQ==",
+      "license": "MIT",
+      "dependencies": {
+        "b4a": "^1.6.4",
+        "bare-fs": "^4.5.5",
+        "fast-fifo": "^1.2.0",
+        "streamx": "^2.15.0"
+      }
+    },
+    "node_modules/teex": {
+      "version": "1.0.1",
+      "resolved": "https://registry.npmjs.org/teex/-/teex-1.0.1.tgz",
+      "integrity": "sha512-eYE6iEI62Ni1H8oIa7KlDU6uQBtqr4Eajni3wX7rpfXD8ysFx8z0+dri+KWEPWpBsxXfxu58x/0jvTVT1ekOSg==",
+      "license": "MIT",
+      "dependencies": {
+        "streamx": "^2.12.5"
+      }
+    },
+    "node_modules/text-decoder": {
+      "version": "1.2.7",
+      "resolved": "https://registry.npmjs.org/text-decoder/-/text-decoder-1.2.7.tgz",
+      "integrity": "sha512-vlLytXkeP4xvEq2otHeJfSQIRyWxo/oZGEbXrtEEF9Hnmrdly59sUbzZ/QgyWuLYHctCHxFF4tRQZNQ9k60ExQ==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "b4a": "^1.6.4"
+      }
+    },
     "node_modules/toidentifier": {
       "version": "1.0.1",
       "resolved": "https://registry.npmjs.org/toidentifier/-/toidentifier-1.0.1.tgz",
@@ -1673,6 +2504,18 @@
         "fsevents": "~2.3.3"
       }
     },
+    "node_modules/tunnel-agent": {
+      "version": "0.6.0",
+      "resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz",
+      "integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==",
+      "license": "Apache-2.0",
+      "dependencies": {
+        "safe-buffer": "^5.0.1"
+      },
+      "engines": {
+        "node": "*"
+      }
+    },
     "node_modules/type-is": {
       "version": "2.0.1",
       "resolved": "https://registry.npmjs.org/type-is/-/type-is-2.0.1.tgz",
@@ -1705,7 +2548,6 @@
       "version": "6.21.0",
       "resolved": "https://registry.npmjs.org/undici-types/-/undici-types-6.21.0.tgz",
       "integrity": "sha512-iwDZqg0QAGrg9Rav5H4n0M64c3mkR59cJ6wQp+7C4nI0gsmExaedaYLNO44eT4AtBBwjbTiGPMlt2Md0T9H9JQ==",
-      "dev": true,
       "license": "MIT"
     },
     "node_modules/unpipe": {
@@ -1717,6 +2559,12 @@
         "node": ">= 0.8"
       }
     },
+    "node_modules/util-deprecate": {
+      "version": "1.0.2",
+      "resolved": "https://registry.npmjs.org/util-deprecate/-/util-deprecate-1.0.2.tgz",
+      "integrity": "sha512-EPD5q1uXyFxJpCrLnCc1nHnq3gOa6DZBocAIiI2TaSCA7VCJ1UJDMagCzIkXNsUYfD1daK//LTEQ8xiIbrHtcw==",
+      "license": "MIT"
+    },
     "node_modules/vary": {
       "version": "1.1.2",
       "resolved": "https://registry.npmjs.org/vary/-/vary-1.1.2.tgz",
diff --git a/src/McpServer/autopilot-monitor-mcp/package.json b/src/McpServer/autopilot-monitor-mcp/package.json
index bc6e19b2..ad5beac4 100644
--- a/src/McpServer/autopilot-monitor-mcp/package.json
+++ b/src/McpServer/autopilot-monitor-mcp/package.json
@@ -1,6 +1,6 @@
 {
   "name": "autopilot-monitor-mcp",
-  "version": "1.0.0",
+  "version": "1.1.0",
   "description": "MCP server for Autopilot-Monitor — enables AI agents to query enrollment sessions, device properties, events and vulnerabilities",
   "type": "module",
   "bin": {
@@ -14,6 +14,7 @@
   "dependencies": {
     "@anthropic-ai/sdk": "^0.80.0",
     "@modelcontextprotocol/sdk": "^1.0.0",
+    "@xenova/transformers": "^2.17.0",
     "zod": "^3.22.0"
   },
   "devDependencies": {
diff --git a/src/McpServer/autopilot-monitor-mcp/src/index.ts b/src/McpServer/autopilot-monitor-mcp/src/index.ts
index 84aaa346..05a3faf0 100644
--- a/src/McpServer/autopilot-monitor-mcp/src/index.ts
+++ b/src/McpServer/autopilot-monitor-mcp/src/index.ts
@@ -1,14 +1,27 @@
+import { resolve, dirname } from 'node:path';
+import { fileURLToPath } from 'node:url';
 import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
 import { StdioServerTransport } from '@modelcontextprotocol/sdk/server/stdio.js';
 import { registerTools } from './tools.js';
 import { registerResources } from './resources.js';
+import { buildKnowledgeBase } from './knowledge-base.js';
+
+const __dirname = dirname(fileURLToPath(import.meta.url));
+
+// Resolve the rules directory (relative to project root)
+const RULES_DIR = process.env.RULES_DIR ?? resolve(__dirname, '..', '..', '..', '..', 'rules');
 
 const server = new McpServer({
   name: 'autopilot-monitor',
-  version: '1.0.0',
+  version: '1.1.0',
 });
 
-registerTools(server);
+// Build the knowledge base (embeddings) before registering tools,
+// so the search_knowledge tool has immediate access.
+console.error('Initializing vector search knowledge base…');
+const knowledgeBase = await buildKnowledgeBase(RULES_DIR);
+
+registerTools(server, knowledgeBase);
 registerResources(server);
 
 const transport = new StdioServerTransport();
@@ -16,3 +29,4 @@ await server.connect(transport);
 
 console.error('Autopilot-Monitor MCP Server running (stdio transport)');
 console.error(`API URL: ${process.env.AUTOPILOT_API_URL ?? 'https://autopilotmonitor-api.azurewebsites.net'}`);
+console.error(`Knowledge base: ${knowledgeBase.size} documents indexed from ${RULES_DIR}`);
diff --git a/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts
new file mode 100644
index 00000000..570c8d25
--- /dev/null
+++ b/src/McpServer/autopilot-monitor-mcp/src/knowledge-base.ts
@@ -0,0 +1,210 @@
+/**
+ * Loads analysis rules, gather rules, and IME log patterns from the rules/
+ * directory, computes embeddings via a local transformer model, and populates
+ * the in-memory vector store for semantic search.
+ */
+
+import { readdir, readFile } from 'node:fs/promises';
+import { join } from 'node:path';
+import { pipeline, type FeatureExtractionPipeline } from '@xenova/transformers';
+import { VectorStore, type VectorDocument } from './vector-store.js';
+
+const MODEL_NAME = 'Xenova/all-MiniLM-L6-v2';
+
+let embedder: FeatureExtractionPipeline | null = null;
+
+async function getEmbedder(): Promise<FeatureExtractionPipeline> {
+  if (!embedder) {
+    embedder = await pipeline('feature-extraction', MODEL_NAME, {
+      quantized: true,
+    }) as FeatureExtractionPipeline;
+  }
+  return embedder;
+}
+
+/** Compute a normalized embedding vector for a text string. */
+export async function embed(text: string): Promise<number[]> {
+  const model = await getEmbedder();
+  const output = await model(text, { pooling: 'mean', normalize: true });
+  return Array.from(output.data as Float32Array);
+}
+
+/** Batch-embed multiple texts (sequential to avoid OOM on large batches). */
+async function embedBatch(texts: string[]): Promise<number[][]> {
+  const model = await getEmbedder();
+  const results: number[][] = [];
+  // Process in small batches to balance speed and memory
+  const batchSize = 16;
+  for (let i = 0; i < texts.length; i += batchSize) {
+    const batch = texts.slice(i, i + batchSize);
+    const outputs = await Promise.all(
+      batch.map(async (t) => {
+        const out = await model(t, { pooling: 'mean', normalize: true });
+        return Array.from(out.data as Float32Array);
+      })
+    );
+    results.push(...outputs);
+  }
+  return results;
+}
+
+// ──────────────────────────────────────────────────────────────
+// Rule / pattern loading helpers
+// ──────────────────────────────────────────────────────────────
+
+interface AnalyzeRule {
+  ruleId: string;
+  title: string;
+  description: string;
+  severity: string;
+  category: string;
+  explanation?: string;
+  remediation?: Array<{ title: string; steps: string[] }>;
+  tags?: string[];
+}
+
+interface GatherRule {
+  ruleId: string;
+  title: string;
+  description: string;
+  category: string;
+  collectorType?: string;
+  target?: string;
+  trigger?: string;
+  tags?: string[];
+}
+
+interface ImeLogPattern {
+  patternId: string;
+  category: string;
+  description: string;
+  pattern: string;
+  action?: string;
+}
+
+async function loadJsonFiles<T>(dir: string): Promise<T[]> {
+  let entries: string[];
+  try {
+    entries = await readdir(dir);
+  } catch {
+    return [];
+  }
+  const results: T[] = [];
+  for (const f of entries) {
+    if (!f.endsWith('.json')) continue;
+    try {
+      const raw = await readFile(join(dir, f), 'utf-8');
+      results.push(JSON.parse(raw) as T);
+    } catch {
+      // skip malformed files
+    }
+  }
+  return results;
+}
+
+/** Build the searchable text for each document type. */
+
+function analyzeRuleText(r: AnalyzeRule): string {
+  const parts = [
+    `[${r.ruleId}] ${r.title}`,
+    r.description,
+    r.explanation ?? '',
+  ];
+  if (r.remediation) {
+    for (const rem of r.remediation) {
+      parts.push(`Remediation: ${rem.title} — ${rem.steps.join('; ')}`);
+    }
+  }
+  if (r.tags?.length) parts.push(`Tags: ${r.tags.join(', ')}`);
+  return parts.filter(Boolean).join('\n');
+}
+
+function gatherRuleText(r: GatherRule): string {
+  return [
+    `[${r.ruleId}] ${r.title}`,
+    r.description,
+    r.target ? `Target: ${r.target}` : '',
+    r.tags?.length ? `Tags: ${r.tags.join(', ')}` : '',
+  ].filter(Boolean).join('\n');
+}
+
+function imePatternText(p: ImeLogPattern): string {
+  return [
+    `[${p.patternId}] ${p.description}`,
+    `Category: ${p.category}`,
+    p.action ? `Action: ${p.action}` : '',
+  ].filter(Boolean).join('\n');
+}
+
+// ──────────────────────────────────────────────────────────────
+// Public API
+// ──────────────────────────────────────────────────────────────
+
+export async function buildKnowledgeBase(rulesRoot: string): Promise<VectorStore> {
+  const store = new VectorStore();
+
+  console.error('Loading knowledge base documents…');
+
+  const [analyzeRules, gatherRules, imePatterns] = await Promise.all([
+    loadJsonFiles<AnalyzeRule>(join(rulesRoot, 'analyze')),
+    loadJsonFiles<GatherRule>(join(rulesRoot, 'gather')),
+    loadJsonFiles<ImeLogPattern>(join(rulesRoot, 'ime-log-patterns')),
+  ]);
+
+  const docs: Array<{ id: string; text: string; metadata: Record<string, unknown> }> = [];
+
+  for (const r of analyzeRules) {
+    docs.push({
+      id: r.ruleId,
+      text: analyzeRuleText(r),
+      metadata: {
+        type: 'analyze-rule',
+        severity: r.severity,
+        category: r.category,
+        title: r.title,
+        tags: r.tags ?? [],
+      },
+    });
+  }
+
+  for (const r of gatherRules) {
+    docs.push({
+      id: r.ruleId,
+      text: gatherRuleText(r),
+      metadata: {
+        type: 'gather-rule',
+        category: r.category,
+        title: r.title,
+        tags: r.tags ?? [],
+      },
+    });
+  }
+
+  for (const p of imePatterns) {
+    docs.push({
+      id: p.patternId,
+      text: imePatternText(p),
+      metadata: {
+        type: 'ime-log-pattern',
+        category: p.category,
+        description: p.description,
+        action: p.action ?? null,
+      },
+    });
+  }
+
+  console.error(`Embedding ${docs.length} documents (model: ${MODEL_NAME})…`);
+
+  const texts = docs.map((d) => d.text);
+  const embeddings = await embedBatch(texts);
+
+  const vectorDocs: VectorDocument[] = docs.map((d, i) => ({
+    ...d,
+    embedding: embeddings[i],
+  }));
+
+  store.addMany(vectorDocs);
+  console.error(`Knowledge base ready — ${store.size} documents indexed.`);
+
+  return store;
+}
diff --git a/src/McpServer/autopilot-monitor-mcp/src/tools.ts b/src/McpServer/autopilot-monitor-mcp/src/tools.ts
index d04a084b..1018e83b 100644
--- a/src/McpServer/autopilot-monitor-mcp/src/tools.ts
+++ b/src/McpServer/autopilot-monitor-mcp/src/tools.ts
@@ -1,8 +1,10 @@
 import { McpServer } from '@modelcontextprotocol/sdk/server/mcp.js';
 import { z } from 'zod';
 import { apiFetch, buildQuery } from './client.js';
+import type { VectorStore } from './vector-store.js';
+import { embed } from './knowledge-base.js';
 
-export function registerTools(server: McpServer): void {
+export function registerTools(server: McpServer, knowledgeBase?: VectorStore): void {
   // Tool 1: search_sessions
   server.tool(
     'search_sessions',
@@ -167,4 +169,173 @@ export function registerTools(server: McpServer): void {
       return { content: [{ type: 'text' as const, text: JSON.stringify(data, null, 2) }] };
     }
   );
+
+  // Tool 8: search_events_semantic (vector search over live events)
+  server.tool(
+    'search_events_semantic',
+    'Semantic search over enrollment event messages within a session or across recent failed sessions. ' +
+    'Unlike exact filters, this finds events by MEANING — e.g. "network timeout" also matches "connection timed out", "request failed after waiting". ' +
+    'Use this when you need to find events that match a symptom description rather than an exact event type. ' +
+    'Provide a sessionId to search within one session, or omit it to search across recent failed sessions.',
+    {
+      query: z.string().describe('Natural language description of what to find (e.g. "app download stuck", "certificate error", "disk space low")'),
+      sessionId: z.string().optional().describe('Search within a specific session. If omitted, searches across recent failed sessions.'),
+      tenantId: z.string().optional().describe('Tenant ID (global-scoped key only)'),
+      topK: z.number().min(1).max(30).optional().default(10).describe('Number of matching events to return (1-30, default 10)'),
+      minScore: z.number().min(0).max(1).optional().default(0.35)
+        .describe('Minimum similarity score (0-1, default 0.35)'),
+    },
+    async ({ query, sessionId, tenantId, topK, minScore }) => {
+      // Fetch events — either from a specific session or from recent failed sessions
+      type EventEntry = { eventType?: string; severity?: string; source?: string; message?: string; timestamp?: string; phase?: string; data?: Record<string, unknown> };
+      let events: EventEntry[] = [];
+      let sessionIds: string[] = [];
+
+      if (sessionId) {
+        // Single session mode
+        const q = buildQuery({ tenantId } as Record<string, string | undefined>);
+        const data = await apiFetch(`/api/sessions/${sessionId}/events${q}`) as { events?: EventEntry[] };
+        events = data?.events ?? [];
+        sessionIds = [sessionId];
+      } else {
+        // Cross-session mode: fetch recent failed sessions, then their events
+        const searchQ = buildQuery({ status: 'Failed', tenantId, limit: 5 } as Record<string, string | number | undefined>);
+        const sessions = await apiFetch(`/api/sessions/search${searchQ}`) as {
+          sessions?: Array<{ sessionId?: string }>;
+        };
+        const ids = (sessions?.sessions ?? []).map((s) => s.sessionId).filter(Boolean) as string[];
+        sessionIds = ids;
+        const q = buildQuery({ tenantId } as Record<string, string | undefined>);
+        const allEvents = await Promise.all(
+          ids.map(async (sid) => {
+            try {
+              const d = await apiFetch(`/api/sessions/${sid}/events${q}`) as { events?: EventEntry[] };
+              return (d?.events ?? []).map((e) => ({ ...e, _sessionId: sid }));
+            } catch { return []; }
+          })
+        );
+        events = allEvents.flat();
+      }
+
+      // Filter to events that have a meaningful message
+      const candidates = events.filter((e) => e.message && e.message.length > 5);
+
+      if (candidates.length === 0) {
+        return {
+          content: [{
+            type: 'text' as const,
+            text: JSON.stringify({ query, resultCount: 0, results: [], note: 'No events with messages found.' }),
+          }],
+        };
+      }
+
+      // Build text representations and compute embeddings
+      const texts = candidates.map((e) => {
+        const parts = [e.message];
+        if (e.eventType) parts.push(`Event: ${e.eventType}`);
+        if (e.severity) parts.push(`Severity: ${e.severity}`);
+        if (e.source) parts.push(`Source: ${e.source}`);
+        return parts.join(' | ');
+      });
+
+      const queryEmbedding = await embed(query);
+
+      // Embed events in batches
+      const eventEmbeddings = await Promise.all(texts.map((t) => embed(t)));
+
+      // Score and rank
+      const scored = candidates.map((e, i) => {
+        let dot = 0, normA = 0, normB = 0;
+        for (let j = 0; j < queryEmbedding.length; j++) {
+          dot += queryEmbedding[j] * eventEmbeddings[i][j];
+          normA += queryEmbedding[j] * queryEmbedding[j];
+          normB += eventEmbeddings[i][j] * eventEmbeddings[i][j];
+        }
+        const denom = Math.sqrt(normA) * Math.sqrt(normB);
+        const score = denom === 0 ? 0 : dot / denom;
+        return { event: e, score, text: texts[i] };
+      });
+
+      const results = scored
+        .filter((r) => r.score >= minScore)
+        .sort((a, b) => b.score - a.score)
+        .slice(0, topK)
+        .map((r) => ({
+          score: Math.round(r.score * 1000) / 1000,
+          sessionId: (r.event as Record<string, unknown>)._sessionId ?? sessionId,
+          eventType: r.event.eventType,
+          severity: r.event.severity,
+          source: r.event.source,
+          phase: r.event.phase,
+          timestamp: r.event.timestamp,
+          message: r.event.message,
+        }));
+
+      return {
+        content: [{
+          type: 'text' as const,
+          text: JSON.stringify({
+            query,
+            sessionsSearched: sessionIds,
+            eventsAnalyzed: candidates.length,
+            resultCount: results.length,
+            results,
+          }, null, 2),
+        }],
+      };
+    }
+  );
+
+  // Tool 9: search_knowledge (vector search over rules)
+  server.tool(
+    'search_knowledge',
+    'Semantic search over the Autopilot Monitor knowledge base: analysis rules, gather rules, and IME log patterns. ' +
+    'Use natural language queries like "app install timeout", "BitLocker issues", "detection script failure". ' +
+    'Returns the most relevant rules and patterns ranked by semantic similarity. ' +
+    'Great for finding remediation steps, understanding error patterns, or discovering relevant diagnostic rules.',
+    {
+      query: z.string().describe('Natural language search query (e.g. "app download timeout", "TPM not ready", "ESP stuck")'),
+      topK: z.number().min(1).max(20).optional().default(5).describe('Number of results to return (1-20, default 5)'),
+      type: z.enum(['all', 'analyze-rule', 'gather-rule', 'ime-log-pattern']).optional().default('all')
+        .describe('Filter by document type. Default: search all types.'),
+      minScore: z.number().min(0).max(1).optional().default(0.3)
+        .describe('Minimum similarity score threshold (0-1, default 0.3). Lower = more results, higher = stricter matching.'),
+    },
+    async ({ query, topK, type, minScore }) => {
+      if (!knowledgeBase || knowledgeBase.size === 0) {
+        return {
+          content: [{
+            type: 'text' as const,
+            text: JSON.stringify({ error: 'Knowledge base not initialized. The server may still be loading embeddings.' }),
+          }],
+        };
+      }
+
+      const queryEmbedding = await embed(query);
+      let results = knowledgeBase.search(queryEmbedding, type === 'all' ? topK * 3 : topK, minScore);
+
+      // Filter by type if specified
+      if (type !== 'all') {
+        results = results.filter((r) => r.metadata.type === type);
+      }
+
+      results = results.slice(0, topK);
+
+      const formatted = results.map((r) => ({
+        id: r.id,
+        score: Math.round(r.score * 1000) / 1000,
+        type: r.metadata.type,
+        title: r.metadata.title ?? r.metadata.description ?? r.id,
+        content: r.text,
+        metadata: r.metadata,
+      }));
+
+      return {
+        content: [{
+          type: 'text' as const,
+          text: JSON.stringify({ query, resultCount: formatted.length, results: formatted }, null, 2),
+        }],
+      };
+    }
+  );
 }
diff --git a/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts b/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts
new file mode 100644
index 00000000..c3828562
--- /dev/null
+++ b/src/McpServer/autopilot-monitor-mcp/src/vector-store.ts
@@ -0,0 +1,61 @@
+/**
+ * Lightweight in-memory vector store using cosine similarity.
+ * Each document is stored alongside its embedding vector.
+ */
+
+export interface VectorDocument {
+  id: string;
+  text: string;
+  metadata: Record<string, unknown>;
+  embedding: number[];
+}
+
+export interface SearchResult {
+  id: string;
+  text: string;
+  metadata: Record<string, unknown>;
+  score: number;
+}
+
+function cosineSimilarity(a: number[], b: number[]): number {
+  let dot = 0;
+  let normA = 0;
+  let normB = 0;
+  for (let i = 0; i < a.length; i++) {
+    dot += a[i] * b[i];
+    normA += a[i] * a[i];
+    normB += b[i] * b[i];
+  }
+  const denom = Math.sqrt(normA) * Math.sqrt(normB);
+  return denom === 0 ? 0 : dot / denom;
+}
+
+export class VectorStore {
+  private documents: VectorDocument[] = [];
+
+  add(doc: VectorDocument): void {
+    this.documents.push(doc);
+  }
+
+  addMany(docs: VectorDocument[]): void {
+    this.documents.push(...docs);
+  }
+
+  search(queryEmbedding: number[], topK = 5, minScore = 0.3): SearchResult[] {
+    const scored = this.documents.map((doc) => ({
+      id: doc.id,
+      text: doc.text,
+      metadata: doc.metadata,
+      score: cosineSimilarity(queryEmbedding, doc.embedding),
+    }));
+
+    return scored
+      .filter((r) => r.score >= minScore)
+      .sort((a, b) => b.score - a.score)
+      .slice(0, topK);
+  }
+
+  get size(): number {
+    return this.documents.length;
+  }
+}