feat(mcpedia): Phase 2 — semantic + hybrid search, tRPC/Hono API

- @mcpedia/embeddings: OpenRouter provider (9router /v1, encoding_format float),
  chunkText + embedChunks; EMBED_DIM=2048
- document_chunks table (real[] embedding) — pgvector NOT available on shared
  imrnes Postgres, so cosine is computed in-app (KB-scale fine); pgvector deferred
- indexer: chunk + embed + upsert per document
- @mcpedia/search: semanticSearch (cosine) + hybridSearch (FTS+cosine RRF)
- apps/api: Hono + tRPC v11 (6 procedures), serve on :4020
- MCP: semantic_search + hybrid_search tools (6 total)
- web: keyword/hybrid toggle; .env.example + README + PHASES updated

apps/mcp/src/index.ts

@@ -1,8 +1,7 @@
 import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
 import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
 import { z } from "zod";
-import { listDocuments, getDocument, getRelated } from "@mcpedia/core";
-import { keywordSearch } from "@mcpedia/search";
+import { listDocuments, getDocument, getRelated, semanticSearch, hybridSearch, keywordSearch } from "@mcpedia/core";
 
 export function createMcpServer(): McpServer {
   const server = new McpServer({
@@ -84,6 +83,42 @@ export function createMcpServer(): McpServer {
     },
   );
 
+  server.registerTool(
+    "semantic_search",
+    {
+      description:
+        "Semantic (embedding) search across chunked document content. Best for conceptual/paraphrased queries that don't share exact keywords. Returns chunks ranked by cosine similarity.",
+      inputSchema: z.object({
+        query: z.string().describe("Natural-language query"),
+        limit: z.number().int().positive().max(50).optional(),
+      }),
+    },
+    async ({ query, limit }) => {
+      const hits = await semanticSearch(query, limit ?? 10);
+      return {
+        content: [{ type: "text", text: JSON.stringify(hits, null, 2) }],
+      };
+    },
+  );
+
+  server.registerTool(
+    "hybrid_search",
+    {
+      description:
+        "Hybrid search fusing full-text (Postgres FTS) and semantic (embedding) signals via Reciprocal Rank Fusion. Best general-purpose search.",
+      inputSchema: z.object({
+        query: z.string().describe("Free-text or natural-language query"),
+        limit: z.number().int().positive().max(50).optional(),
+      }),
+    },
+    async ({ query, limit }) => {
+      const hits = await hybridSearch(query, limit ?? 10);
+      return {
+        content: [{ type: "text", text: JSON.stringify(hits, null, 2) }],
+      };
+    },
+  );
+
   return server;
 }