/**
* Search tools registration
*/
import type { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import type { EmbeddingProvider } from "../embeddings/base.js";
import { BM25SparseVectorGenerator } from "../embeddings/sparse.js";
import type { QdrantManager } from "../qdrant/client.js";
import * as schemas from "./schemas.js";
export interface SearchToolDependencies {
qdrant: QdrantManager;
embeddings: EmbeddingProvider;
}
export function registerSearchTools(
server: McpServer,
deps: SearchToolDependencies,
): void {
const { qdrant, embeddings } = deps;
// semantic_search
server.registerTool(
"semantic_search",
{
title: "Semantic Search",
description:
"Search for documents using natural language queries. Returns the most semantically similar documents.",
inputSchema: schemas.SemanticSearchSchema,
},
async ({ collection, query, limit, filter }) => {
// Check if collection exists
const exists = await qdrant.collectionExists(collection);
if (!exists) {
return {
content: [
{
type: "text",
text: `Error: Collection "${collection}" does not exist.`,
},
],
isError: true,
};
}
// Generate embedding for query
const { embedding } = await embeddings.embed(query);
// Search
const results = await qdrant.search(
collection,
embedding,
limit || 5,
filter,
);
return {
content: [{ type: "text", text: JSON.stringify(results, null, 2) }],
};
},
);
// hybrid_search
server.registerTool(
"hybrid_search",
{
title: "Hybrid Search",
description:
"Perform hybrid search combining semantic vector search with keyword search using BM25. This provides better results by combining the strengths of both approaches. The collection must be created with enableHybrid set to true.",
inputSchema: schemas.HybridSearchSchema,
},
async ({ collection, query, limit, filter }) => {
// Check if collection exists
const exists = await qdrant.collectionExists(collection);
if (!exists) {
return {
content: [
{
type: "text",
text: `Error: Collection "${collection}" does not exist.`,
},
],
isError: true,
};
}
// Check if collection has hybrid search enabled
const collectionInfo = await qdrant.getCollectionInfo(collection);
if (!collectionInfo.hybridEnabled) {
return {
content: [
{
type: "text",
text: `Error: Collection "${collection}" does not have hybrid search enabled. Create a new collection with enableHybrid set to true.`,
},
],
isError: true,
};
}
// Generate dense embedding for query
const { embedding } = await embeddings.embed(query);
// Generate sparse vector for query
const sparseGenerator = new BM25SparseVectorGenerator();
const sparseVector = sparseGenerator.generate(query);
// Perform hybrid search
const results = await qdrant.hybridSearch(
collection,
embedding,
sparseVector,
limit || 5,
filter,
);
return {
content: [{ type: "text", text: JSON.stringify(results, null, 2) }],
};
},
);
}
