/**
* Document operation 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 DocumentToolDependencies {
qdrant: QdrantManager;
embeddings: EmbeddingProvider;
}
export function registerDocumentTools(
server: McpServer,
deps: DocumentToolDependencies,
): void {
const { qdrant, embeddings } = deps;
// add_documents
server.registerTool(
"add_documents",
{
title: "Add Documents",
description:
"Add documents to a collection. Documents will be automatically embedded using the configured embedding provider.",
inputSchema: schemas.AddDocumentsSchema,
},
async ({ collection, documents }) => {
// Check if collection exists and get info
const exists = await qdrant.collectionExists(collection);
if (!exists) {
return {
content: [
{
type: "text",
text: `Error: Collection "${collection}" does not exist. Create it first using create_collection.`,
},
],
isError: true,
};
}
const collectionInfo = await qdrant.getCollectionInfo(collection);
// Generate embeddings for all documents
const texts = documents.map((doc) => doc.text);
const embeddingResults = await embeddings.embedBatch(texts);
// If hybrid search is enabled, generate sparse vectors and use appropriate method
if (collectionInfo.hybridEnabled) {
const sparseGenerator = new BM25SparseVectorGenerator();
// Prepare points with both dense and sparse vectors
const points = documents.map((doc, index) => ({
id: doc.id,
vector: embeddingResults[index].embedding,
sparseVector: sparseGenerator.generate(doc.text),
payload: {
text: doc.text,
...doc.metadata,
},
}));
await qdrant.addPointsWithSparse(collection, points);
} else {
// Standard dense-only vectors
const points = documents.map((doc, index) => ({
id: doc.id,
vector: embeddingResults[index].embedding,
payload: {
text: doc.text,
...doc.metadata,
},
}));
await qdrant.addPoints(collection, points);
}
return {
content: [
{
type: "text",
text: `Successfully added ${documents.length} document(s) to collection "${collection}".`,
},
],
};
},
);
// delete_documents
server.registerTool(
"delete_documents",
{
title: "Delete Documents",
description: "Delete specific documents from a collection by their IDs.",
inputSchema: schemas.DeleteDocumentsSchema,
},
async ({ collection, ids }) => {
await qdrant.deletePoints(collection, ids);
return {
content: [
{
type: "text",
text: `Successfully deleted ${ids.length} document(s) from collection "${collection}".`,
},
],
};
},
);
}
