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fix: fixes vector stage input parsing for aggregate tool MCP-336 #794

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Merged
himanshusinghs merged 1 commit into from
Dec 11, 2025
Merged

fix: fixes vector stage input parsing for aggregate tool MCP-336 #794

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2 changes: 1 addition & 1 deletion src/tools/mongodb/read/aggregate.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ const genericPipelineDescription = "An array of aggregation stages to execute.";
export const getAggregateArgs = (vectorSearchEnabled: boolean) =>
({
pipeline: z
.array(vectorSearchEnabled ? z.union([AnyAggregateStage, VectorSearchStage]) : AnyAggregateStage)
.array(vectorSearchEnabled ? z.union([VectorSearchStage, AnyAggregateStage]) : AnyAggregateStage)
.describe(vectorSearchEnabled ? pipelineDescriptionWithVectorSearch : genericPipelineDescription),
responseBytesLimit: z.number().optional().default(ONE_MB).describe(`\
The maximum number of bytes to return in the response. This value is capped by the server's configured maxBytesPerQuery and cannot be exceeded. \
Expand Down
66 changes: 64 additions & 2 deletions tests/integration/tools/mongodb/read/aggregate.test.ts
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Original file line number Diff line number Diff line change
Expand Up @@ -16,6 +16,7 @@ import {
import * as constants from "../../../../../src/helpers/constants.js";
import { freshInsertDocuments } from "./find.test.js";
import { BSON } from "bson";
import { DOCUMENT_EMBEDDINGS } from "./vyai/embeddings.js";

describeWithMongoDB("aggregate tool", (integration) => {
afterEach(() => {
Expand Down Expand Up @@ -384,8 +385,6 @@ describeWithMongoDB(
}
);

import { DOCUMENT_EMBEDDINGS } from "./vyai/embeddings.js";

describeWithMongoDB(
"aggregate tool with atlas search enabled",
(integration) => {
Expand Down Expand Up @@ -921,6 +920,69 @@ If the user requests additional filtering, include filters in \`$vectorSearch.fi
);
});
});

describe("outputDimension transformation", () => {
it.each([
{ numDimensions: 2048, outputDimension: "2048" },
{ numDimensions: 4096, outputDimension: "4096" },
])(
"should successfully transform outputDimension string '$outputDimension' to number",
async ({ numDimensions, outputDimension }) => {
await waitUntilSearchIsReady(integration.mongoClient());

const collection = integration.mongoClient().db(integration.randomDbName()).collection("databases");
await collection.insertOne({ name: "mongodb", description_embedding: DOCUMENT_EMBEDDINGS.float });

await createVectorSearchIndexAndWait(
integration.mongoClient(),
integration.randomDbName(),
"databases",
[
{
type: "vector",
path: "description_embedding",
numDimensions,
similarity: "cosine",
quantization: "none",
},
]
);

await integration.connectMcpClient();
const response = await integration.mcpClient().callTool({
name: "aggregate",
arguments: {
database: integration.randomDbName(),
collection: "databases",
pipeline: [
{
$vectorSearch: {
index: "default",
path: "description_embedding",
queryVector: "example query",
numCandidates: 10,
limit: 10,
embeddingParameters: {
model: "voyage-3-large",
outputDimension, // Pass as string literal
},
},
},
{
$project: {
description_embedding: 0,
},
},
],
},
});

const responseContent = getResponseContent(response);
// String should succeed and be transformed to number internally
expect(responseContent).toContain("The aggregation resulted in");
}
);
});
},
{
getUserConfig: () => ({
Expand Down