MongoDB MCP Server

import type { CallToolResult } from "@modelcontextprotocol/sdk/types.js"; import { formatUntrustedData } from "../../src/tools/tool.js"; import type { MockedTools } from "./sdk/accuracyTestingClient.js"; import { describeAccuracyTests } from "./sdk/describeAccuracyTests.js"; import { Matcher } from "./sdk/matcher.js"; const mockedTools: MockedTools = { "collection-indexes": ({ collection }: Record<string, unknown>): CallToolResult => { return { content: formatUntrustedData( `Found 1 indexes in the collection "${collection as string}".`, JSON.stringify({ name: "_id_", key: { _id: 1 }, }) ), }; }, }; describeAccuracyTests( [ { prompt: "Create an index that covers the following query on 'mflix.movies' namespace - { \"release_year\": 1992 }", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "classic", keys: { release_year: 1, }, }, ], }, }, ], mockedTools, }, { prompt: "Create a text index on title field in 'mflix.movies' namespace", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "classic", keys: { title: "text", }, }, ], }, }, ], mockedTools, }, { prompt: "Create a vector search index on 'mflix.movies' namespace on the 'plotSummary' field. The index should use 1024 dimensions.", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "vectorSearch", fields: [ { type: "vector", path: "plotSummary", numDimensions: "1024", }, ], }, ], }, }, ], mockedTools, }, { prompt: "Create a vector search index on 'mflix.movies' namespace with on the 'plotSummary' field and 'genre' field, both of which contain vector embeddings. Pick a sensible number of dimensions for a voyage 3.5 model.", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "vectorSearch", fields: [ { type: "vector", path: "plotSummary", numDimensions: Matcher.string((value) => { const intValue = parseInt(value); return intValue % 2 === 0 && intValue >= 256 && intValue <= 8192; }), similarity: Matcher.anyOf(Matcher.undefined, Matcher.string()), }, { type: "vector", path: "genre", numDimensions: Matcher.string((value) => { const intValue = parseInt(value); return intValue % 2 === 0 && intValue >= 256 && intValue <= 8192; }), similarity: Matcher.anyOf(Matcher.undefined, Matcher.string()), }, ], }, ], }, }, ], mockedTools, }, { prompt: "Create a vector search index on 'mflix.movies' namespace where the 'plotSummary' field is indexed as a 1024-dimensional vector and the 'releaseDate' field is indexed as a regular field.", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "vectorSearch", fields: [ { type: "vector", path: "plotSummary", numDimensions: "1024", }, { type: "filter", path: "releaseDate", }, ], }, ], }, }, ], mockedTools, }, { prompt: "Create an Atlas search index on 'mflix.movies' namespace with dynamic mappings enabled", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "search", analyzer: Matcher.anyOf(Matcher.undefined, Matcher.value("lucene.standard")), mappings: { dynamic: true, }, numPartitions: Matcher.anyOf(Matcher.undefined, Matcher.number()), }, ], }, }, ], mockedTools, }, { prompt: "Create an Atlas search index on 'mflix.movies' namespace for searching on 'title' as string field and 'year' as number field", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "search", analyzer: Matcher.anyOf(Matcher.undefined, Matcher.value("lucene.standard")), mappings: { dynamic: Matcher.anyOf(Matcher.undefined, Matcher.value(false)), fields: { title: { type: "string", }, year: { type: "number", }, }, }, numPartitions: Matcher.anyOf(Matcher.undefined, Matcher.number()), }, ], }, }, ], mockedTools, }, { prompt: "Create an Atlas search index on 'mflix.movies' namespace with a custom 'lucene.keyword' analyzer, where 'title' is indexed as an autocomplete field and 'genres' as a string array field, and 'released' as a date field", expectedToolCalls: [ { toolName: "create-index", parameters: { database: "mflix", collection: "movies", name: Matcher.anyOf(Matcher.undefined, Matcher.string()), definition: [ { type: "search", analyzer: "lucene.keyword", mappings: { dynamic: Matcher.anyOf(Matcher.undefined, Matcher.value(false)), fields: { title: { type: "autocomplete", }, genres: { type: "string", }, released: { type: "date", }, }, }, }, ], }, }, ], mockedTools, }, ], { userConfig: { previewFeatures: "search" }, clusterConfig: { search: true, }, } );