MongoDB MCP Server

Instructions

Input Schema

Input Schema (JSON Schema)

Implementation Reference

• src/index.ts:1045-1085 (handler)

  Handler function for the 'get_schema' tool. Samples up to 'sampleSize' documents from the specified collection, infers the schema using the 'inferSchema' helper, and returns the schema as JSON.
  case 'get_schema': { const { database, collection, sampleSize = 100 } = request.params.arguments as { database?: string; collection: string; sampleSize?: number; }; const dbName = database || this.defaultDatabase; if (!dbName) { throw new McpError( ErrorCode.InvalidRequest, 'Database name is required when no default database is configured' ); } const db = client.db(dbName); const docs = await db .collection(collection) .find() .limit(sampleSize) .toArray(); if (docs.length === 0) { return { content: [ { type: 'text', text: 'No documents found in collection', }, ], }; } const schema = await this.inferSchema(docs); return { content: [ { type: 'text', text: JSON.stringify(schema, null, 2), }, ], }; }
• src/index.ts:349-368 (schema)

  Input schema definition for the 'get_schema' tool, specifying parameters like database, collection, and sampleSize with validation constraints.
  inputSchema: { type: 'object', properties: { database: { type: 'string', description: 'Database name (optional if default database is configured)', }, collection: { type: 'string', description: 'Collection name', }, sampleSize: { type: 'number', description: 'Number of documents to sample (default: 100)', minimum: 1, maximum: 1000, }, }, required: ['collection'], },
• src/index.ts:336-369 (registration)

  Tool registration in the ListTools response, defining name, description, and input schema for 'get_schema'.
  name: 'get_schema', description: `Infer schema from a collection by analyzing sample documents. Best Practice: Use this before querying to understand collection structure. Example: use_mcp_tool with server_name: "mongodb", tool_name: "get_schema", arguments: { "collection": "users", "sampleSize": 100 }`, inputSchema: { type: 'object', properties: { database: { type: 'string', description: 'Database name (optional if default database is configured)', }, collection: { type: 'string', description: 'Collection name', }, sampleSize: { type: 'number', description: 'Number of documents to sample (default: 100)', minimum: 1, maximum: 1000, }, }, required: ['collection'], }, },
• src/index.ts:112-153 (helper)

  Helper method 'inferSchema' that recursively analyzes sample documents to infer the data schema, handling nested objects, arrays, and primitive types.
  */ private async inferSchema(documents: any[], path = '', schema: any = {}) { for (const doc of documents) { Object.entries(doc).forEach(([key, value]) => { const fullPath = path ? `${path}.${key}` : key; if (Array.isArray(value)) { if (!schema[fullPath]) { schema[fullPath] = { type: 'array' }; } // Handle empty arrays if (value.length === 0) { schema[fullPath].items = { type: 'unknown' }; } // Handle arrays of primitives else if (typeof value[0] !== 'object' || value[0] === null) { schema[fullPath].items = { type: typeof value[0] }; } // Handle arrays of objects else { schema[fullPath].items = { type: 'object', properties: {} }; this.inferSchema(value, `${fullPath}.items.properties`, schema); } } else if (value && typeof value === 'object') { if (!schema[fullPath]) { schema[fullPath] = { type: 'object', properties: {} }; } this.inferSchema([value], `${fullPath}.properties`, schema); } else { if (!schema[fullPath]) { schema[fullPath] = { type: typeof value, example: value, // Add example value for better understanding nullable: value === null }; } } }); } return schema; }