Complete Weaviate vector database toolkit. All 32 tools for semantic search, vector operations, schema management, data quality, and batch operations. Perfect for AI-native applications.
This MCP Bundle Server is compatible with any MCP client including Claude Desktop, Cursor, and other Model Context Protocol implementations.
Universal fetch tool that retrieves any object, schema, or metadata using smart ID routing with colon-separated format. Supports direct object retriev...
Search Weaviate vector store using hybrid semantic + keyword search or BM25. Returns ranked results with IDs, titles, and scores.
Delete object(s) or entire collection from Weaviate. Supports three modes: single object deletion, bulk deletion with failure tracking, and collection...
Insert or update objects in Weaviate. Data is ALWAYS an array - use [obj] for single items, [obj1, obj2, ...] for batch. Provide ids array for updates...
List all Weaviate collections with rich filtering options. Supports pattern matching, schema inclusion, object counts, and pagination. Use this for co...
Browse objects in a collection with rich filtering, sorting, pagination, and property selection. The workhorse tool for exploring data with granular c...
Create a complete backup of a collection including schema and data.
Export collection data including vectors, properties, and metadata for backup or migration.
Get a graph of cross-references between collections. Shows which collections reference each other and how.
Check for broken cross-references in collections. Returns objects with references to deleted or non-existent objects.
Perform hybrid search combining semantic vector search with keyword matching. Hybrid search combines BM25 keyword search with vector similarity.
Validate objects against collection schema definition. Checks if objects have all required properties and correct data types.
Restore a collection from a backup. CAUTION: This will overwrite existing data in the collection.
Get comprehensive schema information for a collection including properties, data types, vectorizer configuration, indexes, and module settings.
Copy objects between collections. Useful for data migration, creating backups, or duplicating data for testing. Optionally preserves or regenerates ve...
Check vector quality for corruption issues. Detects zero vectors, NaN values, and vectors with abnormal magnitude. Critical for maintaining search qua...
Find near-duplicate objects using vector similarity. Helps identify redundant data and potential data quality issues from duplicate imports.
Generate comprehensive data quality report. Combines vector quality, property completeness, duplicates, and collection health into a single report wit...
Validate property completeness and quality. Find objects with null, missing, or empty required properties. Essential for data quality monitoring.
Get representative sample objects from a collection. Useful for quick inspection and understanding data structure without fetching all objects.
Find collections that have no objects (empty collections). Useful for identifying collections that can be safely removed or need to be populated.
Generate comprehensive collection profile with statistics on object count, properties, vectors, and overall health. Essential starting point for under...
Analyze distribution of values for a specific property. Shows top values, uniqueness, null rate, and statistics. Essential for understanding data patt...
Update multiple objects matching a filter condition. Useful for bulk status changes, migrations, or data corrections. Includes safety limits to preven...
Analyze property usage in a collection. Shows which properties are consistently populated vs frequently null/empty. Useful for understanding data comp...
Analyze vector embeddings distribution. Shows dimensionality, magnitude statistics, density, and similarity patterns. Critical for understanding embed...
Import multiple objects in batch. Optimized for bulk loading with automatic error handling, retry logic, and progress reporting. Much faster than indi...
Find objects without vector embeddings. Critical for debugging semantic search issues - objects without vectors won't appear in similarity searches.
Delete multiple objects matching a filter condition. DESTRUCTIVE operation with safety limits. Requires explicit confirmation and WHERE filter to prev...
Compare two collections to find differences in schemas, object counts, and configurations. Useful for validating migrations or understanding related c...
Create a new Weaviate collection with custom schema. Define properties, data types, and optionally configure a vectorizer for automatic embedding gene...
Connect this Weaviate MCP Bundle Server to any MCP client in minutes
Compatible with Claude Desktop, Cursor, and all Model Context Protocol clients
Model Context Protocol lets AI tools call external capabilities securely through a single URL. This bundle groups tools behind an MCP endpoint that many clients can use.
Skip the manual setup! Use the .mcpb file format for one-click installation. Check the Claude Desktop tab for setup instructions.
Select ChatGPT, Cursor, Claude Code, or another tab for copy-paste config.