Connect your account, then chat with AI to run tools.
Profile vectors, find anomalies, and validate quality. Analyze vector distributions, detect missing embeddings, validate properties, check data integrity, and ensure vector database health.
Opens MCPBundles Studio with this server selected. After sign-in, chat and run tools from the same thread.
Browse all toolsDomain knowledge for Data Quality — workflow patterns, data models, and gotchas for your AI agent.
Profile collections, hunt duplicate objects, detect missing embeddings, validate property shapes, score vector health, assemble holistic quality summaries, and find references that no longer resolve.
Analyze distribution of values for a specific property. Shows top values, uniqueness, null rate, and statistics. Essential for understanding data patt...
Analyze vector embeddings distribution. Shows dimensionality, magnitude statistics, density, and similarity patterns. Critical for understanding embed...
Check for broken cross-references in collections. Returns objects with references to deleted or non-existent objects.
Check vector quality for corruption issues. Detects zero vectors, NaN values, and vectors with abnormal magnitude. Critical for maintaining search qua...
Generate detailed data quality report. Combines vector quality, property completeness, duplicates, and collection health into a single report with act...
Find near-duplicate objects using vector similarity. Helps identify redundant data and potential data quality issues from duplicate imports.
Find objects without vector embeddings. Critical for debugging semantic search issues - objects without vectors won't appear in similarity searches.
Generate full collection profile with statistics on object count, properties, vectors, and overall health. Essential starting point for understanding ...
Validate property completeness and quality. Find objects with null, missing, or empty required properties. Essential for data quality monitoring.
Validate objects against collection schema definition. Checks if objects have all required properties and correct data types.
Profile vectors, find anomalies, and validate quality. Analyze vector distributions, detect missing embeddings, validate properties, check data integrity, and ensure vector database health. It provides 10 tools that AI agents can use through the Model Context Protocol (MCP).
Add the MCPBundles server URL to your MCP client configuration (Claude Desktop, Cursor, VS Code, etc.). The URL format is: https://mcp.mcpbundles.com/bundle/weaviate-data-quality. Authentication is handled automatically.
Data Quality provides 10 tools that can be called by AI agents, along with a SKILL.md that gives your AI agent domain knowledge about when and how to use them.
Data Quality uses API Key. Weaviate requires credentials. Connect via MCPBundles and authentication is handled automatically.
Connect Data Quality to any MCP client in minutes
https://mcp.mcpbundles.com/bundle/weaviate-data-qualityThe link prefills the Add custom connector dialog — you still review the values and click Add, then Connect to complete OAuth.
Data Quality and paste the MCP URL into Remote MCP server URL.Custom connectors at claude.ai require a paid Claude plan (Pro, Max, Team, or Enterprise).
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