Enables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.
Begin creating a new MCP server project by generating project structures, technical specifications, and documentation for TypeScript or Python development.
Create a Python script in the Avizo MCP work directory to automate Python runs within Avizo. Specify script content, optionally set job name and filename.
Execute data quality validation rules from a YAML file against DuckDB, BigQuery, Athena, Databricks, or Postgres, returning a JSON report with optional LLM-driven root cause analysis.
Statically audits MCP server Python files by enumerating tools registered via decorators and reporting security risks including shell execution, filesystem writes, network egress, and code injection.