"Documentation" matching MCP servers:

Provides AI assistants with access to Vega-Lite documentation, visualization examples, and specification validation tools. Enables searching docs, retrieving examples by category, validating JSON specs, and accessing schema information for creating data visualizations.

Enables AI assistants to audit, profile, and govern dbt projects through natural language, including coverage scoring, schema drift detection, test suggestions, and non-destructive YAML documentation updates.

Enables AI assistants to audit dbt project health, profile data, detect schema drift, suggest tests, and auto-generate documentation through natural language. It consolidates coverage analysis, data profiling, and safe YAML updates into a single MCP interface.

Enables AI assistants to audit dbt project coverage and health, profile warehouse data, detect schema drift, suggest tests, and generate or safely update model documentation through natural language.

Generates complete PyAirbyte data pipeline code and setup instructions for moving data between 600+ Airbyte source and destination connectors or to Pandas DataFrames, using AI-powered context-aware guidance based on connector documentation.

Enables AI coding assistants to assess dbt project health and test/documentation coverage, profile warehouse data, detect schema drift, suggest tests, and safely update schema.yml documentation through natural language.

It enables AI assistants to audit dbt project health, profile data, detect schema drift, suggest tests, and auto-generate documentation through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, suggest tests, and safely generate or update schema.yml documentation through natural language.

Enables AI assistants to audit dbt project quality, profile warehouse data, detect schema drift, and auto-generate or update documentation and tests through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, suggest tests, and auto-generate non-destructive schema documentation through natural language.

Enables AI assistants to audit dbt project quality, profile warehouse data, detect schema drift, suggest tests, and generate non-destructive schema.yml documentation through natural language.

Enables AI assistants to audit a dbt project's health, profile warehouse data, detect schema drift, and generate tests and documentation through natural language. It scores documentation and test coverage, recommends dbt tests from column profiling statistics, and non-destructively merges results into schema.yml files while preserving existing comments and formatting.

Enables AI assistants to audit dbt projects for documentation, test coverage, and naming health, profile warehouse data, detect schema drift against the manifest, and non-destructively generate or update schema.yml documentation and tests through natural language.

Enables AI assistants to audit dbt project quality, profile warehouse data, detect schema drift, and safely generate or update model documentation and tests through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, suggest tests, and safely generate or update model documentation through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and generate non-destructive schema.yml documentation and test suggestions through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and auto-generate or update model documentation and tests through natural language.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and safely generate or update dbt documentation and tests through natural language.

Enables AI assistants to audit dbt project health, profile data, detect schema drift, suggest tests, and safely generate documentation through natural language.

Enables AI clients to investigate data engineering workflows by exposing tools for job status, logs, schema lookup, read-only SQL, and searching documentation/incidents, along with resources and prompts for ETL failure analysis.