"Information about dbt (Data Build Tool or other meanings)" matching MCP servers:

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.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and auto-generate documentation and test suggestions through natural language. It provides tooling for coverage scoring, batched column statistics, and non-destructive schema.yml updates.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and auto-generate documentation through natural language. It combines coverage scoring, test suggestions, and non-destructive YAML updates in a single MCP server.

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.

Enables AI assistants to audit dbt project health and test coverage, profile data-warehouse columns, detect schema drift, and safely generate documentation and test suggestions into schema.yml files. It runs read-only warehouse queries and non-destructive YAML merges, all through natural-language conversation in Claude Desktop, Cursor, or other MCP clients.

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 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 project health, profile warehouse data, detect schema drift, recommend tests, and safely generate or update schema.yml documentation through natural language. It provides project scoring, coverage rankings, and non-destructive YAML edits while preserving comments and existing configurations.

Enables AI assistants to audit dbt project health, profile warehouse data, detect schema drift, and suggest tests through natural language. It also auto-generates documentation and safely merges updates 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, suggest tests, and generate documentation through natural language. It provides read-only analysis and non-destructive YAML updates for dbt projects.

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.

An MCP server that gives coding agents hands and eyes on an Android device, providing 19 tools for building, testing, installing, UI inspection, input, logs, and toolchain diagnosis. It returns concise, structured results to make the build-debug loop efficient.

Audits MCP tool descriptions for quality and reliability, scoring them 0-100, detecting smells, and providing rewritten versions for better agent accuracy.

Protects AI agents from bad tool outputs, schema mismatches, and tool poisoning by validating, sanitizing, and scoring reliability of tool responses.

Enables Claude to interact with physical iOS devices running Expo development builds via USB or network tunnels. It supports capturing screenshots, streaming system logs, managing applications, and retrieving device information through natural language.

This MCP server verifies that an agent’s claimed tool output matches the actual response returned by a tool to prevent invented or distorted results. It flags mismatches, omissions, and invented fields to ensure downstream logic only processes verified tool data.

Enables verification of deployment-scoped model-visible DSH tool surface conformance by checking recorded tool identities and schemas against approved locks, reporting only drift categories and hashes.

Analyzes multi-step AI agent tool chains to compute success probability, identify bottlenecks, and suggest better execution orders, enabling more reliable agents via local pure-math computation.

A throwaway HTTP MCP server that empirically tests whether Cursor invokes tools by exact name or munges them, to inform case normalization decisions.

A lightweight multi-provider MCP testing fixture with two providers (eva_air and uk_railway), each exposing 5 tools using in-memory fake data, designed for verifying MCP client tool discovery, tool calls, and multi-provider routing.