"A search for information about Supabase and Postgres" matching MCP servers:

Enables interaction with Azure DevOps through natural language in Cursor IDE. Supports work item management, pull requests, builds, releases, test management, and guided workflows for development teams, QA testers, and release management.

Provides a standardized way for MCP clients to interact with Apache Airflow's REST API, supporting operations like DAG management and monitoring Airflow system health.

A Model Context Protocol server implementation that allows AI models to interact with and manage Spinnaker deployments, pipelines, and applications through a standardized interface.

Integrates Cline with Azure DevOps services, enabling access to work items, repositories, and pull requests through configurable MCP tools.

Provides AI assistants with comprehensive access to Azure DevOps services including work items, repositories, pull requests, wikis, builds, pipelines, and test plans through 25+ MCP tools with multi-project support.

Enables coding agents to deploy Next.js apps and provision Postgres, Redis, MinIO, NATS, Mailpit, and nginx locally via Docker without requiring Vercel or Supabase signups, using a single account and reading logs from the same environment.

Enables comprehensive GitHub workflow automation including Actions monitoring, PR management, code search, file operations, and repository management through a code-first architecture that reduces token usage by 98%.

Enables AI assistants to interact with Argo CD applications, managing clusters, applications, and resources through natural language.

Provides integration with Apache Airflow's REST API, allowing AI assistants to programmatically interact with Airflow workflows, monitor DAG runs, and manage tasks.

MCP server that enables AI assistants to manage Azure DevOps resources including Work Items, Git repositories, pipelines, and user identities.

A remote MCP server that provides AI agents access to the Rootly API for incident management, allowing users to query and manage incidents, alerts, teams, services, and other incident management resources through natural language.

Record an agent's MCP tool-call workflow once, replay it deterministically for zero tokens, and get a receipt — a snapshot-testing and deterministic-execution layer for Agent Skills.

Blocks unsafe PostgreSQL migrations before an AI agent writes or runs them. check_before_apply returns a pass/fail gate; reads the real Postgres parser, classifies the lock each statement takes, checks 112 safety rules. Runs offline, no database required.

Zero-config data quality monitoring as MCP tools. Profiles a warehouse (Postgres, BigQuery, Snowflake, MySQL, DuckDB), detects anomalies, and gates CI — read-only with the connection resolved server-side, never via the model.

A comprehensive and efficient Model Context Protocol server for task management that works with Claude, Cursor, and other MCP clients, providing powerful search, filtering, and organization capabilities across multiple file formats.

Enables coding agents to run Playwright e2e tests, validate Supabase RLS policies with role-based SQL, typecheck TypeScript, execute npm scripts with parsed test output, and validate Next.js builds, all through a structured MCP interface.

Enables retrieving comprehensive information about GitLab merge requests, including details, commits, diffs, discussions, approvals, and CI/CD status, through natural language.

A demonstration MCP server built with FastMCP v2.0 that provides basic mathematical calculations and greeting functionality. Features Docker containerization, comprehensive testing, and CI/CD automation for learning MCP development patterns.

Enables interaction with the Tembo API to manage organization tasks and view enabled code repositories. Users can create, search, and list tasks or retrieve account information directly through MCP-compatible clients.

Agentic data quality MCP server — runs structured validation rules against warehouses (DuckDB, BigQuery, Athena, Databricks, Postgres), diagnoses failures with LLM root cause analysis, and proposes SQL remediations. Full audit trail of every AI decision.