"Checking Supabase migrations in GitHub Actions" matching MCP servers:

An MCP server that connects to Supabase PostgreSQL databases, exposing table schemas as resources and providing tools for data analysis through SQL queries.

Enables agents to connect to Kafka clusters, discover topics and consumer groups, draft and activate a catalog describing what events carry, inspect stuck or at-risk value, and optionally redrive dead letters after a dry run.

MCP server for configuring the konsol Frappe app via the CLI. Enables AI clients to manage dimensions, measures, fact tables, connectors, and schemas using natural language.

Enables checking lead rows, CSV files, or Apify datasets for duplicates, email syntax and risk signals, domain issues, missing fields, and stale dates, returning a per-row import, review, or reject verdict so only clean records reach a CRM or sequencer.

Bridges Microsoft Foundry agents and Power BI semantic models into GitHub Copilot Chat, enabling users to query and modify measures as the signed-in user via MCP.

An MCP server for ReciPal's nutrition-label API that lets AI assistants read and edit recipes, ingredients, and subrecipes, including bulk operations like cloning and swapping ingredients, with safety guards for destructive actions.

Enables MCP-compatible hosts to record, retrieve, and verify LoopGrid decision evidence through tools for decisions, reviews, actions, outcomes, and verification.

Enables language models to model SAP BW/4HANA through the BW Modeling Tools REST interface, with intent-based tools for searching, reading, writing, activating, querying data, monitoring, runtime actions, and transports. It uses compact schemas and safe-by-default permissions to complete changes in few calls.

Enables validating CSV structure, checking simple schemas, converting between CSV and JSON, sampling rows, and finding duplicate keys on local files. All processing stays local, so no user data is ever uploaded.

An MCP server for data reconciliation between MySQL and Snowflake, enabling LLM agents to validate data integrity during migrations and ETL processes.

Enables harnesses to record supplied GUI actions, inspect trajectory statistics, and export privacy-filtered datasets through MCP tools without executing device actions.

Enables AI agents to manage Cloudera Data Visualization resources including groups, users, roles, segments, workspaces, datasets, visuals, connections, and migrations via natural language.

Enables LLM agents to interact with a SQLMesh project, providing model metadata, column-level lineage, plan previews, audits/tests, and environment diffs, with optional apply/run actions.

Local-first Taiwan market evidence workbench for AI agents with persistent Watchlists, governed MCP, auditable evidence, and explicit execute-once retrieval. GitHub Repository URL

Enables AI assistants to manage CData Arc connectors, files, transactions, workspaces, and administrative actions through natural language interactions via the Arc Admin API.

A local MCP server that unifies advertising campaigns, spend, and analytics from platforms such as Google Ads, Yandex Direct, Telegram Ads, and Microsoft Advertising into one interface for an AI agent, storing data locally in SQLite with explicit confirmation for external actions.

Enables interaction with live Pokémon data through PokeAPI, providing comprehensive Pokémon information, battle calculations, moveset validation, and team analysis. Supports searching Pokémon and moves, calculating stats, checking type effectiveness, and analyzing team synergies with in-memory caching for improved performance.

Connects AI assistants to Axcient for real-time data retrieval and actions through natural conversation.

Connects AI assistants and developer tools to a workspace so they can inspect pipelines, schemas, metrics, profiles, audiences, documentation, and service status, and perform approved control-plane actions such as managing audiences and transformations or triggering syncs.

Connects agents to Consail over local stdio, providing read-only, allowlisted tools to inspect environments, catalogs, datasets, actions, and validate pipelines without exposing database credentials or secret APIs.

Enables managing orchestration workflows through tools for creating, preflighting, linting, and running sequences, checking instance status and outputs, sending signals, retrying instances, and inspecting DLQ and usage.

Enables AI agents to discover, search, and connect to over 1,679 live data sources through Pipeworx, including browsing packs, checking platform health, and measuring real-time data freshness.

Enables management of GitHub workflows, issue templates, and repository configuration for the Frontal MCP Server project through CI/CD pipelines and security automation.

Enables agents to answer analytics questions with provable correctness by resolving metrics through the semantic layer, tracing lineage, traversing knowledge graphs, checking freshness, and searching glossary definitions, while enforcing access controls and logging every call.

Connects an AI agent over Streamable HTTP to a workspace so it can read datasets, contracts, runs and receipts, and, when the owner permits, create fields and value sets, start runs and publish releases — while each deployed tool is also served as its own endpoint whose schema is generated from the owner's contracts. Every value comes with a receipt and safety exclusions are enforced in the query, with sensitive actions like approving review items or publishing past the accuracy gate reserved for a person.

Enables authorized DatumLabMHQ GitHub members to access the Datum data platform through MCP tools such as whoami, list_products, list_questions, ask, query, and health, without holding API keys.

A production-ready MCP server that provides comprehensive dbt project quality assessment for any GitHub repository, enabling AI agents to analyze dbt models, check metadata coverage, and map data lineage.

Turns natural language into data pipeline actions using six specialist agents that collaborate through MCP to build, validate, and monitor data infrastructure.

Exposes Iceberg-backed ontology objects, links, and actions as typed MCP tools for LLM agents, enabling governed data access and operations without raw SQL.

An MCP server that integrates with DataHub to track and score the reliability of AI agent actions. It provides a trust gateway that stamps every piece of context with its author's settled trust score.

Enables AI agents to inspect and query Elasticsearch clusters through six read-only tools for listing indices, reading mappings, searching with Query DSL or ES|QL, checking shards, and monitoring cluster health.

A versatile tool that enables querying and exporting data from multiple relational databases (MySQL, PostgreSQL, Oracle, SQLite, etc.) in read-only mode for data safety.

Enables converting invoice PDFs, scans, and photos into Excel/CSV spreadsheets, checking invoice arithmetic, extracting structured JSON data, and retrieving outputs from previously submitted documents through a hosted MCP service.

Enables AI agents and MCP clients to heuristically parse multi-column document dumps, aligned spacing, and CSV/TSV input, converting them into clean GitHub Flavored Markdown tables and typed JSON. Runs fully offline on the Python standard library with zero external dependencies, returning deterministic results without LLM round-trips.

Deterministic bank-statement parsing for AI agents: messy CSV/OFX exports to clean, categorized ledger rows. In-memory only, no storage, no external calls, no LLM in the loop.

Enables cross-database and domestic-database migration analysis by checking type mappings, dialect gaps, NULL and charset/collation semantics, object parity, SQL translation, consistency verification with checksums and sampling, effort estimation, and migration planning/reporting.

Enables AI agents and MCP clients to convert ASCII/OCR text dumps, aligned spacing, and CSV/TSV data into clean GitHub Flavored Markdown tables and typed JSON deterministically.

Enables coding agents to plan, dry-run, and execute table migrations between PostgreSQL, MySQL, Oracle, Snowflake, Databricks, SQLite and CSV, loading into staging tables and verifying row and column digests before promoting or rolling back. Every step is recorded in a tamper-evident evidence log and can be exported as a Markdown report.

Analyzes user-authorized CSV, JSON, or XLSX comment exports locally to generate risk signals, prioritized comments, evidence-backed actions, and reports while keeping data private.

Enables programmatic access to Tiingo financial data, including stock prices, news, forex, fundamentals, and corporate actions via the MCP protocol.