"Loop" matching MCP servers:

Enables autonomous data quality inspection and repair workflows. It scans DuckDB warehouses for anomalies, generates and verifies fixes in a dry-run copy, then applies them after validation, with full audit logging.

Postgres performance MCP server that analyzes hotspots, explains query plans, and detects dead-weight indexes. It aims to produce evidence-backed recommendations through a verification loop.

MCP server that lets AI coding agents (Claude Code, Cursor, Cline) audit Supabase projects for security misconfigurations AND apply the fixes — without leaving the agent. Tools: audit_project, list_findings, preview_fix (BEGIN/ROLLBACK safety), apply_fix (with confirmation), apply_all_fixes (transactional bulk). Closes the audit-fix loop entirely in the agent — other Supabase scanners only report.

Enables users to query enterprise sales data from a local SQLite database and investigate anomalies through an inline dashboard with human-in-the-loop review buttons.

MCP server that exposes ERP functionalities to AI agents, enabling read-only queries for sales orders and inventory, and human-in-the-loop purchase requisition creation and approval.

Enables secure enterprise integration by bridging SQL, financial, SCADA, and document backends to LLM agents with RBAC, DLP redaction, human-in-the-loop approvals, and OpenTelemetry tracing.

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

Drives Lumen's data-to-SQL-to-chart-to-report loop from any MCP client, supporting keyless SQL and charting or keyed agentic data analysis.

This MCP server provides fail-closed governance for DataHub data agents by implementing a Read Context → Restricted AST Gate → Tamper-Evident Proof Pack → Native DataHub Write-Back loop. It verifies dataset identity, schema, and lineage evidence against candidate contracts, binds claims to evidence, and appends VERIFIED or BLOCKED receipts back to DataHub through the official native update_description MCP mutation.

Enables LLM agents to read and modify company data from Notion and Supabase, with human-in-the-loop approval for all write and delete operations.

Enables stateless human-in-the-loop DevOps automation, demonstrating MCP SEP-2322 multi-round trip requests for emergency database migration authorization.

An enterprise-grade, deterministic multi-agent financial analytics engine that processes natural language financial queries, generates dialect-validated SQL, pulls live market data, and executes a self-healing verification loop before presenting synthesized results.

Enables conversational processing of Daily Status Reports (DSR) by extracting, validating, and storing data from JSON, DOCX, and XLSX files in IBM Cloud Object Storage. It provides a human-in-the-loop workflow for batch file processing and schema validation directly within MCP-compatible clients like Claude Desktop.

Enables natural language interaction with telecom network operations data via MCP, providing read-only insights and role-gated write operations such as bandwidth upgrades, with progress tracking and human-in-the-loop elicitation.

Enables agents to store conversational turns in a single SQLite store and recall relevant context through lexical, entity, dense, and recent views without an LLM in the recall loop. It exposes MCP tools for remembering, bulk ingest, recall, stats, and session deletion, with persistent indexes and zero-token maintenance.

A production-grade MCP server over Postgres, providing secure data operations with tenant isolation, exact-once mutations, loop-aware rate limiting, and a tamper-evident audit trail.

Enables AI agents to investigate incidents by safely querying PostgreSQL, triaging GitHub issues, retrieving ERP order data, and sending Slack alerts, with AST-validated SQL and human-in-the-loop safeguards.

Enables conversational management of Postgres-backed project data, off-limits rules, and hook styles, plus logging TikTok view counts and correlating performance by creative direction, hook style, or video-gen model. It also provides a sandbox to invoke Direction, Hook, and Script agents directly for prompt iteration without a full Telegram approval loop.