"sql server" matching MCP servers:

Enables users to inspect database schemas, validate SQL syntax, and execute queries against PostgreSQL and Apache Hive through standardized Model Context Protocol tools for safe query generation and validation.

A bridge that allows AI assistants like Claude to directly query and explore Microsoft SQL Server databases without requiring coding experience.

Enables secure, privacy-first SQL database interaction via natural language, using local LLM and PostgreSQL with dynamic tool-calling.

Enables secure, read-only analytical querying of financial data through natural language, with built-in SQL injection defense and automatic query repair.

Protects AI agents from threats like prompt injection, jailbreaks, and SQL injection through a multi-layer scanning pipeline. It also enables PII redaction and rehydration to ensure data privacy during LLM interactions.

A tool that converts natural-language requests into full, deployable, execution-verified Apache Flink SQL jobs by orchestrating an LLM to draft specs, generate Flink SQL, and verify correctness against real Flink clusters and streaming semantics—including windowed aggregates, joins, Kafka grounding, and Deployable pipelines via the generate_flink_job, ground_kafka_topic, and deploy_to_kafka tools.

An MCP server that enables a supervising OpenAI Codex agent to delegate bounded implementation tasks to isolated worker threads with enforced file scopes and verification, optionally running in parallel git worktrees.

A pre-action risk gate for AI agents. Your agent calls the forecast tool before any irreversible action — send email, run SQL, make a payment, delete a file — and gets a risk score (0–100) and a GO / CONFIRM / STOP verdict in a few seconds.

It gives AI agents a ranked, token-budgeted digest of what changed since their last cursor, with drill-down handles and cursor acknowledgement, read from a local database of file and SQL source changes. It enables agents to act on current business state without re-reading and diffing everything themselves.

Enables sub-millisecond pre-execution safety filtering for AI agent tool calls, blocking destructive shell commands, dangerous SQL mutations, credential access, SSRF, and scope-creep or prompt-injection patterns before they execute, with optional fail-closed transparent proxy wrapping for any MCP server.

A provably read-only SQL layer for AI agents (MCP) and weekly reports — over the database behind an ERP (Logo Tiger, Netsis, Mikro). Statement-checking guard, measured against 28 attacks.

Enables LLM-driven tool execution with policy-gated authorization, deterministic verification, and replay for radiographic measurement and SQL repair tasks.

Enables AI agents to rehearse risky actions — SQL, shell, git, Terraform, cloud and SaaS operations — against simulations of the real system before executing them, so they can see the consequences first. Returns a consequence report with a safe, confirm or block verdict, blast radius, reversibility and evidence, without ever executing the action itself.

MCP server that coordinates multiple AI specialists (SQL database, document RAG, and web research) using LangGraph, routing each question to the relevant agents and synthesizing their responses into a single answer with honest confidence flags.

Enables enterprise multi-agent decision workflows that expose 8+ MCP tools such as SQL query, web search, Python sandbox, RAG, file access, data cleaning, chart generation, and HTTP calls, orchestrated with LangGraph, streaming output, and human-in-the-loop approvals.

A tool-augmented LLM system for the full PDDL planning pipeline, improving reliability without domain-specific training.

Enables an event-driven operations agent to query internal SQL, GitHub, Slack, Jira, documentation, and Kubernetes telemetry through 28 RBAC-gated MCP tools, with shared memory for playbook retrieval and incident workflows.

Enables AI agents to maintain persistent personal memory and portable skills across MCP-compliant clients, with hybrid semantic recall and deterministic SQL analytics.

Enables autonomous infrastructure health management by exposing tools for retrieving system logs, querying a knowledge base, executing SQL analytics, and simulating system commands, all integrated into an AI-driven incident response workflow.