DevOps, Cloud & Databases MCP Servers

DevOps, Cloud & Databases mcp servers for AI agents and assistants.

8 results in DevOps, Cloud & Databases MCP Servers

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MCP AWS ServerMCP ServersAWS Labs' collection of specialized MCP servers covers documentation, infrastructure, data services and operational workflows.Open sourceSoftwareFree

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MCP Cloudflare ServerMCP ServersCloudflare's domain-specific remote MCP servers expose documentation, browser, container and other service-focused tools.Open sourceSoftwareFree

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MDMCP Docker ServerMCP ServersA community Python connector for managing Docker containers, images, networks and volumes and reading container logs or statistics.Open sourceSoftwareFree

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MCP PostgreSQL ServerMCP ServersAn archived PostgreSQL reference connector that exposes table schemas and executes queries inside read-only transactions.Open sourceSoftwareFree

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MCP Sentry ServerMCP ServersSentry's MCP service brings issues, errors, traces and debugging context into coding-assistant workflows.Open sourceSoftwareFree

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MCP SQLite ServerMCP ServersAn archived SQLite connector with schema inspection, read and write queries, table creation and a business-insights memo.Open sourceSoftwareFree

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MCP Vercel ServerMCP ServersVercel's hosted MCP server connects supported AI clients to documentation, projects, deployments and deployment logs.CommercialSoftwareFree

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Mmcp-server-kubernetesMCP ServersA community Kubernetes connector that uses kubeconfig and kubectl to inspect and manage cluster resources, with optional Helm integration.Open sourceSoftwareFree

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3 matching tools from this category

How to choose devops, cloud & databases mcp servers tools

DevOps, cloud and database MCP servers let AI clients inspect and operate infrastructure: querying databases, reading cloud account configuration, listing containers and pods, reviewing deployments and pulling error events. They turn routine operational lookups into tool calls an assistant can chain together. Because many of these tools can change production state, they carry more risk than documentation or research servers, and permission design matters more than feature count.

Read the guide

Who uses these tools

SREs querying cluster state from an assistant
Platform engineers reviewing cloud configuration
Database administrators running read-only diagnostics

Capabilities to compare

Read-only versus mutating tool sets
SQL query execution and schema introspection
Container and pod listing, logs and manifests
Cloud resource and deployment inspection
Error event and stack trace retrieval

Operating considerations

Credentials with production write access are high risk
Destructive commands need human confirmation
Query results may contain customer personal data
Rate limits and cost of cloud API calls
Audit logging of every tool invocation

Selection checklist

Start with a read-only role or replica connection and add write permissions only for specific, reviewed tools.
Check whether the server asks for confirmation before destructive actions like deleting resources or applying manifests.
Confirm which cloud regions, accounts or namespaces the credentials can reach and narrow them to one environment.
Verify that every tool call is logged with enough detail to reconstruct what the assistant changed.

What belongs in this category: A server is grouped here when its tools reach infrastructure, deployment platforms, container orchestrators, error tracking or databases, rather than source code editing, business applications or public web content.