Best DigitalOcean MCP Server MCP Server Alternatives 2026

AWS Labs maintains a monorepo of specialized, open-source MCP servers that bring AWS best practices directly into AI-assisted development workflows, spanning infrastructure, data, AI/ML, cost management, and healthcare/life-sciences domains. Rather than one monolithic server, the project ships dozens of focused servers you install individually depending on the task: the AWS Documentation MCP Server for real-time official docs and API references, dedicated servers for Terraform/CDK/CloudFormation infrastructure-as-code, container and serverless platforms (ECS, EKS, Lambda), SQL/NoSQL databases (DynamoDB, RDS, Aurora), search and analytics (OpenSearch), messaging (SQS/SNS), and cost/billing analysis. Most servers install via uvx with a package name like awslabs.aws-documentation-mcp-server, run locally over stdio, and use standard AWS credential chains (IAM roles, profiles, or access keys) rather than exposing raw account credentials to the model. AWS also now offers a managed, remote "AWS MCP Server" (in preview) that combines full API coverage with pre-built agent SOPs, syntactically validated API calls, and complete CloudTrail audit logging for teams that want centralized governance instead of running servers locally. The Getting Started with Kiro/Cursor/VS Code/Claude Code sections in the repo provide one-click install configs for each server, making it straightforward to wire up only the AWS services a given project actually touches.

The MCP server is free and open-source. AWS services use pay-as-you-go pricing. Free tier available for many services for 12 months. Costs vary by service usage.

Cloudflare ships two different things under this name. The mcp-server-cloudflare repo provides 16 remote, domain-specific MCP servers rather than one monolith — Documentation, Workers Bindings (storage/AI/compute primitives), Workers Builds, Observability (logs/analytics), Container sandboxes, Browser Rendering (fetch pages, convert to markdown, screenshots), Logpush health, AI Gateway (prompt/response search), AI Search, Audit Logs, DNS Analytics, Digital Experience Monitoring, Cloudflare One CASB, Radar, GraphQL analytics and the Agents SDK docs server, each on its own `*.mcp.cloudflare.com/mcp` hostname. Separately, the Cloudflare API MCP server at mcp.cloudflare.com/mcp (repo: cloudflare/mcp) exposes the whole 2,500+ endpoint Cloudflare API through just two tools, `search` and `execute`, using the Code Mode pattern — model-written JavaScript runs in an isolated Dynamic Worker, costing ~1,000 tokens of context against the ~1.17M an equivalent native-tool server would need. Pick a domain server when you want a readable, curated tool list for one product area; pick the API server for breadth or for endpoints nobody wrote a tool for. All endpoints are Streamable HTTP on `/mcp` and support the MCP 2026-07-28 spec; the historical `/sse` URLs remain as aliases for the same Streamable HTTP handler but no longer serve the deprecated HTTP+SSE transport, so clients pinned to SSE must switch. Auth is OAuth on connect, or a scoped Cloudflare API token as a bearer header for CI. Clients without native remote-MCP support bridge via `npx mcp-remote https://<subdomain>.mcp.cloudflare.com/mcp`.

The MCP server is free and open-source. Cloudflare: Generous free tier for Workers, KV, R2, D1. Workers Paid: $5/mo. Pro: $20/mo. Business: $200/mo.

Supabase MCP Server connects Cursor, Claude Code, Claude Desktop, Windsurf and other MCP clients to a Supabase project, and the first thing to know is that the personal access token setup most guides still describe is gone. Supabase now runs a hosted server at https://mcp.supabase.com/mcp using OAuth 2.1 with dynamic client registration — you add the URL, your client opens a browser, you pick the organization, and there is no PAT to mint or rotate. For Claude Code that is `claude mcp add --scope project --transport http supabase "https://mcp.supabase.com/mcp"` followed by `/mcp` in a plain terminal (not the IDE extension) to run the auth flow. Three URL query parameters do the real configuration work: `read_only=true` runs every statement as a read-only Postgres role, `project_ref=<id>` scopes the server to one project and drops the account-management tools entirely, and `features=` selects the tool groups. Those groups are database (list_tables, list_extensions, list_migrations, apply_migration, execute_sql), debugging (get_logs across API/Postgres/Edge Functions/Auth/Storage/Realtime, plus get_advisors for security and performance findings), development (get_project_url, get_publishable_keys, generate_typescript_types), Edge Functions (list, get, deploy), account management, docs search, experimental branching on paid plans, and storage — storage is the one group disabled by default. Running Supabase locally with the CLI exposes a reduced server at http://localhost:54321/mcp with no OAuth; self-hosted installs are similar. The npm package `@supabase/mcp-server-supabase` still exists for stdio clients and also exports `createToolSchemas()` so Vercel AI SDK users get typed tool inputs and outputs. Read Supabase's security best-practices page before pointing this at anything with production data — the mutating tools are real.

The MCP server is free and open-source. Supabase: Free tier (500MB, 50K monthly active users). Pro: $25/mo. Team: $599/mo. Enterprise: Custom.

The Neon MCP Server (neondatabase/mcp-server-neon) is Neon's official, open-source bridge between natural language and the Neon serverless Postgres platform. It translates conversational requests into Neon API and SQL calls, letting Claude, Cursor, VS Code, and other MCP clients create projects and branches, run queries, inspect schemas, and perform database migrations without hand-writing SQL or hitting the API directly. A standout capability is migration support built on Neon's branching: the server can spin up a branch, apply and test a schema change there, and only then merge it — so an assistant can safely run "add a created_at column to the users table" against an isolated copy first. The easiest setup is the remote hosted server at https://mcp.neon.tech/mcp, which supports both OAuth (no API key to manage) and API-key auth via the Authorization header; run `npx neon@latest init` for one-command configuration of Cursor, VS Code (Copilot), and Claude Code, or `npx add-mcp https://mcp.neon.tech/mcp` to register it across all detected editors. Requires Node.js 18+ and a free Neon account; if IP Allow is enabled, whitelist the mcp.neon.tech static IPs. Neon explicitly scopes this server to local development and IDE workflows — its README warns against production use since natural-language commands can execute powerful, irreversible database operations, so always review actions before authorizing them.

The MCP server is free and open-source. Neon: Free tier (0.5 GiB storage). Launch: $19/mo. Scale: $69/mo. Business: $700/mo.

The Vercel MCP server is a powerful Model Context Protocol integration that allows AI assistants like Claude, Cursor, and Cline to interact directly with your Vercel infrastructure. It exposes essential platform capabilities as AI-callable tools, meaning you can manage projects, trigger deployments, inspect build logs, and configure custom domains via natural language prompts. For frontend developers and DevOps teams working within the Vercel ecosystem, this eliminates the need to constantly context-switch between an IDE, terminal, and the Vercel dashboard. You can simply ask your AI agent to "check the status of the latest production deployment", "fetch the build logs for the staging environment and identify the Next.js hydration error", or "list all environment variables for the current project". By bridging the gap between your codebase and your hosting platform, the Vercel MCP server turns your AI assistant into an embedded DevOps engineer capable of diagnosing build failures and managing serverless deployments in real time. Vercel ships this as an official hosted (remote) MCP server at https://mcp.vercel.com — there is no package to install locally. Connect an MCP client to that URL and authenticate through the browser-based OAuth flow, which scopes access to the Vercel teams and projects your account can already reach rather than a long-lived Personal Access Token. For example, add it to Claude Code with `claude mcp add --transport http vercel https://mcp.vercel.com`, then complete the OAuth consent screen; the repo vercel/vercel-mcp-overview is the official public overview of this server, with full docs at vercel.com/docs/mcp/vercel-mcp.

The MCP server is free and open-source. Vercel: Hobby (free). Pro: $20/user/mo. Enterprise: Custom pricing.

The Netlify MCP Server is Netlify's official Model Context Protocol integration (netlify/netlify-mcp), acting as a bridge between AI coding agents and the Netlify API/CLI so they can create, build, deploy, and manage Netlify projects using natural-language prompts instead of manual dashboard clicks or hand-written API calls. Installed via `npx -y @netlify/mcp` (requires Node.js 22+, and the Netlify CLI installed globally for the best experience), it connects to Windsurf, Cursor, Claude Desktop/Code, VS Code Copilot, Cline, Warp, LM Studio, and any other MCP-compatible client, with one-click install links published for several of them. Core capabilities include creating and managing sites, triggering and monitoring deploys, modifying access controls and team permissions, installing or uninstalling Netlify extensions, fetching user/team/site metadata, and creating or updating environment variables and secrets. Authentication runs through the Netlify CLI's existing login session, so agents inherit whatever account/team access the developer already has rather than requiring a separately scoped token. Typical use: ask Claude to "deploy the current branch as a preview and give me the URL" or "add a STRIPE_SECRET_KEY environment variable to the production site" and the agent executes the equivalent Netlify CLI/API calls directly, which is useful for developers who want deploy and config management folded into an AI pair-programming workflow instead of context-switching to the Netlify dashboard.

The MCP server is free and open-source. Netlify: Free tier (100GB bandwidth). Pro: $19/mo. Business: $99/mo. Enterprise: Custom.

The Docker MCP server (ckreiling/mcp-server-docker) gives an AI assistant direct control of a Docker daemon over the Model Context Protocol: containers, images, networks and volumes, as tools rather than shell commands. It is the community server most people mean by "Docker MCP" — distinct from Docker’s own Docker MCP Gateway, which does not manage your containers at all but runs *other* MCP servers inside containers. If you want to ask Claude why the postgres container keeps restarting, you want this one; if you want a single secure endpoint in front of twenty catalog servers, you want the gateway. The tool surface is explicit and small enough to reason about: list_containers, create_container, run_container, recreate_container, start_container, fetch_container_logs, stop_container and remove_container for containers; list_images, pull_image, push_image, build_image and remove_image for images; list_networks / create_network / remove_network and list_volumes / create_volume / remove_volume for the rest. Two resource templates, docker://containers/{id}/logs and docker://containers/{id}/stats, let a client read logs and live stats by container ID or name without a tool call. It also ships a docker_compose prompt that puts the model into a plan-then-apply loop — you describe the containers you want under a project name, the model proposes a concise plan, and nothing runs until you approve it; reopening the prompt with the same project name re-reads the state of everything created under it, which is how you clean up after a lost chat. It runs on the Python Docker SDK’s from_env, so DOCKER_HOST applies: set ssh://user@host and the same server administers a remote engine. Two limits are deliberate and stated by the project — privileged options like --privileged and --cap-add/--cap-drop are not supported, and container configuration passes through the model, so no secrets belong in it.

The MCP server is free and open-source. Docker Desktop: Free for personal and small business. Pro: $5/mo. Team: $9/user/mo. Business: $24/user/mo.

The E2B MCP Server connects Claude Desktop and other MCP clients to E2B's cloud-hosted code sandboxes, giving an AI assistant a safe, isolated place to actually execute code instead of only reasoning about it. Each sandbox is a full Firecracker microVM with a real filesystem, package manager, and network access, so an agent can install dependencies, run scripts in Python, JavaScript, or other languages, read back stdout/stderr/exceptions, and iterate on failures the same way a human developer would in a REPL — a strong fit for data-analysis agents, coding assistants that need to verify generated code actually runs, and any workflow where letting the LLM execute arbitrary shell commands directly on the host would be unsafe. The server ships in two editions in the same repo: a JavaScript/Node package and a Python package, both wrapping the underlying E2B SDK and code-interpreter API. Install via `npx -y @e2b/mcp-server` (Node 18+) or the equivalent Python entry point, then set an E2B_API_KEY environment variable from the E2B dashboard; a Smithery one-click installer (`npx @smithery/cli install e2b --client claude`) is also documented. IMPORTANT: this repository is explicitly marked deprecated/no-longer-actively-maintained by E2B (last meaningful update several months ago) — it still installs and runs, and remains the highest-starred E2B-authored MCP entry point, but teams building new integrations should check E2B's current docs for whether a newer first-party server or their Code Interpreter SDK directly is now the recommended path before committing to long-term production use.

The MCP server is free and open-source. E2B: Free tier with limited sandbox hours. Pro: $45/mo. Enterprise: Custom pricing.

The Kubernetes MCP server (mcp-server-kubernetes, built by Flux159) brings cluster management capabilities into AI assistant workflows, letting developers and platform engineers query and manage Kubernetes resources through natural-language interactions with Claude, Cursor, and other MCP-compatible clients. It loads your existing kubeconfig automatically, so it works with any cluster — local minikube and kind setups, Amazon EKS, Google GKE, Azure AKS, or on-premises deployments — with no separate credential setup required. Core tools exposed by the server include: listing pods, deployments, services, and namespaces; describing individual resources and their status; fetching pod logs for debugging; applying and updating manifests; scaling deployments; checking rollout status and history; and querying resource utilization and cluster events. A built-in non-destructive mode can disable delete/scale-down operations entirely, making it safe to point at production clusters for read-only diagnostics. DevOps engineers use it to debug failing deployments by asking Claude to inspect pod logs and recent events, identify resource constraints causing OOMKilled pods, or summarize the current state of a namespace before a production release. For SREs responding to incidents, it enables rapid triage through conversational commands — no memorizing kubectl flags or switching terminal windows mid-incident — and optional OpenTelemetry integration adds observability into what the AI agent actually did against the cluster. Install with: `npx mcp-server-kubernetes`. Pairs well with the GitHub MCP server for full GitOps review workflows.

Free and open-source MCP server. Kubernetes is free and open-source. Cloud-managed K8s (EKS, GKE, AKS) have cluster management fees starting around $0.10/hr.

live Terraform Registry data — provider schemas, module inputs and outputs, policy libraries — plus full HCP Terraform and Terraform Enterprise workspace management, so an AI assistant writes HCL against the real resource arguments instead of inventing attribute names from memory. HashiCorp distributes it as a Go binary and a Docker image, not as an npm package: `@hashicorp/terraform-mcp-server` returns 404 on the npm registry, so npx configurations copied from older write-ups cannot work. The supported install is the hashicorp/terraform-mcp-server image on Docker Hub (v1.1.0, released 2026-07-14), run with `docker run -i --rm hashicorp/terraform-mcp-server:1.1.0`, or the release binary invoked as `terraform-mcp-server stdio`. Both transports are supported: stdio for local clients, and streamable-http via `terraform-mcp-server streamable-http --transport-port 8080 --mcp-endpoint /mcp` for shared deployments. Registry lookups need no credentials at all — only the HCP Terraform and Terraform Enterprise tools do, via TFE_TOKEN plus TFE_ADDRESS (which must include the protocol, e.g. https://app.terraform.io, and in streamable-http mode can only be set as an environment variable, never supplied by a client header). Two flags matter before you expose it to a team: ENABLE_TF_OPERATIONS is false by default and gates the tools that mutate infrastructure, and the HTTP server defaults to strict CORS with 10:20 global and 5:10 per-session rate limits, an MCP_ORGANIZATION_ALLOWLIST for restricting which HCP organisations can connect, and required MCP_TLS_CERT_FILE / MCP_TLS_KEY_FILE for any non-localhost bind. Server instructions live in cmd/terraform-mcp-server/instructions.md and are meant to be replaced with your own conventions if the default answers do not match how your organisation writes Terraform.

The MCP server is free and open-source. Terraform CLI: Free and open-source. Terraform Cloud: Free tier (up to 500 resources). Team: $20/user/mo. Business: Custom.