Three MCP Server Selection Criteria the Medium Article Presents
In choosing an MCP server you’re really selecting an execution model, language ecosystem, and transport strategy that will shape how your agents scale, secure themselves, and plug into the rest of your stack. Below are three questions that surface the trade-offs, with scenario-based guidance drawn from current FastMCP, Spring AI MCP, and Node.js implementations.
🔌 1. Where will the server run—on the user’s laptop, inside a pod, or across a fleet?
| Deployment style | Transport sweet spot | Framework fit | When it shines | Watch-outs |
|---|---|---|---|---|
| Local-first desktop helper (e.g., Claude Desktop, IDE plug-in) | STDIO (fires a short-lived subprocess)github.com | FastMCP (Python)gofastmcp.com Node.js SDK (JS/TS)npmjs.com | Zero infra; instant access to local file system; great for power-user tools | No horizontal scaling; must sandbox file writes; Windows/macOS permission quirks with Claude Desktopnpmjs.com |
| Single-tenant web service / POC | Streamable HTTP (stateless requests; resumable streams)mcp-framework.com | FastMCP’s run(transport="streamable-http") call | Quick SaaS demos; serverless endpoints | You add auth, rate-limits, & state store |
| Enterprise microservice | WebFlux SSE (reactive), migrating to Streamable HTTP for multi-node resiliencedocs.spring.io | Spring AI MCP (Java)docs.spring.io | Tight Spring Boot stack, JDBC, OAuth2 resource-server patterns | Original SSE needs sticky sessions and struggles behind LB/firewallsmcpevals.io; plan migration |
Scenario analysis
If you need an agent that edits a designer’s local Figma files, STDIO + Node.js is unbeatable—Claude spawns the process only while the user is active, and the JavaScript ecosystem makes file-system APIs trivial.
If you’re rolling out a “context-as-a-service” backend for 20 LLM teams, pick Streamable HTTP and either FastMCP (Python async) or Spring AI WebFlux (Java) so you avoid SSE reconnect pain and can add Istio or API Gateway in front.
☕ 2. Which language runtime lines up with your team and compliance rules?
| Vector | FastMCP (Python) | Spring AI MCP (Java) | Node.js SDK / mcp-node |
|---|---|---|---|
| Ecosystem leverage | Rich data-science libs; Pydantic auto-schemas | Spring Security, Actuator, Observability | npm universe; ES modules; desktop bundlers |
| Async story | async/await baked in; uvicorn-style throughput | Reactive WebFlux for SSE/HTTP streams | Event loop excels at thousands of tool invocations |
| Security primitives | Bring-your-own OAuth middleware; Python decorators | First-class OAuth2 Resource-Server configbaeldung.com | mcp-node permission pop-ups for file access |
| Prod scaling | Needs Redis/session store for sticky state | Mature JVM clustering; Kubernetes CRDs | Best behind a CDN or desktop—server farms require extra worker coordination |
Scenario analysis
Fin-tech org with strict JVM governance: Spring AI MCP lets you reuse existing OAuth2 and auditing filters; devs feel at home writing @Tool methods that auto-wire into the same observability stackdocs.spring.io.
Startup shipping daily prototypes: FastMCP’s decorator pattern turns a notebook snippet into a shareable server in minutes; its auto-schema from type-hints eliminates boilerplategofastmcp.com.
VS Code extension author: Node.js SDK means you can ship a single .vsix that starts an MCP server beside the editor, accessing code files locally without extra daemonsnpmjs.com.
🛡️ 3. How will you handle state, security, and future protocol changes?
State & scaling
- Python + Redis: FastMCP keeps session objects in RAM; add a Redis or Dynamo layer when you run multiple replicas.
- Spring session stores: Spring AI can back state with JDBC or Hazelcast so WebFlux nodes share context tokensdocs.spring.io.
- Stateless Node.js: Lean on Streamable HTTP’s session-ID headers to rebuild context per requestmodelcontextprotocol.io.
Security layers
- Input validation: Use Pydantic (Python), Bean Validation (Java), or Zod (TS) so malicious arguments never reach shell callsnpmjs.com.
- OAuth2 flows: Spring AI already supports delegated OAuth2 for MCP; FastMCP relies on upcoming client-credentials PRs; Node.js uses Passport or custom JWT checks.
- Transport hardening: Streamable HTTP avoids the long-lived sockets that make SSE vulnerable to proxy drops and token leakagemcp-framework.commcpevals.io.
Future-proofing
- MCP spec deprecates SSE in favour of Streamable HTTP (2025-03-26 draft)mcp-framework.com. Budget time to swap transports.
- Type-schema evolution: keep tool parameters backward-compatible; FastMCP & Spring generate JSON Schema automatically for yougithub.comdocs.spring.io.
- Track the official SDKs—FastMCP 2.0 refactors to match low-level Python API, and the TypeScript SDK sees weekly releasesgithub.com.
Scenario analysis
Need HIPAA compliance tomorrow? Spring AI + OAuth2 resource-server lets you plug into Okta and log every JWT scope decision without custom code.
Launching a consumer desktop beta? Node.js STDIO keeps all data on-device, sidestepping GDPR cross-border issues, and you can gate every dangerous action behind a UI “Allow?” modal.
Scaling a SaaS to 1000 rps? Python plus Streamable HTTP behind an ASGI server scales horizontally once session state is externalized; monitor “Mcp-Session-Id” churn to spot lost affinitymodelcontextprotocol.io.
The bottom line
Pick transport first, because it dictates scaling patterns; match language runtime to the skill set and security controls you already trust; and design for stateless, scope-checked calls so your agents survive the protocol’s rapid evolution. FastMCP gives Python speed and decorator joy; Spring AI delivers enterprise-grade Java rigor; Node.js excels at event-driven local or serverless tasks. Anchor those choices in Streamable HTTP, strong OAuth scopes, and external state stores, and your MCP agents will be ready for both today’s prototypes and tomorrow’s production workloads.
