Which MCP Server is for me?

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.com mcpevals.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.com docs.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.

Read the Medium article

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