MCP Server: CTO Playbook

If this is you

CTOs, Chief Architects, Heads of Platform and SRE. You own the reference architecture and the developer experience. Your choices decide build versus buy, transport, identity, policy, observability, and operations.

TL;DR

MCP servers expose tools and resources to agents over stdio or HTTP, then coordinate multi-step plans into downstream API calls. They reduce integration sprawl and lift policy and tracing into the mesh. You will operate a control plane, so design it like a production service with SLOs, budgets, and audits.

Problems this solves

• Fragile glue code
  Each agent or IDE integration creates new code paths. MCP consolidates invocation, schemas, and policy into one service.
• No standard discovery
  Engineers guess at tool names and parameters. The MCP catalog declares tools with schemas, descriptions, and examples, which unlocks automation and validation.
• Limited mesh traces
  Edge logs show ingress and egress only. MCP emits spans for agent to MCP to API, with identity and policy attributes on each hop.
• Host divergence
  Each host displays tools differently. Conformance tests and simple tool shapes reduce breakage across Claude, VS Code, Cursor, Continue, and Cline.

Reference architecture decisions

• Transport
  Stdio for local and air-gapped, HTTP for managed services. Keep an adapter layer so you can switch transports without rewriting tools.
• Identity
  Short lived tokens issued per agent and per tool. Scopes bind to purpose and resource. Elevation is logged with approver and reason.
• Policy
  Allow, deny, redact at call time. Region routing by data class. Vendor allow lists for egress. Simple policy language with tests and staging.
• Tracing
  OpenTelemetry with W3C trace context, span names like agent.plan and mcp.tool.invoke. Custom attributes for actor.id, actor.type, scope.grants, policy.decision.
• Evidence
  Signed, append-only logs. Evidence artifacts linked to spans. Export to SIEM with retention policy and compression.
• Operations
  Rate limits per principal and per tool, concurrency caps, queues, backoff, circuit breakers, idempotency keys for write tools.

Build versus buy matrix

SRE checklist

• Timeouts and retries with exponential backoff, per tool and per downstream service
• Rate limits by agent and tool, with clear 429 behavior and headers
• Concurrency caps, queue backpressure, and shed-load strategies
• Idempotency keys for mutating tools, ideally set by the MCP layer
• Budgets for LLM calls and high frequency tools, alert when approaching limits
• Health checks, structured JSON logs, crash-only process restarts, and automated rollbacks

Developer experience guardrails

• Consistent naming, stable input and output schemas, examples in the catalog
• Golden flows for critical tasks, with adversarial prompts and chain exploits in CI
• Conformance tests per host, verify that tool discovery and parameters render correctly
• A portable dev container that starts the default servers, loads policy, and tests locally

KPIs and SLOs

• Tool onboarding lead time, target under two days
• Failed plan rate divided by policy blocks versus system errors, aim to reduce system errors week over week
• P95 tool latency and error budget burn per tool group, publish weekly
• Cost per workflow, tracked against a monthly cap, anomaly detection and remediation time
• Mean time to detect and fix plan failures, with top three causes per month

First 90 days

• Days 0 to 30
  Stand up a minimal server. Choose stdio transport first. Wire traces and logs. Connect to one host, for example Claude Desktop or VS Code.
• Days 31 to 60
  Introduce scoped identities and short TTL tokens. Add policy for allow, deny, redact. Create golden flows and adversarial tests, run in CI. Enable region routing and vendor allow lists.
• Days 61 to 90
  Add budgets and limits. Publish a stable catalog with owners and versioning. Create host conformance tests. Publish SLOs and weekly reports.

Build vs buy: what to ask

• How do you propagate non-human identity through spans, and how is it verified
• Which policy language is supported, what redaction modes exist, and how is routing enforced
• How do you simulate prompt injection and chain exploits in tests, and how are results surfaced
• What is the cold start profile, steady state CPU and memory, and scaling model
• How is evidence signed, stored, and exported, and what integrations exist with SIEM and ticketing

How Levo can help

Levo provides runtime capture with pre-encryption visibility, identity stitching for agent to MCP to API, inline policy for allow, deny, redact, region routing and vendor allow lists, signed evidence with retention, continuous exploit-aware tests, and cost guards. You get a faster path to a production grade MCP layer and better developer ergonomics.

Interested to see how this looks in practice: Book a demo.

Conclusion & Key Takeaways

Bottom line
An MCP server is a control plane you operate. Design it like any production service: explicit contracts, strong identity, policy in the path, tracing by default, and SRE guardrails.

Takeaways

• Choose transport deliberately (stdio first, HTTP where needed) behind an adapter layer.
• Standardize schemas and naming so tools are predictable across hosts and teams.
• Propagate non-human identity through OpenTelemetry with W3C context and custom attributes.
• Put policy in the mesh with unit tests, staging, and golden flows in CI.
• Run with SRE rigor: timeouts, retries, backoff, circuit breakers, idempotency keys, and rate limits.
• Publish SLOs and KPIs: tool onboarding lead time, failed plan rate, P95 latency, budget adherence.

Build vs buy closing lens
Build when deep customization and platform investment are strategic. Buy when time-to-value, mesh visibility, inline DLP, and signed evidence are needed quickly.

Related: Learn how Levo brings mesh visibility, identity-first governance, inline guardrails, and continuous testing to MCP deployments Levo MCP server use case