Group APIs by task
Endpoints are grouped by user outcome, data ownership and impact. This creates smaller and clearer tools.
Design tool contracts
Name, description, input schema, output, errors, permissions and side effects are documented and tested explicitly.
Release incrementally
Start with a small number of stable capabilities. Usage and failure patterns show which extensions are genuinely required.
The precise engineering question
The relevant question is how technical endpoints become bounded, domain-oriented capabilities rather than an unsafe one-to-one conversion. MCP standardizes discovery and invocation between an AI host and a server; it does not create travel inventory, contractual validity, authorization policy or transaction recovery.
The analysis must therefore name the host, client connection, domain server, authorization boundary and system of record. It must also state which data is informative, which state is revalidated and which action creates a legal or financial commitment.
Travel case study
Consider curating several offer, fare-rule and order endpoints into separate read, prepare and execute tools with stable schemas and explicit side effects. Every result carries supplier provenance, retrieval time, currency, material conditions and a validity boundary. The client may summarize these facts, but it must not convert an expired or indicative offer into confirmed availability.
Search, quote, hold, book, ticket, pay, exchange and cancel are separate risk classes. Binding execution follows a fresh backend check, an approval tied to the exact terms and an idempotency or deduplication mechanism.
Architecture and security controls
The host limits visible capabilities and explains actions; the MCP server validates schema, identity, tenant and object rights; the downstream system enforces its own business rules. No model decision substitutes for these independently enforceable controls.
Remote servers additionally require protected-resource metadata, correct token audience, transport security, origin validation, bounded egress and operational revocation. Tool results remain untrusted content and cannot acquire system authority through embedded instructions.
Failure analysis
A representative failure is publishing every endpoint, flattening supplier error semantics, passing broad backend tokens through the server or changing descriptions without regression tests. Tests cover malformed input, denied access, stale state, supplier timeout, partial completion and duplicate delivery.
Observability separates model selection, client policy, MCP processing and supplier-system outcomes using a shared correlation identifier. Sensitive inputs are minimized or redacted rather than copied wholesale into logs.
Production evidence
Evidence includes version negotiation, capability compatibility, contract and negative tests, task correctness, inappropriate-call rate, user correction, latency, cost and recovery time. A successful demo is not production evidence.
The conclusion remains conditional: MCP is justified when reusable capability discovery and controlled model interaction add measurable value. A direct API or deterministic workflow remains preferable where those properties are unnecessary.
Primary sources and academic context
This page follows the versioned MCP specification and separates normative requirements from architectural recommendations.