Model Context Protocol (MCP) is a standardised way for AI agents and apps to fetch, share, and govern context (like history, preferences, roles, and permissions) from your organisation’s data sources, so automated workflows stay consistent, secure, and relevant.
- What it is: An open, standardised protocol (server + clients) to manage AI context across agents and systems.
- Why it matters: Prevents context loss, improves consistency, enables scale, and supports audit/compliance.
- Who needs it: Teams building chatbots, retrieval agents, assistants, or multi-agent workflows touching sensitive or complex data.
- How to start: Define context needs → standardise formats → set exchange rules → integrate → monitor/audit → iterate.
What Is Model Context Protocol?
Model Context Protocol is a method for AI systems to manage and exchange contextual data within a workflow. MCP is typically implemented as a server that exposes standardised operations; AI clients (agents, copilots, apps) retrieve and update context through that server. The goal is to keep responses consistent and relevant as tasks move between models, tools, and services.
Where MCP Helps
-
Chat & virtual agents: Maintain conversation history, preferences, roles, and guardrails.
-
Knowledge retrieval agents: Share query traces, filters, and access scopes.
-
Workflow automation: Pass state and decisions across tasks without losing context.
How MCP Works (In Plain English)
MCP synchronises the data AI needs to “know”: user identity, roles, consent, conversation history, preferences, session rules, and relevant resource links. Architects define schemas, metadata, and exchange rules so that every agent gets the right slice of context, no more, no less.
MCP can also encode operational policies, like:
- When to reset or merge context
- How to expire or clear context (privacy/consent)
- What to log and audit for compliance
Why MCP Matters
Without MCP, AI agents often lose track of prior exchanges, create inconsistent outputs, and risk misusing data. MCP gives teams a repeatable standard for collaboration across agents and apps so they can scale with governance.
Key advantages
- Consistency: Shared formats reduce ambiguity and errors.
- Better UX: Persistent, relevant context → more helpful interactions.
- Interoperability: Diverse models/tools use a single method to access data.
- Scalability: New agents and apps plug into the same protocol.
- Security & Privacy: Clear rules for access, masking, retention, and audit.
MCP vs. RAG (Retrieval-Augmented Generation)
| Topic | MCP | RAG |
|---|---|---|
| Purpose | Governs how context is shared/maintained across agents and systems | Improves answers by retrieving external knowledge at generation time |
| Scope | Protocol for state, roles, history, policies | Technique for augmenting model outputs with retrieved data |
| Dependency | RAG can use MCP-managed context | Can run without MCP, but benefits from it |
| Outcome | Reliable, governed workflows | More accurate, up-to-date responses |
Bottom line: RAG boosts answer quality; MCP ensures the right context is consistently available and governed.
Implementation Steps
- Define context needs
Identify what each agent requires: conversation history, roles, consent, session timeouts, preferences, resource pointers, and guardrails. - Standardise data structures
Use self-documenting JSON with metadata (timestamps, source, sensitivity, retention). - Set exchange rules
Specify when to update, overwrite, merge, retain, or clear context; include triggers (events, timeouts, consent changes). - Integrate with agents & apps
Add MCP client calls for fetch, write, validate; enforce scopes and least-privilege access. - Audit & monitor
Track usage, latency, accuracy, and policy adherence; keep immutable logs for regulated environments. - Refine continuously
Gather feedback from product, support, and risk/compliance; evolve schemas and policies as workflows grow.
Governance, Security, and Compliance Considerations
- Access control: Role-/attribute-based access to context segments.
- Data minimisation: Only provide the context needed for a task.
- Redaction & masking: Strip PII or sensitive fields where not required.
- Retention & deletion: Enforce time-bound policies and user-driven erasure.
- Auditability: Log who accessed what, when, and why (and whether they wrote back).
- Testing & drift checks: Validate that agents interpret context as intended.
KPIs to Track After You Implement MCP
- Resolution rate and first-contact resolution for support agents
- Context error rate (missing/incorrect context incidents)
- Time-to-answer and handoff success in multi-agent flows
- Policy violations (unauthorised reads/writes)
- Latency/availability of MCP server under load
FAQs
What is MCP and how does it work?
MCP is a set of rules, formats, and practices that AI agents use to share context (history, preferences, roles). It standardizes how this data is exchanged, updated, or deleted, helping sustain accurate, relevant operations across tools and models.
What’s the difference between MCP and RAG?
MCP governs context exchange and state management across AI components. RAG improves model outputs by retrieving external knowledge. RAG benefits from MCP, but MCP is the layer that defines and controls context.
Do I need MCP if I already use vector databases and RAG?
Likely yes. Vector/RAG improves retrieval quality, but MCP ensures every agent gets the right governed context (identity, roles, consent, history) consistently.
Is MCP only for chatbots?
No. It’s valuable anywhere context spans multiple steps/agents—customer support, underwriting, logistics, DevOps copilots, and more.
Implementing Model Context Protocol (MCP) isn’t just an upgrade in technical architecture, it’s a strategic step towards building AI-powered workflows that are consistent, compliant, and ready to scale. By standardising how context is managed and shared between agents, your organisation can reduce errors, safeguard sensitive data, and deliver truly seamless automated experiences across every channel.
At Project Salsa, we’re committed to helping New Zealand businesses harness the full power of digital transformation with solutions that address real-world integration and governance needs. If you’re looking to simplify secure context management and unlock new levels of automation with Oracle NetSuite, our team is here to guide you every step of the way.
Curious to see how easy AI MCP Integration can be with NetSuite?
