Why Organisations Adopt MCP
Without a protocol, prompts and context sprawl across repos, notebooks, and JSON files—making them hard to govern, version, and reuse across models or clouds.
Prompt Sprawl
Duplicate prompts in every micro‑service and notebook
No Single Source
No single source of truth for grounding data & tool schemas
Risk & Drift
Risk of prompt drift, hidden PII leaks, and cost spikes
Vendor Lock-in
Tight coupling to a single LLM vendor slows innovation
MCP in the Real World
Multi‑Cloud Model Routing
Dynamically swap OpenAI, DBRX, or on‑prem models by pointing agents at the same MCP capsule.
Regulated Data Guardrails
Attach encryption, redaction, and audit policies to the capsule—enforced at runtime.
Prompt A/B Testing at Scale
Blue‑green deploy new capsule versions; auto‑roll back on eval regressions.
Marketplace Distribution
Publish reusable MCP capsules to partners & internal teams via private registry.
Reference MCP Architecture
A standardized protocol for packaging, serving, and managing AI context across any LLM platform, ensuring portability and governance at scale.
Capsule Registry
Signed, versioned .mcp files stored in S3, GCS, or Git—indexed with metadata APIs.
MCP Server
REST & gRPC endpoints expose /resolve & /eval operations; plug‑ins for RAG & tool execution.
Agent Runtime
SDK fetches capsule, injects user prompt, routes to selected LLM, logs metrics to observability stack.
PipeIQ MCP Services
- ✅Greenfield MCP Server Build — Rust or Go micro‑service with plug‑in architecture, deployed on AWS, Azure, or GCP
- ✅3rd‑Party MCP Integration — Adapter layers for Salesforce Agentforce, LangSmith, or PromptLayer MCP endpoints
- ✅Capsule Migration Factory — Automated scripts to convert existing prompts, RAG configs, and tool specs into MCP format
- ✅Eval & Cost Optimisation — Hidden test suites, latency budgets, and model‑routing rules wired into CI/CD
- ✅Governance & Compliance — PII masking, key rotation, and signed capsule workflows with audit trails
- ✅Enablement & COE — Developer training, capsule style guides, and lifecycle playbooks
Standardise & Scale Your AI Context
Book a discovery call to craft a Model Context Protocol strategy—whether you're building your own server or leveraging 3rd‑party MCP.
