MCP Server Development Services
MCP Servers That Actually Run in Production
Build secure MCP servers that connect AI agents to your business systems with authentication, permissions, observability, and audit-ready controls.
MCP Server Architecture
Client / Host Connection
Tool & Resource Discovery
Auth & Permission Scoping
System & Data Integration
Years Building Enterprise AI
Customers and End Clients
100+
4–12 Wks
Trusted by Enterprise Teams in Regulated Industries
Trusted by Enterprise Teams in Regulated Industries
What It Is
What Is MCP, and What Does an MCP Server Do?
MCP is the standard way to connect AI assistants to your business systems.
Who made it. Anthropic released the Model Context Protocol as an open standard in November 2024. OpenAI and Google have since adopted it.
What it replaces. MCP replaces one-off custom integrations. Before MCP, connecting AI to a new system meant writing custom code for that specific connection. With MCP, you build a server once, and any MCP-compatible AI can use it. The MCP server is what RTS builds.
What it isn’t. MCP is not a product. It is a standard. An MCP server is what follows the standard.
Why it matters now. A system built to the MCP standard today works with any MCP-compatible AI assistant, now and as the landscape changes.
What an MCP Server Gives an AI to Work With
Every MCP server exposes some combination of three things:
Tools
Actions the AI can take — look up a claim status, send an email, update a record. The AI decides when to call a tool; the user approves the action.
Resources
Information the AI can read — a customer record, a policy PDF, a sales report. Read-only context the AI uses to answer questions or make decisions.
Prompts
Pre-written templates a user can trigger — “Draft a denial letter,” “Summarize last quarter.” The user picks the template; the AI fills it in using live data.
What a Tool Looks Like in Real Code
@mcp.tool()
async def lookup_claim_status(claim_id: str) -> dict:
"""Returns the status of a claim. Checks the user has permission first."""
return await claims_service.get(claim_id, user=ctx.user)Python example — illustrative.
The Hard Truth
Why MCP Servers Fail in Production
Most MCP guides end at ‘hello world.’ Production is where the real problems start. These are the six patterns RTS encounters most often.
01
Stuck in Demo Mode
Your team built a working MCP demo. Six months later it still isn’t connected to the real claims database. Tutorial code doesn’t survive real authentication, real permissions, or real load.
02
Security Was an Afterthought
The server uses one shared key to access your systems. There’s no way to tell which user asked the AI to do what. Works in dev; fails the first security review.
03
Flying Blind in Production
The server is live, but nobody knows which tool calls are slow, failing, or running up cost. The first warning sign is a customer complaint.
04
Locked Into One AI Provider
The integration was built directly against one LLM provider. Switching means rewriting everything — the whole point of MCP was to avoid this.
05
One Customer Sees Another's Data
The server returns whatever the AI asks for, but the check on who’s allowed to see what lives somewhere else. One badly-scoped tool, one cross-tenant leak, and compliance is involved.
06
Still Figuring Out Where to Start?
MCP servers are the standard integration layer for AI Automation and a critical control surface for AI Governance. If you’re deciding which comes first, start there.
What We Build
What RTS Delivers
Six service areas. Each is a complete deliverable — working as a full program or as a targeted engagement where a specific gap exists.
01
MCP Readiness Assessment
A 2-week look at where you are. We map your AI systems, target integrations, and auth requirements. Output: a prioritized build-vs-buy recommendation and architecture decision doc.
02
Custom MCP Server Development
A production MCP server built for your specific systems — claims, contracts, CRM, ERP, fleet, property data. Written in your preferred language. Ready for real traffic from day one.
03
Authentication and Authorization
The security layer most tutorials skip. Per-user auth, role-based access, tenant separation, full audit logging. SOC 2, HIPAA, and FedRAMP-aware patterns where required.
04
Multi-Tenant MCP Architecture
MCP servers for SaaS products where every customer’s data has to stay separate. Access rules enforced at the tool layer, not just the application layer.
05
MCP Observability and Operations
Monitoring, error tracking, and cost dashboards wired into your existing observability tools — Datadog, Grafana, Splunk, or whatever your team already runs.
06
MCP Server Audit and Hardening
Already built one in-house? We review for security gaps, performance issues, and multi-tenant safety. We tell you what’s ready and fix what’s not.
How the Architecture Works
The AI assistant never talks to your systems directly. It goes through the MCP server.
The MCP server is the control point. It handles authentication (who is asking), authorization (what they are allowed to do), the actual call to your system, and the audit log entry for everything that happened. Your internal systems stay behind that layer.
AI Assistant
Claude, ChatGPT, Cursor, your app
MCP Client
Built into the AI assistant
MCP Server (RTS-built)
Auth | Tools | Resources | Audit log
Your Systems
CRM, ERP, claims, contracts, databases
Industry Playbooks
MCP Servers for Regulated Industries
MCP servers for regulated industries require more than connectivity — the auth model, audit trail, and compliance controls the industry demands. RTS has 14+ years in exactly these four verticals.
Financial Services
- Loan origination MCP — credit scoring, document classification, decisioning
- Fraud detection MCP — real-time signal aggregation across systems
- AML/KYC MCP — entity resolution, sanctions screening, audit-ready logging
- Customer-360 MCP — unified view across CRM, transactions, support
- Compliance reporting MCP — SR 11-7 evidence and audit packages
client outcome
40%
Improvement in forecast accuracy, plus 35% better resource optimization — asset management & financial advisory client
Insurance
- Claims status MCP — status, notes, and history lookup across claims systems
- Underwriting data MCP — risk data, policy history, third-party feed access
- Policy document MCP — retrieval and summarization for agents and customers
- FNOL automation MCP — first-notice-of-loss intake into claims systems
- Customer service MCP — full-context access across policy admin and account systems
client outcome
40%
Increase in customer satisfaction after deploying AI-powered customer service connected to policy and account systems — HSA Trustee
Logistics
- Fleet operations MCP — vehicle location, status, maintenance, driver data
- Freight pricing MCP — real-time rate lookup and quote generation
- Track and trace MCP — shipment status and exception retrieval
- Carrier onboarding MCP — document collection and verification
- Inventory forecasting MCP — demand signal retrieval and reporting
client outcome
18%
Revenue increase — plus 31% fewer empty miles and 12x faster truck load times — top-50 US freight broker
Real Estate & Construction
- Contract review MCP — multi-document comparison and red-flag detection
- Property data MCP — records, valuation, and market comps with access controls
- Tenant portal MCP — maintenance requests, lease queries, account management
- Construction document MCP — drawing, spec, and submittal retrieval
- Investment analysis MCP — portfolio and deal data for AI-assisted underwriting
client outcome
30%
Better demand forecast accuracy — plus 25% less overstocking and 20% fewer supply-chain disruptions — fiberglass manufacturer
Our Methodology
The RTS MCP Sprint
A defined 5-stage delivery methodology. Each stage has a named output engineering buyers can use to plan their team’s involvement.
01
Discover
Wk 1–2
Map AI systems, target integrations, auth requirements, and compliance constraints. Output: architecture decision document.
02
Design
Wk 2–4
Design the server — which tools, which resources, transport choice, auth model, multi-tenant boundaries. Output: tech spec ready for build.
03
Build
Wk 3–8
Implement, test, and integrate with target systems. Code review, integration tests. Output: working server in staging.
04
Harden
Wk 6–10
Security review, performance testing, multi-tenant testing, observability wiring. Output: production-ready server with runbook.
05
Operate
Wk 10–24+
Live monitoring of tool-call latency, errors, and cost. Drift detection. New tools as the AI footprint grows, MCP spec tracking, and LLM provider switching without rewriting. Optional managed operations.
“Every stage of the MCP Sprint exists because we’ve seen what happens without it. Skip Harden and security findings surface in production. Skip Design and the tools don’t match what the AI actually needs. The methodology is the lesson list from 600+ engagements compressed into a repeatable process.”
Sandhya Ramamurthy — Director of Data Engineering, RTS Labs
The Security Layer
The Production Security Model
Most MCP tutorials do not cover security. Production MCP servers require a full security model. Here is what RTS builds into every production server.
Authentication
Every user gets their own token. The MCP server knows who is asking. No shared service keys, no anonymous access.
OAuth 2.1 with PKCE, refresh handling, and session isolation across tenants.
Authorization
Permission checks live inside the MCP server. When the AI calls a tool on behalf of a user, the server checks what that user is allowed to see. The application layer and database are not the control point. The server is.
Audit Log
Every action the AI takes is logged: who asked, what was called, what came back, and when. Exportable to your SIEM for compliance reviews.
Splunk, Datadog, and Elastic supported.
Compliance Ready
SOC 2 Type II practices by default. HIPAA-aware patterns for healthcare data. FedRAMP-aligned controls when needed.
See AI Governance Consulting for full framework support across NIST AI RMF, ISO/IEC 42001, EU AI Act, and more.
Proof
We've Shipped This Engineering Pattern Already
MCP was released in November 2024, so no one has years of MCP-branded case studies. What RTS has is 14+ years building exactly the same integration pattern MCP now standardizes — AI connected to enterprise systems, with authentication, multi-tenant safety, and measurable outcomes.
Legal
Preferred Legal Group
AI connected to legal systems for document extraction and drafting — the same engineering pattern as a contract-review MCP server. Built before MCP existed; the same pattern, today implemented through MCP.
120 → 10 Min
drafting time · 91% reduction
Insurance
HSA Trustee
AI customer service connected to policy admin and account systems — the exact use case a claims or service MCP server handles today. Same pattern, now implemented through MCP.
increase in customer satisfaction
Legal / Real Estate
Commercial Real Estate Contract Analysis
AI connected to a contract repository for multi-document review — the same multi-system connectivity an enterprise MCP server provides.
better forecast accuracy · 25% less overstocking · 20% fewer disruptions
Why RTS
Why Us
01
We've Built the Systems Your MCP Server Plugs Into
14+ years across Finance, Insurance, Logistics, and Real Estate — Landstar, Dominion Energy, Advance Auto Parts, Goodwill, Centivo, Stovall. Not logos we’d like to work with; work we’ve shipped.
02
Production Discipline, Not Demo Theater
Authentication, tenant separation, monitoring, runbooks. The 80% of MCP work tutorials skip is the 80% your server actually needs to handle real users.
03
We Work in Your Stack, Not Ours
Python, TypeScript, Java, Go, .NET. We meet your team where you already are. We don’t sell a platform you’d have to adopt.
04
We Publish What We Learn
Benchmarks across LLMs, security findings, and real production lessons — original research nobody else in this market is publishing yet.
The Real Choice
Build In-House, or Build With RTS
That is the actual choice. There’s no shame in either direction — but know which one you’re picking.
leadership
Jyot Singh
Founder & CEO
Founded RTS Labs 14+ years ago. Has led AI and data-engineering engagements across Finance, Logistics, Insurance, and Healthcare for mid-market and enterprise clients.
Alex Hogancamp
Director of AI
Leads AI strategy and LLM engineering at RTS. Designed the Production Sprint methodology and oversees model architecture, evaluation, and governance practices.
Sandhya Ramamurthy
Director of Data Engineering
Leads data platform and pipeline architecture. Specializes in making enterprise data production-ready for AI — the step most projects skip and most projects fail at.
Tech Stack
A Stack as Flexible as Your Team's
We work with the tools your team already uses. These categories show the full set RTS delivers across.
Languages
LLM Providers
Transports & Protocols
Cloud & Infrastructure
Observability
Security & Compliance
Client Voices
What Clients Actually Say
“RTS Labs has been an invaluable partner in our journey. Their expertise and ability to understand our vision allowed them to deliver innovative, AI-powered solutions that align perfectly with our goals. Their collaborative approach and commitment to excellence have transformed how we operate, driving efficiency and unlocking new opportunities. We couldn’t have asked for a better partner.”
Jason Herzog
Founder, Holon Health
“RTS Labs became an extension of our team, delivering AI solutions that transformed how we operate. Their understanding of our needs and collaborative approach unlocked opportunities we hadn’t imagined. Truly a game changer for our business.”
Gemma Brooks
COO, BlueOceanBrain (an HSI Company)
Common Questions
Frequently Asked Questions
What is the Model Context Protocol (MCP)?
MCP is an open standard for connecting AI assistants to external systems and data sources. It defines a common format for how AI applications request information or take actions, and how servers respond. Before MCP, connecting AI to a new system meant writing custom integration code for each combination of AI model and system. MCP standardizes that layer, so any AI assistant that supports MCP can work with any MCP server, regardless of who built either one.
Who created MCP, and is it an open standard?
Anthropic released MCP as an open standard in November 2024 and published the specification publicly. OpenAI and Google have since adopted the protocol, confirming its status as an industry standard rather than a proprietary tool. The specification is maintained at modelcontextprotocol.io and is available for anyone to implement.
What is the difference between MCP and a traditional API?
A traditional API is built for software-to-software communication: a developer writes code that calls specific endpoints in a predictable way. MCP is built for AI-to-system communication — the AI decides which tools to use, when, and how to interpret the results. The key difference is that MCP includes semantic descriptions of what each tool does, letting an AI reason about which tool to call without a developer hardcoding the logic. APIs still sit behind the MCP server in most implementations; MCP is a layer on top.
What is the difference between MCP and RAG?
RAG (retrieval-augmented generation) gives an AI access to a knowledge base, usually by embedding documents and retrieving relevant chunks by semantic similarity. MCP gives an AI the ability to take actions and access live data — look up a record, run a query, update a field, send a message. The two are complementary: you might use RAG for a policy library and MCP for live account data. Many production AI systems use both.
What programming languages support MCP server development?
The MCP specification has official SDKs for Python and TypeScript. RTS also works in Java (Spring AI MCP), Go, .NET, and Rust. The right choice depends on your existing infrastructure and team — if your backend is Python, we build in Python; if your team runs TypeScript, we build in TypeScript. We don’t require you to adopt a new language or framework.
What is the difference between local (STDIO) and remote (HTTP+SSE) MCP servers?
A local MCP server runs on the same machine as the AI client and communicates over standard input/output (STDIO) — simple to set up and common in tools like Cursor and Claude Desktop. A remote MCP server runs as a separate service over HTTP with server-sent events (SSE). Remote servers are required for multi-user production deployments because they run continuously, support many users at once, and can be deployed with proper authentication and access controls. Most enterprise use cases require remote HTTP+SSE servers.
How long does it take to build a production MCP server?
A focused, single-system server with authentication, a handful of tools, and basic observability typically takes 4–8 weeks from discovery to production. More complex servers — multiple systems, multi-tenant isolation, compliance requirements, custom observability — run longer. The RTS MCP Sprint runs from Discover (weeks 1–2) through Operate (weeks 10–24+). The 2-week MCP Readiness Assessment is the fastest way to get a realistic estimate for your situation.
How much does MCP server development cost?
Costs vary by scope. A 2-week MCP Readiness Assessment typically runs $5,000–$10,000 and produces an architecture decision document and build-vs-buy recommendation. Production MCP server development usually ranges from $25,000 to $100,000, depending on how many systems you’re connecting, your authentication model, multi-tenant requirements, and compliance constraints. Multi-server platform engagements are scoped individually. The best way to get a real number is a conversation about your specific situation.
How do you secure a multi-tenant MCP server?
Multi-tenant security requires that every tool call is scoped to the authenticated user and their tenant context. The server must validate identity and permissions before executing any tool, and the query or action passed to your system must be filtered to that tenant’s data only. A common failure is checking permissions at the application layer but not at the MCP tool layer — which lets one user’s AI session retrieve another user’s data. RTS builds access controls directly into the tool handlers using row-level security patterns at the MCP layer.
Can one MCP server work with Claude, GPT, and Gemini?
Yes — that’s one of the primary benefits of building to the MCP standard rather than integrating with a single provider. Any MCP-compatible client can connect to any MCP-compliant server: Claude, GPT-4o, Gemini, Cursor, and other MCP-enabled tools can all use the same server without modification. When a new provider adds MCP support, your server works with it immediately.
Let's Talk
Ready to Build?
If your team is hitting integration walls, an MCP server is often the right next step. If you’re not sure yet, the MCP Readiness Assessment is the lowest-risk way to find out what your architecture actually needs.
- ✓ 2-week MCP Readiness Assessment to scope the work
- ✓ We work in your stack — Python, TypeScript, Java, Go, .NET
- ✓ U.S.-based engineers, regulated-industry experience
Trusted by 600+ companies including Dominion Energy, Landstar, Advance Auto Parts, and Goodwill Industries. No spam. We respond within one business day.