MCP Server Development
MCP Servers That Connect AI Models to Your Real Business Data
Model Context Protocol (MCP) is the open standard from Anthropic that lets AI models securely access your databases, APIs, and file systems. Instead of copying data into prompts manually, MCP servers expose your tools and data as structured resources that any compatible AI model can discover and use. We build production-grade MCP servers with authentication, rate limiting, and audit logging — so your AI agents operate on live data, not stale exports.
AI Models Are Blind Without Structured Data Access
Most AI deployments hit the same wall: the model is powerful, but it can't see your data. Teams copy-paste database records into prompts, manually export CSVs for analysis, and build brittle one-off integrations for every tool the AI needs to access.
This approach doesn't scale. Every new data source requires custom code. There's no authentication layer. No audit trail. No way to control what the model can access. And when you switch AI providers — from GPT to Claude or vice versa — every integration breaks.
Model Context Protocol solves this by creating a universal interface between AI models and external systems. One MCP server can serve any compatible client. The protocol handles discovery (what tools and resources are available), invocation (calling tools with typed parameters), and security (authentication, permissions, rate limiting). It's the USB-C of AI connectivity — one standard connector for everything.
Production-Grade MCP Servers for Your Business Systems
We build MCP servers that expose your business data and tools through the standardized protocol. Each server is a standalone service that any MCP-compatible AI client can connect to.
Database MCP servers expose your PostgreSQL, MySQL, or MongoDB data as queryable resources. The AI model can search customers, look up orders, check inventory — all through typed, validated queries with row-level access control. No raw SQL reaches your database.
API MCP servers wrap your existing REST or GraphQL APIs as MCP tools. Your CRM, ERP, project management, and communication tools become available to AI agents through a consistent interface. The server handles authentication, rate limiting, and response transformation.
File system MCP servers give AI models controlled access to documents, images, and data files. The model can read contracts, analyze spreadsheets, and process uploaded documents — with path restrictions ensuring it only accesses what it should.
Every server includes TypeScript type safety, JSON Schema validation for all inputs, structured error handling, comprehensive logging, and health monitoring endpoints.
MCP Server Development in 4 Phases
1
Data & Tool Inventory(1 week)
We audit the systems your AI models need to access: databases, APIs, file stores, and internal tools. We define which operations should be read-only vs. read-write, map authentication requirements, and design the resource and tool schema.
2
Server Architecture & Security Design(1 week)
We design the MCP server architecture: transport layer (stdio, SSE, or HTTP), authentication mechanism (API keys, OAuth, JWT), rate limiting rules, and audit logging strategy. For multi-system deployments, we plan the server topology and client routing.
3
Implementation & Testing(2-3 weeks)
We build the MCP server with full TypeScript type safety, implement all resource endpoints and tool handlers, add input validation via JSON Schema, and test extensively. Testing covers normal operations, malformed inputs, authentication edge cases, and concurrent request handling.
4
Deployment & Client Integration(1 week)
The server deploys to your infrastructure with health monitoring, log aggregation, and alerting. We configure your AI clients (Claude Desktop, custom agents, IDE extensions) to connect to the server and verify end-to-end functionality across all tools and resources.
MCP Server Technology Stack
M
MCP TypeScript SDK
Official Anthropic SDK for building MCP servers with full protocol compliance and type safety
P
PostgreSQL / MySQL
Database backends with connection pooling, prepared statements, and row-level security policies
N
Node.js
Server runtime with async I/O for high-throughput tool execution and concurrent client connections
Z
Zod
Runtime type validation for all tool inputs and resource parameters
D
Docker
Containerized deployment with resource limits, health checks, and easy scaling
P
Prometheus / Grafana
Metrics collection and dashboarding for request volume, latency, error rates, and tool usage analytics
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MCP Server Use Cases
SaaS Companies
Challenge: AI features needed access to 12 different microservice APIs, each with its own auth scheme and data format
Solution: Unified MCP server wrapping all microservice APIs with consistent authentication, typed tool definitions, and automatic API version management
Result: Integration time for new AI features dropped from 2 weeks to 2 days; API-related bugs in AI features reduced by 78%
Financial Services
Challenge: Compliance required full audit trails for every AI-initiated data access, but ad-hoc integrations had no logging
Solution: MCP server with comprehensive audit logging, role-based access control, and data masking for sensitive fields
Result: Passed SOC 2 audit for AI data access; compliance team can review every AI interaction with customer data
Legal Firms
Challenge: Lawyers needed AI to search case files, contracts, and precedent databases, but documents were spread across 4 systems
Solution: Document MCP server unifying access to DMS, contract repository, case management, and legal research databases
Result: Legal research time reduced by 65%; AI-assisted contract review covers 3x more documents per review cycle
E-commerce
Challenge: Customer service AI needed real-time access to orders, inventory, shipping, and returns — but each system had different APIs
Why idataweb for MCP Server Development
Modern Production Stack
MCP servers built with TypeScript on our standard Next.js 16 + PostgreSQL stack. We run MCP in production daily — Claude Code with custom MCP servers is part of our development workflow. This isn't a technology we're experimenting with; it's how we build software.
AI-Native Team
Claude and GPT-4o aren't services we resell — they're tools we use every day to build software, generate content, and run internal operations. Our AI coding agents write production code. Our content pipeline generates and publishes articles autonomously. We build AI agents because we are an AI-native team.
Self-Hosted Infrastructure
Self-hosted infrastructure means your data stays where you control it. No vendor lock-in to SaaS platforms that can change pricing or terms. Full PostgreSQL audit trails, your own backups, and GDPR compliance built into the architecture.
End-to-End Delivery
Strategy, architecture, development, deployment, and ongoing support — all from one team. No handoffs between consultants, designers, and developers. The engineers who build your system are the same ones who maintain it.
Automation-First Operations
Our own operations are automated end-to-end: CI/CD pipelines, infrastructure monitoring with Telegram alerts, daily database backups, automated content publishing, and AI-assisted development workflows. We build automation for clients because automation is how we run our own business.
Transparent Fixed Pricing
Frequently Asked Questions
How much does MCP server development cost?
Single-system MCP servers (one database or API) start at $12,000-$18,000. Multi-system servers connecting 3-5 data sources range from $20,000-$40,000. Enterprise MCP infrastructure with multiple servers, authentication federation, and monitoring costs $40,000-$80,000. Ongoing maintenance averages $1,000-$3,000/month depending on complexity.
What AI models support MCP?
Claude (via Claude Desktop and the API) has native MCP support. The protocol is open-source, so any AI client can implement it. Current compatible clients include Claude Desktop, Cursor IDE, Windsurf, Cline, and custom applications built with the MCP client SDK. The ecosystem is growing rapidly.
Is MCP secure for production use?
MCP itself is a protocol — security depends on the server implementation. Our servers include authentication (API keys, OAuth 2.0, or JWT), authorization (role-based access control per tool and resource), input validation (JSON Schema for every parameter), rate limiting, audit logging, and encrypted transport.
Can MCP servers handle high-volume production traffic?
Yes. Our MCP servers are built on Node.js with connection pooling and async I/O, handling hundreds of concurrent requests. For high-throughput scenarios, we deploy behind a load balancer with horizontal scaling. Typical latency is 50-200ms per tool call.
Ready to Implement MCP Server Development?
Tell us about your needs and we'll design a custom mcp server development solution for your business.
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