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Our end-to-end MCP server development services
From the first architecture decision to a server running in production, our MCP server development services cover every stage of making your systems usable by AI. Each server is built against the official Model Context Protocol specification, tested with the AI clients your teams rely on, and secured for real business data.
MCP Consulting & Architecture
Before any code is written, we map which systems your AI needs to reach, what it should be allowed to read or change, and who will use it. Our consultants then define the server boundaries, choose between local and remote deployment, plan the authorization model, and decide which capabilities become tools, resources, or prompts. The result is an MCP architecture that fits your security policies and scales as more AI use cases arrive.
WHAT’S INCLUDED
- AI use case and system mapping
- MCP server boundary planning
- Local vs remote deployment strategy
- Authorization and permission design
- Third-party MCP server assessment
Custom MCP Server Development
We build MCP servers around the way your teams actually work, not around a raw list of endpoints. Each tool gets a clear name, a plain-language description, and strict input and output schemas, so AI models pick the right action and pass the right parameters. We develop in Python, TypeScript, Java, C#, or Go using the official MCP SDKs, matching the stack your engineers already maintain.
WHAT’S INCLUDED
- Task-focused tool design
- Resource and prompt templates
- Structured input and output schemas
- Official MCP SDK implementation
- Error handling for AI-friendly responses
MCP Integration Services
Our MCP integration services connect your servers to the AI clients your organization uses, including Claude, ChatGPT, Microsoft Copilot Studio, GitHub Copilot, Cursor, and custom AI applications. We configure client connections, handle the differences in how each client supports the protocol, and set up rollout so the right teams get the right servers without manual setup on every machine.
WHAT’S INCLUDED
- Claude and ChatGPT connector setup
- Copilot Studio and IDE configuration
- Custom AI application connections
- Client compatibility testing
- Team-wide server rollout
API-to-MCP Conversion
Most organizations already have the APIs their AI needs, but those APIs were written for developers, not models. We turn existing REST, GraphQL, and internal service APIs into MCP servers, grouping related calls into tools that match real tasks and leaving out endpoints the AI should never touch. Your existing systems stay unchanged while gaining a clean, AI-ready interface.
WHAT’S INCLUDED
- API review and tool selection
- REST and GraphQL to MCP mapping
- Database and data warehouse access
- Legacy system wrappers
- OpenAPI-based server generation
MCP Security & Access Control
An MCP server can read records and take actions, so security cannot be an afterthought. We implement OAuth 2.1-based authorization, map AI access to your existing user roles, add confirmation steps for sensitive actions, and log every tool call. We also harden servers against MCP-specific risks such as prompt injection through tool results, poisoned tool descriptions, and over-broad data exposure.
WHAT’S INCLUDED
- OAuth 2.1 and SSO-based authorization
- Role-based tool permissions
- Human approval for sensitive actions
- Prompt injection safeguards
- Tool call audit logging
Remote MCP Server Deployment
Local servers suit individual developers, but company-wide AI needs remote MCP servers that everyone can reach securely. We deploy servers over Streamable HTTP on AWS, Azure, Google Cloud, or your own infrastructure, with containerized builds, autoscaling, and secrets management. For organizations running many servers, we set up an MCP gateway that centralizes authentication, routing, and policy.
WHAT’S INCLUDED
- Streamable HTTP remote servers
- Cloud or on-premise hosting
- Docker and Kubernetes deployment
- MCP gateway and server registry
- Secrets and credential management
MCP Server Testing, Monitoring & Support
We test each server the way an AI will use it: checking that models choose the right tools, pass valid parameters, and recover from errors. After launch, we monitor tool usage, latency, and failures, then refine tool descriptions based on real behavior. As the MCP specification and client support evolve, we keep your servers current so they continue working without disruption.
WHAT’S INCLUDED
- Tool selection and accuracy testing
- MCP Inspector and client testing
- Usage, latency, and error monitoring
- Tool description refinement
- Specification and SDK upgrades
MCP server use cases across industries
Every industry runs on its own mix of systems, records, and rules. We design MCP servers around the specific platforms and data each sector depends on, so AI assistants can answer questions and complete tasks using live information, within the access limits your compliance teams require.
LET’S BUILD TOGETHER
Still building a separate connector for every AI tool?
Talk to our MCP engineers about your systems. We will show you which ones to expose first, what the AI should be allowed to do, and how one MCP server can serve every client your teams use.
AI Solutions Engineered for Enterprise Scale
150+
AI Engineers & Data Scientists
300+
AI Solutions Delivered
ISO 9001 Certified
NASSCOM & STPI Accreditation
NASSCOM & STPI Accreditation
100+
AI Models in Production
30+
Industries Served
THE MCP ADVANTAGE
Why businesses are standardizing on the Model Context Protocol
Before MCP, every AI tool needed its own custom connector to every system, and each one had to be rebuilt when the model or vendor changed. MCP replaces that with one standard interface, which is why MCP AI development is becoming a regular part of enterprise AI roadmaps.
Schedule a Consultation
01
Build a Connector Once, Use It Everywhere
A single MCP server for your CRM or database works with Claude, ChatGPT, Copilot, and your in-house AI apps. Your engineering effort goes into one well-built server instead of a separate integration for each tool.
02
AI Answers Come From Live Business Data
Instead of relying on what a model learned during training or on pasted spreadsheets, AI assistants query your systems at the moment a question is asked, so answers reflect current orders, tickets, and records.
03
Access Rules Live in One Place
Permissions, approvals, and logging are enforced inside the MCP server rather than scattered across many integrations. Security teams review one layer and know exactly what each AI client can reach.
04
Switching AI Models Stops Being a Rebuild
Because MCP is an open standard supported by the major AI providers, you can change models or add new AI tools without rewriting how they connect to your systems. That protects your investment as the market moves.
05
New AI Use Cases Ship Faster
Once your core systems are available through MCP, new assistants and agentic AI workflows can reuse the same servers. Teams move from idea to working pilot without waiting on fresh integration work.
06
Every AI Action Leaves an Audit Trail
Each tool call passes through a defined interface that can be logged, reviewed, and limited. When auditors or managers ask what the AI did and why, the record is already there.
MCP Tools
Tools are the actions an AI can take through your server, such as creating a ticket, looking up an order, or updating a record. We design each tool around a single clear task, write descriptions that models understand reliably, and define strict input schemas so requests are validated before they reach your systems.
Context Resources
Resources give AI clients read access to documents, files, database records, and other context without exposing an action. They are well suited to reference material like policies or product data, and they work alongside retrieval pipelines built through our RAG development services when large knowledge bases are involved.
Prompt Templates
Prompts are reusable templates that the server offers to users, such as "summarize this account" or "draft a renewal email". They package your team's best instructions and the right context into one step, giving everyone consistent, higher-quality results without having to write prompts from scratch.
Structured Tool Output
Beyond plain text, MCP tools can return typed, structured data defined by an output schema. We use structured output when results feed into other software or later steps in a workflow, so downstream systems receive predictable fields instead of free text they would have to parse.
User Elicitation
Elicitation lets a server pause and ask the user for missing details or confirmation during a task, such as choosing a date range or approving a change. We use it to keep people in control of sensitive actions and to avoid AI guessing at information it should ask for.
Transport Layers
MCP servers communicate over stdio when running locally next to a desktop app or IDE, or over Streamable HTTP when hosted remotely for many users. We choose the transport based on who uses the server and where it runs, and we build remote servers to handle many concurrent sessions reliably.
OAuth Authorization
For remote servers, the MCP specification defines an OAuth 2.1-based authorization flow. We implement it with your existing identity provider, such as Microsoft Entra ID, Okta, or Google Workspace, so AI access follows the same sign-in, roles, and revocation rules as the rest of your applications.
AI models and assistants we connect through MCP
MCP is model-agnostic, so the servers we build are not tied to a single AI vendor. We connect them to commercial assistants, open-weight models running in your own environment, and custom AI applications, and test behavior with each model your teams plan to use.
Claude
GPT
Gemini
Copilot
Llama
Mistral
Qwen
DeepSeek
Gemma
Amazon Nova
Claude Opus
Meta AI
Make your business systems AI-ready with MCP
Share the systems you want your AI to work with. Our team will outline the MCP servers you need, the access each one should allow, and a practical path from first pilot to company-wide rollout.
Technology stack for MCP server development
We build on the official MCP SDKs and proven backend, security, and cloud tooling, chosen to match your existing stack so your team can maintain the servers after handover.
Python SDK
TypeScript SDK
Java SDK
C# SDK
Go SDK
FastMCP
Node.js
FastAPI
Express.js
GraphQL
REST APIs
PostgreSQL
MongoDB
Redis
Elasticsearch
Pinecone
Databricks
Snowflake
OAuth 2.1
Role-Based Access Control
AES-256 Encryption
SSL/TLS
VPC Isolation
SOC 2 Controls
Claude
ChatGPT
GeminiMicrosoft Copilot
Llama
Mistral
LangGraph
LangChainOpenAI Agents SDK
CrewAI
PydanticAI
AutoGen
AWS
Microsoft Azure
Google Cloud
Terraform
Docker
Kubernetes
Prometheus
Grafana
Datadog
ELK Stack
CloudWatch
Salesforce
MS Dynamics 365
SharePoint
ServiceNow
SAPCRM and Sales MCP Servers
Let AI assistants look up accounts, update opportunities, and log activity in Salesforce, HubSpot, or Dynamics 365 without leaving the chat.
- Account and contact lookups
- Opportunity and pipeline updates
- Activity and note logging
- Quote and order status
- Role-based record access
Database and Analytics MCP Servers
Give AI governed, query-level access to PostgreSQL, Snowflake, Databricks, and other data stores for reporting and analysis.
- Read-only query tools
- Approved views and schemas
- Natural language reporting
- Row and column level permissions
- Query limits and cost controls
Knowledge and Document MCP Servers
Connect SharePoint, Google Drive, Confluence, and internal wikis so AI answers from approved, current documents.
- Document search and retrieval
- Policy and SOP access
- Permission-aware results
- Source references in answers
- Content freshness controls
ERP and Finance MCP Servers
Expose inventory, purchase orders, invoices, and approvals from SAP, Oracle, or NetSuite as safe, task-focused tools.
- Inventory and stock lookups
- Purchase order status
- Invoice and payment queries
- Approval workflows with sign-off
- Audit-ready action logs
Support and ITSM MCP Servers
Allow AI to read, create, and route tickets in ServiceNow, Jira Service Management, or Zendesk for faster resolution.
- Ticket search and summaries
- Ticket creation and routing
- Knowledge base suggestions
- SLA and status checks
- Escalation to human agents
Developer and DevOps MCP Servers
Help engineering teams work with code repositories, CI/CD pipelines, cloud resources, and logs from their IDE or AI assistant.
- Repository and pull request tools
- Build and deployment status
- Log and incident lookups
- Cloud resource queries
- Guarded write actions
How our MCP development services work
Our approach to MCP development moves in clear stages with a review point at each one, so you can check tool behavior, security, and scope before the next step begins.
1
Discovery
Identify the systems, users, and AI clients involved, and agree on what the AI should be able to read and do.
2
Tool Design
Define tools, resources, and prompts with schemas, descriptions, permissions, and the authorization approach.
3
Development
Build the MCP server with the official SDKs, connect it to your systems, and add logging and error handling.
4
Testing & Security
Test tool selection with real prompts across your AI clients and run security checks before release.
5
Deploy & Support
Deploy locally or as a remote server, roll out to teams, then monitor usage and keep the server current.
What makes Xicom a trusted MCP server development company
With over 20 years of software engineering behind us and 1,800+ projects delivered, we know that connecting AI to real business systems is as much about security and reliability as it is about code. As a custom MCP server development company, we treat every server as production software that handles sensitive data, which is why teams that need more than a quick prototype choose us as their MCP development agency.
Our engineers work directly from the Model Context Protocol specification and official SDKs, and we track each revision as it is released. Your servers follow the current standard for tools, resources, transports, and authorization, so they behave correctly across clients instead of working with only one.
We define what each AI client may read and change before building any tool, then enforce it with your identity provider, scoped permissions, approval steps, and audit logs. Known MCP risks like prompt injection and tool poisoning are reviewed as part of every delivery, not after an incident.
We are not tied to any single AI provider or platform. Servers are tested with the clients and models you actually use, whether that is Claude, ChatGPT, Copilot, or an open-weight model in your own environment, so you keep the freedom to change direction later.
A server that mirrors every API endpoint often confuses AI models. We design fewer, task-focused tools with clear descriptions and predictable responses, then test them with real prompts, which leads to more accurate tool selection and fewer failed calls in daily use.
Every server ships with source code, deployment scripts, tool documentation, and test suites written for your engineers. You can extend it in-house, keep us on for ongoing support, or combine both as your MCP footprint grows.
Engagement models for MCP server development services
Every custom MCP server development service engagement starts with a clear scope of systems, tools, and security needs. Choose the model that fits your stage, from a single fixed-price server to a dedicated team building out an organization-wide MCP layer.
Fixed Price Model
Best for a clearly scoped MCP server, such as one system with a defined set of tools, a set budget, and an agreed delivery date.
- Upfront agreed cost and project scope
- Milestone-based progress tracking
- No hidden charges or overheads
- Reliable delivery timelines and outcomes
Most Popular
Dedicated Teams Model
Ideal for organizations connecting many systems to AI, with engineers working exclusively on your MCP servers, gateway, and rollout.
- Full control over team structure and workflows
- Highly scalable and cost-effective
- Direct communication with developers
- Working hours overlap with US time zones
Time & Material Model
Suited to MCP projects where the list of systems, tools, or AI clients is still taking shape as pilots reveal what users need.
- Flexible billing based on actual efforts
- Adjust resources and scope anytime
- Ideal for iterative and evolving projects
- Faster implementation and continuous optimization
Security standards behind our MCP servers
MCP servers give AI access to business data and actions, so we deliver them under the same security and privacy standards as the rest of our engineering work. Where your data falls under industry rules such as HIPAA or CCPA, we design access, logging, and data handling to support those requirements.
Client testimonials and reviews showcasing the value we consistently deliver
Explore how our clients describe their journey with us, reflecting strong collaboration, effective execution, and consistent outcomes delivered across engagements. See how our delivery framework ensures consistency from initiation through to successful completion.
❝
Parity AI
We came to Xicom with an ambitious goal: make institutional-grade financial planning accessible to everyone through AI. Xicom went beyond the brief, blending conversational AI and predictive analytics into an effortless platform. Parity AI is exactly what we envisioned.
❝
RadioFX
Xicom took the time to understand what our stations and listeners actually need, building something tailored rather than generic. Live chat and polls are now core to how we connect with our audience — the app has become a true extension of our on-air presence.
❝
StudyBuddy
Xicom built exactly what our students needed: a secure space to connect, collaborate, and find tutoring support in one place. Their understanding of student behavior shows in how naturally everything fits together — StudyBuddy is something students genuinely rely on.
❝
BoxxDocks
Xicom helped us bring BoxxDocks to life exactly as we envisioned it. Their team understood the complexity of AI powered real-time asset tracking and delivered a platform that's both powerful and genuinely easy to use.
❝
TodayFit
We are very happy with their services. I cannot recommend them highly enough. The value, ability, and delivery that we have seen has been superb, and we look forward to continuing this relationship.
❝
SmartVend AI
Their team built SmartVend from the ground up, combining hardware integration, real-time inventory sync, and a centralized dashboard that completely changed how we operate. We couldn't have asked for a more capable technical partner.
❝
Tap Retail
Partnering with Xicom to develop our Tap Retail app and website was one of the best decisions we made. Their team demonstrated exceptional professionalism, technical expertise, and a deep understanding of our business goals.
❝
GoGmGo
Partnering with Xicom was a game-changer for us. They didn't just build software, they helped us redefine how we run our business. Their proactive support and deep domain knowledge gave us the confidence to innovate faster.
❝
DENVEN
Working with Xicom has been a transformative experience. Their team quickly understood our challenges and delivered a scalable, intuitive solution. I highly recommend Xicom to any organization seeking future-ready, high-performance digital products.
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Frequently Asked Questions
1. What is MCP server development?
MCP server development is the process of building a server that follows the Model Context Protocol, so AI assistants and agents can use your business data and actions in a standard way. The server exposes tools the AI can call, resources it can read, and prompts it can reuse, along with authentication, permissions, and logging. Once built, the same server can be used by any MCP-compatible AI client without writing a separate connector for each one.
2. What is the Model Context Protocol?
The Model Context Protocol (MCP) is an open standard, introduced by Anthropic in late 2024, that defines how AI applications connect to external tools and data. It works like a common plug: AI clients such as Claude, ChatGPT, Microsoft Copilot, and many developer tools can connect to any MCP server and discover what it offers. Major AI providers including OpenAI, Google, and Microsoft now support it.
3. Why work with a Model Context Protocol development company?
A basic MCP server can be built in an afternoon, but a production server that handles real business data needs careful tool design, OAuth-based authorization, permission scoping, input validation, rate limiting, logging, and ongoing updates as the protocol evolves. A Model Context Protocol development company brings that experience, so your team avoids security gaps and unreliable tools and gets a server that AI clients can use correctly from day one.
4. What is the difference between an MCP server and a regular API?
A regular API is designed for developers who read documentation and write code against it. An MCP server is designed for AI models: it describes each tool in plain language, defines input and output schemas, and lets the AI client discover available capabilities at runtime. In many projects, the MCP server sits in front of your existing APIs and translates them into tools an AI can understand and use safely.
5. Which AI clients can use the MCP servers you build?
The servers we build follow the official MCP specification, so they work with MCP-compatible clients such as Claude, ChatGPT, Microsoft Copilot Studio, GitHub Copilot in VS Code, Cursor, and Gemini-based tools, as well as custom applications built with agent frameworks like LangGraph or the OpenAI Agents SDK. We test each server against the specific clients your teams use.
6. Can you convert our existing APIs into MCP servers?
Yes. We review your REST, GraphQL, or internal service APIs and turn the most useful operations into well-described MCP tools and resources. Rather than exposing every endpoint, we group related calls into task-focused tools, add clear descriptions, and apply permission checks, which makes the server easier for AI models to use correctly and safer to run.
7. How do you secure MCP servers?
We apply OAuth 2.1-based authorization for remote servers, least-privilege access so each user and AI client only reaches what it should, input validation on every tool, confirmation steps for sensitive actions, rate limiting, and full audit logging. We also design against MCP-specific risks such as prompt injection through tool results, tool description poisoning, and excessive data exposure, and we review third-party MCP servers before they are connected to your environment.
8. How is MCP server development different from AI agent development or AI integration?
MCP server development builds the standard connection layer between AI and your systems. AI agents are the applications that use that layer to plan and complete tasks, which our AI agent development team builds. AI integration is a broader service that embeds AI capabilities into your existing enterprise software, covered by our AI integration services. Many clients combine all three, with MCP servers providing the reusable connectors.
9. How long does custom MCP server development take?
A focused MCP server that exposes a small set of tools from one system can often be delivered in a few weeks. Servers that connect multiple systems, require enterprise authentication, or need remote hosting with monitoring take longer. We confirm the timeline after reviewing your systems and use cases in a discovery session.
10. How much do custom MCP server development services cost?
The cost of custom MCP server development services depends on the number of systems and tools involved, authentication and security requirements, hosting model, and testing needs. We scope cost after a discovery conversation and offer fixed-price, dedicated team, and time and material engagement models.
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