MCP Server Development Services - Scinforma

Model Context Protocol servers that connect AI assistants to your data, tools, and business systems

We specialize in developing Model Context Protocol servers that enable AI assistants like Claude to securely access and interact with your databases, APIs, file systems, and business applications through standardized interfaces.

Whether you need to connect Claude to your internal knowledge base, enable AI-powered automation across your tech stack, or build custom tools that extend AI capabilities, we create MCP servers that bridge the gap between large language models and your organization’s data and services. From simple file system access to complex multi-system integrations, we deliver production-ready MCP servers that unlock the full potential of AI assistants in your workflow.

What We Do

  • Custom MCP Server Development
    Build custom MCP servers that expose your proprietary systems, databases, and APIs to AI assistants through standardized protocols.
  • Database Integration Servers
    Create MCP servers that provide secure, read-only or read-write access to SQL and NoSQL databases with query validation and access controls.
  • File System & Storage Servers
    Develop servers that enable AI assistants to read, search, and analyze files from local storage, cloud storage, or document management systems.
  • API Gateway MCP Servers
    Build MCP servers that act as gateways to external APIs, translating AI requests into proper API calls with authentication and rate limiting.
  • Search & Retrieval Servers
    Implement MCP servers that provide semantic search capabilities over your documents, wikis, and knowledge bases.
  • Tool & Action Servers
    Create servers that expose business actions like creating tickets, sending emails, updating records, or triggering workflows to AI assistants.
  • Multi-System Integration Servers
    Develop comprehensive MCP servers that orchestrate interactions across multiple backend systems and data sources.
  • Real-Time Data Servers
    Build servers that provide streaming data, live metrics, or real-time updates to AI assistants for dynamic responses.
  • Security & Authentication Implementation
    Implement robust security with OAuth, API keys, role-based access control, and data encryption for enterprise-grade protection.
  • MCP Server Deployment & Management
    Deploy, monitor, and maintain MCP servers in cloud or on-premise environments with logging, metrics, and error handling.

Our Technology Stack

We build MCP servers using modern development tools and frameworks:

Programming Languages

  • • Python
  • • TypeScript/Node.js
  • • Go
  • • Rust
  • • Java
  • • C#/.NET

MCP SDKs & Frameworks

  • • MCP Python SDK
  • • MCP TypeScript SDK
  • • FastMCP
  • • Custom Protocol Implementations
  • • Server-Sent Events (SSE)
  • • JSON-RPC 2.0

Data Sources

  • • PostgreSQL & MySQL
  • • MongoDB & Redis
  • • Elasticsearch
  • • AWS S3 & Cloud Storage
  • • REST & GraphQL APIs
  • • File Systems

Authentication & Security

  • • OAuth 2.0
  • • JWT Tokens
  • • API Key Management
  • • TLS/SSL Encryption
  • • Role-Based Access Control
  • • Secret Management (Vault)

Deployment & Infrastructure

  • • Docker Containers
  • • Kubernetes
  • • AWS Lambda
  • • Azure Functions
  • • Google Cloud Run
  • • Self-Hosted Servers

Monitoring & Logging

  • • Prometheus & Grafana
  • • ELK Stack
  • • CloudWatch
  • • Datadog
  • • Sentry
  • • Custom Logging

Our Development Process

We follow a systematic approach to building reliable, secure MCP servers.

1. Requirements & Use Case Analysis

Understand what data and tools AI assistants need to access, security requirements, and expected usage patterns to design the optimal MCP architecture.

2. Resource & Tool Definition

Define MCP resources (data sources), tools (actions), and prompts that the server will expose with clear schemas and descriptions.

3. Security Architecture Design

Design authentication, authorization, and data access patterns that protect sensitive information while enabling AI functionality.

4. Server Implementation

Develop the MCP server using appropriate SDKs with proper error handling, validation, logging, and performance optimization.

5. Integration Development

Build integrations with backend systems, databases, APIs, and services with proper connection pooling and retry logic.

6. Testing & Validation

Comprehensive testing including unit tests, integration tests, security testing, and testing with actual AI assistants like Claude.

7. Documentation

Create detailed documentation of available resources, tools, parameters, and usage examples for AI assistants and developers.

8. Deployment & Configuration

Deploy servers to production environments and configure AI assistants to connect with proper credentials and settings.

9. Monitoring & Maintenance

Implement monitoring, logging, and alerting with ongoing maintenance, security updates, and feature enhancements.

Types of MCP Servers We Build

  • Database Query Servers
    Enable AI assistants to query databases with natural language, automatically generating and executing SQL with built-in safeguards.
  • Document & Knowledge Base Servers
    Provide access to documentation, wikis, SharePoint, Confluence, or custom knowledge bases with semantic search capabilities.
  • File System Servers
    Allow AI assistants to read, search, and analyze files from local drives, network shares, or cloud storage like S3 or Google Drive.
  • CRM Integration Servers
    Connect to Salesforce, HubSpot, or other CRM systems to retrieve customer data, update records, or create opportunities.
  • Code Repository Servers
    Access GitHub, GitLab, or Bitbucket repositories to read code, review PRs, search issues, or analyze codebases.
  • Communication & Collaboration Servers
    Integrate with Slack, Microsoft Teams, email systems, or calendar applications for messaging and scheduling.
  • Analytics & Reporting Servers
    Query data warehouses, business intelligence tools, or analytics platforms to generate insights and visualizations.
  • E-Commerce & Inventory Servers
    Access product catalogs, inventory systems, order management, and customer data for e-commerce operations.
  • DevOps & Infrastructure Servers
    Interact with CI/CD pipelines, cloud infrastructure, monitoring tools, and deployment systems.
  • Custom Business Process Servers
    Expose organization-specific workflows, approval processes, and business logic to AI assistants.

MCP Server Components

MCP servers expose three main types of capabilities to AI assistants:

Resources

Data sources that AI assistants can read and reference in their context.

  • • Files and documents
  • • Database records
  • • API responses
  • • Search results
  • • Real-time data streams

Tools

Actions that AI assistants can invoke to perform operations or retrieve data.

  • • Execute queries
  • • Create or update records
  • • Send notifications
  • • Trigger workflows
  • • Generate reports

Prompts

Reusable prompt templates that AI assistants can use for common tasks.

  • • Query templates
  • • Analysis frameworks
  • • Report formats
  • • Task workflows
  • • Best practice guides

MCP Server Use Cases

Real-world applications of MCP servers across different scenarios:

Customer Support Automation

MCP server connecting Claude to your CRM, knowledge base, and ticketing system to provide instant, accurate customer support with full context from past interactions.

Data Analysis Assistant

Enable Claude to query your data warehouse, generate SQL, visualize results, and provide insights without requiring technical SQL knowledge from users.

Development Productivity

Connect Claude to your codebase, documentation, and DevOps tools for code review, debugging assistance, and automated deployment workflows.

Research & Knowledge Management

Access internal research, patents, academic papers, and proprietary knowledge to answer complex questions and generate comprehensive reports.

Security & Best Practices

We implement comprehensive security measures in all MCP servers:

✓ Authentication & Authorization

Secure authentication with OAuth, API keys, or certificate-based auth and fine-grained access control

✓ Data Encryption

TLS/SSL for data in transit and encryption at rest for sensitive information

✓ Input Validation

Rigorous validation of all inputs to prevent injection attacks and data corruption

✓ Rate Limiting

Protect backend systems from overload with configurable rate limits and throttling

✓ Audit Logging

Comprehensive logging of all requests, responses, and actions for security audits

✓ Error Handling

Graceful error handling that doesn’t expose sensitive system information

Essential MCP Server Features

Our MCP servers include these critical capabilities:

  • Schema Definition & Validation
    Clear, well-documented schemas for all resources and tools with automatic validation of parameters and data types.
  • Connection Management
    Efficient connection pooling, retry logic, and graceful handling of backend system failures or timeouts.
  • Caching Strategy
    Intelligent caching of frequently accessed data to reduce load on backend systems and improve response times.
  • Pagination Support
    Handle large datasets efficiently with pagination, cursors, or streaming for resources with many records.
  • Real-Time Updates
    Support for subscriptions and real-time data updates when backend systems change or new data becomes available.
  • Multi-Tenancy
    Support multiple organizations or users with isolated data access and tenant-specific configurations.
  • Versioning
    API versioning to maintain backward compatibility while evolving server capabilities over time.
  • Performance Monitoring
    Track response times, error rates, and resource usage to identify and resolve performance bottlenecks.

Why Choose Our MCP Server Development Services?

  • MCP Protocol Expertise
    Deep understanding of Model Context Protocol specifications, best practices, and patterns for building reliable, efficient servers.
  • Security-First Approach
    Every MCP server includes enterprise-grade security with authentication, authorization, encryption, and comprehensive audit logging.
  • Production-Ready Code
    Well-architected, tested, and documented code that’s ready for production deployment with monitoring and error handling built-in.
  • Integration Experience
    Extensive experience integrating with databases, APIs, cloud services, and enterprise systems across diverse technology stacks.
  • Performance Optimization
    Optimize server performance with efficient queries, caching, connection pooling, and asynchronous processing where appropriate.
  • Comprehensive Documentation
    Clear documentation of all resources, tools, parameters, and usage examples for both AI assistants and developers.
  • Scalable Architecture
    Design servers that scale from prototype to production, handling increasing load and growing data volumes efficiently.
  • Ongoing Support
    Post-deployment support including monitoring, bug fixes, feature enhancements, and adaptation to evolving MCP specifications.

Common MCP Integrations

We build MCP servers that integrate with popular platforms and services:

Business Applications

Salesforce, HubSpot, SAP, Oracle, Microsoft Dynamics

Collaboration Tools

Slack, Microsoft Teams, Zoom, Google Workspace, Office 365

Development Platforms

GitHub, GitLab, Bitbucket, Jira, Confluence, Linear

Cloud Services

AWS, Azure, Google Cloud, DigitalOcean, Heroku

Databases

PostgreSQL, MySQL, MongoDB, Redis, Elasticsearch, Snowflake

Analytics & BI

Tableau, Power BI, Looker, Google Analytics, Mixpanel

Industries We Serve

We develop MCP servers for organizations across all industries:

Technology
Financial Services
Healthcare
E-Commerce
Manufacturing
Professional Services
Education
Media
Legal
Government
Real Estate
Telecommunications

Our Philosophy

We believe MCP represents a fundamental shift in how AI assistants interact with enterprise data and systems, moving from isolated tools to deeply integrated collaborators.

The power of MCP lies in its standardized approach to connecting AI with diverse data sources and tools. Rather than building custom integrations for each AI platform, MCP servers provide a universal interface that works across different AI assistants. We approach every MCP project with a focus on security, reliability, and developer experience, ensuring your MCP servers become trusted bridges between AI capabilities and your organization’s digital infrastructure. Whether you’re enabling Claude to query your database or building a comprehensive AI integration layer across your tech stack, we deliver MCP servers that are secure, performant, and built to evolve with your needs.

Ready to Build Your MCP Server?

Let’s discuss your integration needs and develop an MCP server that connects AI assistants to your data and systems.

Get Started Today