Services
MCP Server Development
Zenoware builds custom Model Context Protocol (MCP) servers that connect AI agents to your systems, databases, and APIs. Enable Claude, GPT, and custom agents to interact with your infrastructure through secure, production-ready implementations.
What is MCP
The open protocol for AI-system integration
Model Context Protocol (MCP) is an open standard for connecting AI agents to external systems. Instead of building custom integrations for each AI platform, MCP provides a universal interface that works with Claude, GPT, and any agent that implements the protocol.
Zenoware specializes in building production-grade MCP servers that expose your data, tools, and business logic to AI agents securely and reliably. From database connectors to custom business workflows, we design MCP implementations that scale with your AI strategy.
Integration patterns
MCP use cases & applications
From simple data access to complex workflow automation, MCP servers enable AI agents to interact with your infrastructure as seamlessly as humans use APIs and interfaces.
Database & Data Access
Enable AI agents to query databases, read records, and access structured data with proper authentication, query optimization, and safety constraints.
API & Service Integration
Connect AI agents to REST APIs, GraphQL endpoints, and microservices. Handle authentication, rate limiting, and response transformation automatically.
File System & Document Access
Allow agents to read, search, and analyze documents, codebases, configuration files, and knowledge bases with appropriate access controls.
Business System Connectors
Integrate with CRM, ERP, project management, and internal tools. Enable agents to read data, trigger workflows, and update records safely.
Search & Knowledge Retrieval
Implement semantic search, vector databases, and RAG pipelines. Give agents access to indexed knowledge with relevance ranking and filtering.
Custom Tool Development
Build domain-specific tools for agents—calculations, transformations, validations, or any programmatic capability your agents need to perform tasks.
Platform capabilities
What MCP servers provide
MCP's architecture supports resources, tools, prompts, and real-time subscriptions—giving agents comprehensive access to your systems through a standardized protocol.
Resource Providers
Expose data sources, documents, and information streams as standardized MCP resources with URI-based addressing and metadata.
Tool Definitions
Define function schemas that agents can discover and invoke with parameter validation, error handling, and response formatting.
Authentication & Security
Implement OAuth flows, API key management, role-based access control, and audit logging for secure agent-to-system interactions.
Real-time Subscriptions
Enable agents to subscribe to data changes, event streams, and notifications using MCP's subscription protocol for reactive workflows.
Multi-transport Support
Deploy MCP servers over HTTP, WebSocket, stdin/stdout, or custom transports depending on your architecture and deployment model.
Sampling & Prompts
Provide context-aware prompt templates and sampling configurations that guide agent behavior when interacting with your systems.
Development roadmap
Our iterative approach delivers working MCP servers quickly, with continuous testing and refinement based on real agent interactions and performance metrics.
Requirements & architecture
Week 1- System integration mapping and data access requirements
- Security model design with authentication and authorization flows
- MCP server architecture planning with transport and deployment strategy
Core server implementation
Week 2–3- MCP server scaffold with SDK integration and transport setup
- Resource providers for data sources and document access
- Tool definitions with input validation and error handling
Integration & testing
Week 4- System connector implementation with API and database integration
- Authentication flows and security hardening
- Agent testing with Claude, GPT, or custom AI agents
Deployment & documentation
Week 5- Production deployment with monitoring and logging
- Developer documentation and agent integration guides
- Performance optimization and observability instrumentation
Why MCP for AI integration
MCP provides the standardization, security, and flexibility needed to connect AI agents to production systems safely and sustainably.
Open protocol standard
MCP is an open protocol backed by Anthropic, ensuring long-term viability, community support, and compatibility across AI platforms.
Agent-agnostic design
Build once, use everywhere. MCP servers work with Claude, GPT, custom agents, and any AI system that implements the protocol.
Type-safe tool schemas
Define tools with JSON Schema validation, ensuring agents invoke functions correctly with proper parameter types and constraints.
Secure by design
Built-in authentication, authorization, and audit logging patterns help you expose system capabilities safely to AI agents.
Production-ready infrastructure
Enterprise-grade implementations with error handling, rate limiting, logging, and monitoring for reliable production deployments.
Rapid iteration cycle
Test changes immediately with interactive agent sessions. Add new tools and resources without rebuilding agent prompts or workflows.