MCP (Model Context Protocol) Servers provide a standardized, secure way for LLM applications to access tools, data, prompts, and resources. We build production-ready MCP servers that power reliable, policy-controlled AI workflows. Oodles designs and deploys MCP servers using Python, TypeScript / JavaScript, JSON-RPC, HTTP & WebSockets, and containerized cloud infrastructure—enabling secure, scalable, and auditable AI integrations.
The Model Context Protocol (MCP) defines a consistent interface for exposing tools, resources, prompts, and data to Large Language Models. An MCP Server implements this protocol to act as a trusted execution and retrieval layer for AI clients.
MCP servers are typically built using Python or Node.js, expose JSON-RPC / HTTP APIs, and integrate with databases, vector stores, file systems, and enterprise services.
Define MCP-compliant tools for search, CRUD operations, workflows, notifications, and system actions.
Secure integrations with SQL/NoSQL databases, vector databases, CRMs, ERPs, cloud storage, and internal APIs.
Prompt templates, file resources, embeddings, and hybrid retrieval for RAG-based LLM applications.
Authentication, authorization, policy enforcement, redaction, rate limiting, and human-in-the-loop approvals.
Structured logs, traces, metrics, error analysis, regression testing, and quality evaluation pipelines.
Dockerized MCP servers with CI/CD, environment configs, secrets management, and Kubernetes-ready scaling.
MCP Servers act as a secure control plane between AI models and real-world systems, enabling consistent automation, governance, and operational safety.
One consistent interface for tools, prompts, and resources across AI clients.
IAM, access controls, audit logs, and policy enforcement by design.
Resilient execution, error handling, and observability for production workloads.
Connect AI agents safely to enterprise data and operational systems.