Connect AI agents to your business systems efficiently with custom Model Context Protocol (MCP) servers. Lionwood helps you build MCP servers that give AI applications, like Claude and OpenAI powered agents, controlled access to APIs and data from internal tools. We provide full-cycle services from choosing the right tech stack and implementing OAuth to integrating existing services, testing, and deployment. Whether you’re a startup exploring AI or an established business looking to automate workflows, we can help you design, build, and ship an MCP solution that fits your specific needs.
Your AI agent can generate code and answer questions, but it needs a reliable way to interact with your business systems. MCP servers connect AI clients with selected APIs, databases, and internal tools, enabling agents to retrieve information and perform authorized actions.
Connecting AI applications to a growing software stack through separate, bespoke integrations can be difficult to maintain. MCP provides an open standard for communication between AI clients and external tools, helping you build reusable connectors and manage integrations more efficiently.
Agents need clear boundaries when interacting with customer data, SaaS platforms, or business-critical systems. We implement appropriate authentication, OAuth where applicable, authorization rules, input validation, and read-only access when write operations are unnecessary.
Already have an MCP server? We can audit its architecture, test tool execution, fix integration issues, update dependencies, and review its security and compatibility with major agent platforms. Our goal is to make your implementation more reliable and maintainable.
Your AI agent can generate code and answer questions, but it needs a reliable way to interact with your business systems. MCP servers connect AI clients with selected APIs, databases, and internal tools, enabling agents to retrieve information and perform authorized actions.
Connecting AI applications to a growing software stack through separate, bespoke integrations can be difficult to maintain. MCP provides an open standard for communication between AI clients and external tools, helping you build reusable connectors and manage integrations more efficiently.
Agents need clear boundaries when interacting with customer data, SaaS platforms, or business-critical systems. We implement appropriate authentication, OAuth where applicable, authorization rules, input validation, and read-only access when write operations are unnecessary.
Already have an MCP server? We can audit its architecture, test tool execution, fix integration issues, update dependencies, and review its security and compatibility with major agent platforms. Our goal is to make your implementation more reliable and maintainable.
The Model Context Protocol (MCP) is an open standard that enables AI applications to discover and use tools and contextual information through a common interface. It can help join agents, chatbots, and other applications to existing business capabilities without requiring a separate integration for every client.
However, adopting MCP involves more than implementing a protocol specification. The server must work with your APIs, data models, permissions, infrastructure, and existing tech stack. Lionwood combines backend engineering, system integration, and AI development expertise to help you create a solution that supports real business workflows.
Build production-ready, reliable AI systems integrated into your infrastructure — ranging from intelligent systems to full applications.
We help associate AI capabilities with existing applications and workflows to enable smooth automated workflows across functions.
We identify the intended users, AI clients, systems, and workflows, and what the server should read, write or automate.
We define the implementation approach, access boundaries, and whether OAuth, read-only permissions, etc. are required.
We implement the server using a suitable language and SDK, such as TypeScript or Python.
We configure permissions and connect the server to the required APIs, databases, SaaS tools, etc.
We test tool discovery, input handling, API, permissions, error scenarios, and validate read and write operations.
We package and deploy the server to the agreed environment, document its configuration, and support the next steps.
Our services cover the MCP implementation lifecycle, from defining the right integration approach to deploying, testing, and improving a working server. We tailor each engagement to your systems, AI use cases, security requirements, and available resources.
3
core MCP capability types: tools, resources, prompts
Build an MCP server around your business logic instead of forcing your workflows into a generic connector. We implement the tools and context your agents need, using your existing APIs and data sources wherever practical.
Connect AI agents with the applications your business already uses. We develop MCP connectors for existing APIs, SaaS platforms, databases, and internal tools, helping your ecosystem reuse established business capabilities rather than duplicating them.
Prepare your MCP server for use in the intended environment. We configure the required runtime, transport, credentials, and settings, taking into account your infrastructure and the AI clients that will use it.
Assess an existing MCP server to identify security gaps, integration failures, and reliability issues. We review the implementation against its intended use, test how tools behave, and help fix problems that could affect users or connected systems.
Determine where MCP fits into your AI strategy before committing significant development resources. We help you evaluate use cases, select an implementation approach, and understand the engineering effort, dependencies, and risks involved.
Connect AI agents to CRM platforms, customer portals, SaaS products, and internal business applications. Enable approved operations such as retrieving customer information, checking records, or updating data under defined permissions.
Give AI applications controlled access to business knowledge, documents, databases, and operational data. Use read-only tools where retrieval is sufficient, with access rules aligned to the sensitivity of the information.
Consolidate multiple systems so an AI agent can coordinate approved steps across a business process. Potential use cases include inventory checks, support workflows, reporting, and communication between internal tools.
Improve an existing implementation that needs better compatibility, stronger access controls, more reliable tool execution, or a clearer rollout process. We help identify issues, fix code where required, and prepare the server for evolving requirements.
We focus on practical integration outcomes rather than implementing technology for its own sake. Our team considers how agents will use your systems, how access should be controlled, and how the solution will be maintained after launch.
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Lionwood develops MCP servers that connect AI agents with your existing systems. We handle implementation, integration, rollout, and auditing to help you build secure, maintainable connections between AI applications and business functionality.
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What is your project about? Share a few details and we’ll respond with practical next steps and a realistic view of scope and approach.
Fill the formAn MCP server is a component that exposes tools, resources, or prompts to compatible AI applications through the Model Context Protocol. It gives an AI client a standardized way to discover available capabilities and interact with connected systems, subject to the server’s permissions and implementation.
