An MCP server exposes tools, data and prompt templates to AI applications using the open Model Context Protocol. Any MCP-compatible client or agent can use it, so one server replaces separate integrations for each model or agent. Introduction to MCP covers the protocol in more detail.
MCP Server Development Services
MCP server development for AI agents: secure connectors to your CRM, databases and internal APIs, with access control and audit logs. Python, TypeScript, Go.
The Model Context Protocol (MCP) is an open standard for connecting AI applications to tools and data. Instead of writing a custom integration for every model and every system, you build one MCP server per system and any compatible agent can use it.
Lightrains builds MCP servers for companies whose agents need to work with real business systems. The work is mostly not the protocol. It is deciding what an agent may see and do, enforcing that in code, and logging every call so someone can check what happened.
What Lightrains builds
Connectors for business systems
MCP servers for CRM, ticketing, ERP and billing systems, so an agent can look up an account, read a ticket history or draft an update through one tested interface.
How MCP works, and where it falls short →Database and document access
Read access to SQL databases, data warehouses and document stores, scoped by user and role, with sensitive fields masked before anything reaches the model.
Enterprise context engineering →Internal APIs as agent tools
Wrapping undocumented internal services as MCP tools with typed inputs, clear descriptions and safe defaults, so agents call them correctly and fail loudly when they cannot.
AI agent development →Moving existing tools to MCP
Porting function-calling or framework-specific tools to MCP, so the same tool layer works with any compatible model and client instead of one vendor.
AI agent architecture guide →When an MCP server is worth it
MCP is not always the answer. For one or two stable integrations, a direct API call is simpler. MCP pays off when several agents or models need the same systems.
| Approach | Good fit when | Main limitation |
|---|---|---|
| Direct API integration | One or two integrations that will not change often. | Each new model or agent needs the integration rewritten. |
| Vendor function calling | A single model provider and a small, stable tool set. | Tool definitions are tied to that provider. |
| Framework tools (LangChain, LlamaIndex) | Teams already committed to one framework. | Tool abstractions differ between frameworks, which hurts portability. |
| MCP server | Several systems, several agents, or a need to compare or switch models. | Young ecosystem: auth flows and discovery still need custom work. |
From Lightrains projects
1 day
To build an MCP server for a Salesforce connection with the Python SDK. The same integration took three weeks with custom API code on an earlier project.
3 models
Evaluated in parallel by one client against an identical MCP tool layer, so the comparison measured the models and not the integrations.
Security and governance
An MCP server is a new door into your systems. These controls are part of every build. The MCP handbook for CXOs explains the governance side in more depth.
Authentication at the entry point
OAuth2, API keys or mTLS in front of every server. MCP leaves complex auth flows to the implementer, so this is built and tested explicitly.
Permissions per tool and resource
Role-based access and data masking are applied at the resource level, so an agent acting for a user only sees what that user may see.
Read-only by default
Tools that write, delete, send or pay are separate from read tools and can require human approval before they run.
Audit log of every call
Each tool call is logged with the caller, inputs, result and latency. Security teams review one server instead of a dozen custom integrations.
Rate limits and timeouts
Limits per agent and per tool protect the systems behind the server from a looping agent or a burst of requests.
Deployment you control
Servers ship as containers and run in your cloud account or on-premises, next to the systems they connect to.
How a project runs
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Step 1
Map the systems
List the systems agents need, the actions they should be allowed to take, and who approves what. The output is a tool specification your security team can review.
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Step 2
Build the first server
Start with one system and the read-only tools. Test them against real agent prompts and your own data, then add write tools behind approvals.
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Step 3
Harden and extend
Add monitoring, audit export and load testing, then extend to the next systems. Lightrains can run the servers or hand them to your team with documentation.
MCP servers are usually one part of a larger agent project. If you also need the agents themselves, see AI agent development. If the agent mainly answers questions from documents, start with RAG development.
Frequently Asked Questions
Function calling is defined per model provider. An MCP server sits outside the model, so the same tools work across providers. On one Lightrains project the client compared three models side by side while the tool layer stayed identical.
A server for one well-documented system can be quick. Lightrains built an MCP server for a Salesforce connection in one day with the Python SDK; the same integration had taken three weeks with custom API code on an earlier project. Auth, permissions and approvals for write actions take longer and depend on your security requirements.
Python, TypeScript and Go, which have the most mature MCP SDKs. The choice usually follows your existing stack and who will maintain the server.
The protocol is stable, but the ecosystem is young. Server discovery is still manual and complex auth flows need custom work, so budget for that. The MCP handbook for CXOs covers governance and rollout.
You do. All IP belongs to you, access to your systems is VPN-only under NDA, and payments are tied to milestones.
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