KriraAI builds custom MCP servers that connect your AI agents and LLMs to your databases, APIs, and business tools. Our MCP server development covers architecture, implementation, setup, and integration, with security and monitoring built in from day one. You get a production ready Model Context Protocol server that powers reliable AI automation and AI agent development.
KriraAI provides MCP server development services that connect your AI agents and LLMs to your real business systems. Our MCP development services cover design, build, deployment, and support in one place. You can start with a single service or take the complete package.
An MCP server is a small service that lets AI agents and LLMs use your business data and tools in a safe, standard way. You build it once, and every compatible AI client can use it. This gives you faster AI automation, lower integration cost, and better control over what AI can access.
A Model Context Protocol server follows the open MCP standard for connecting AI models to external systems. It sits between your AI agent and your databases, CRMs, and internal APIs. The agent asks for data or an action, and the server checks permissions and returns a clear result.
MCP tools are actions the AI can run, such as creating a ticket or updating a record. Resources are data the AI can read, and prompts are reusable instruction templates that keep answers consistent.
You connect each system once, and any compatible agent can use it, whether it runs on Claude, OpenAI, or Gemini. This saves development time and keeps AI tool integration consistent.
An MCP server gives LLMs live business data, such as current order status or the latest policy, instead of letting them guess. This makes your LLM applications more accurate and easier to trust.
MCP gives every AI agent one shared, governed way to reach your systems. You can grow from one assistant to a team of specialized agents without building separate connections for each.
A custom API integration links one AI app to one system and needs rework when the model changes. An MCP server exposes your systems once for many AI clients.
Area
Custom API Integration
MCP Server
New AI client
New build each time
Reuse the same server
Tool discovery
Manual, hard coded
Automatic, standard
Security
Different per integration
Central, consistent
Long term maintenance
Grows with each connection
One place to update
Our MCP server architecture has six layers, so every AI request is controlled, traceable, and fast.
The single entry point for every AI agent. It routes requests to the right tools and applies rate limits.
Every request is tied to a verified user or agent and checked against role based permissions. Sensitive actions can require approval.
Your MCP tools connect to databases, CRMs, and internal APIs, each with a clear schema and validated inputs.
Out of scope requests are blocked and sensitive data is filtered before it reaches the AI.
We log every tool call and track success rate, latency, errors, and cost.
The server runs in containers on AWS, Azure, or Google Cloud with automated releases and easy rollback.
Our MCP server implementation follows six clear steps, from first scope call to live support. This MCP development process keeps timelines predictable and MCP server setup smooth.
Every MCP development engagement ends with four clear deliverables, so you know exactly what you own at handover.
Every MCP server we deliver must meet four clear standards before sign off, so you know the work is ready for production. Tool Call Success Rate Verified on Your Data — We test every MCP tool against your real data and agree on a target success rate with you before launch. You receive the test results as proof. Out of Scope Request Handling — Requests outside the agreed scope are denied by default. We test this directly, so your AI agents cannot reach data or actions you did not approve. Zero Critical Defects at Sign Off — No critical or high severity defects remain open when we hand over the server. Any issue found during testing is fixed and retested first. Source and Freshness Metadata on Every Response — Each response can include where the data came from and how recent it is. This helps your users trust the answer and check it when needed.
Our MCP server development uses proven tools and platforms, so your server works with the AI models and systems you already run. MCP SDKs: Python and TypeScript — We build MCP servers with the official Python and TypeScript SDKs. Your team gets clean, standard code that is easy to review and extend. LLM and Client Platforms: Claude, OpenAI, Gemini, LangChain — Your MCP server works with Claude, OpenAI, and Gemini, and with agent frameworks such as LangChain. You can add or switch AI models without rebuilding your tools. Cloud Platforms: AWS, Azure, Google Cloud — We deploy on AWS, Azure, or Google Cloud, based on where your systems already run. Containers and automated releases keep deployment safe and repeatable. Databases, CRMs, ERPs and Internal APIs — We connect to SQL and NoSQL databases, CRMs, ERPs, and internal REST or GraphQL APIs. Your existing systems keep running as they do today.
An MCP server turns AI agent development and AI application development into working business tools. These are the five use cases where our clients see the fastest results from AI automation.
Teams ask questions in plain language and the assistant runs approved queries on your data warehouse and dashboards. Access rules keep sensitive data protected.
Agents check deployments, read logs, raise incidents, and handle routine requests through controlled tools. Sensitive actions can require human approval first.
Agents work with financial or clinical data under strict access control and full audit logging. Out of scope requests are blocked, which supports your compliance needs.
Employees ask questions and get answers from your documents, wikis, and policies. The MCP server checks each person's permissions, so they only see what they are allowed to see.
AI assistants look up order status, account details, and ticket history, then take actions such as creating or updating a ticket. Answers stay accurate because they come from live data.
Teams ask questions in plain language and the assistant runs approved queries on your data warehouse and dashboards. Access rules keep sensitive data protected.
Agents check deployments, read logs, raise incidents, and handle routine requests through controlled tools. Sensitive actions can require human approval first.
Agents work with financial or clinical data under strict access control and full audit logging. Out of scope requests are blocked, which supports your compliance needs.
Employees ask questions and get answers from your documents, wikis, and policies. The MCP server checks each person's permissions, so they only see what they are allowed to see.
AI assistants look up order status, account details, and ticket history, then take actions such as creating or updating a ticket. Answers stay accurate because they come from live data.
Teams ask questions in plain language and the assistant runs approved queries on your data warehouse and dashboards. Access rules keep sensitive data protected.
We build MCP servers for real business use, with testing, monitoring, and support included. Your server is ready to run on live data from day one.
Authentication, role based access control, and audit logging are part of every build. This helps your team meet internal security and compliance needs.
Our team covers AI agent development, AI application development, and AI agent integration services, along with web and app development. One team can build your MCP server and the AI products that use it.
We agree on scope, milestones, and delivery dates before work starts. You get regular progress updates, so there are no surprises at launch.
We build MCP servers for real business use, with testing, monitoring, and support included. Your server is ready to run on live data from day one.
Tell us which systems and AI agents you want to connect, and we will reply with a clear scope and next steps. Book a free MCP consultation and get a practical plan for your MCP server.
MCP server development is the process of building a server that follows the Model Context Protocol so AI agents and LLMs can use your data and tools. The server exposes your systems as clear tools with defined inputs and outputs. Any compatible AI client can then use them safely.
The timeline depends on how many systems, tools, and security rules your project needs. A focused MCP server with a few tools usually takes [add your real range] weeks from scope to launch. Larger projects with many integrations take longer, and we confirm milestones before work starts.
The cost depends on the number of tools, the systems you connect, your security and compliance needs, and the support level you choose. We give a fixed quote after a short scoping call, so you know the price before work begins. Contact us with your use case and we will share a clear estimate.
Yes, when it is built with the right controls. Our servers include authentication, role based access control, audit logging, and default blocking of out of scope requests. Sensitive actions can also require human approval before they run.
An MCP server works with any client that supports the Model Context Protocol, including Claude, OpenAI based tools, and Gemini through compatible clients. It also works with agent frameworks such as LangChain. You can add or switch models later without rebuilding your tools.
Yes, we connect your MCP server to databases, CRMs, ERPs, ticketing tools, and internal APIs. Your current systems keep running as they do today, because we build connectors around them instead of changing them. We can also migrate older AI scripts and custom integrations into one MCP setup.
A normal API connects one application to one system and often needs rework when the AI model or app changes. An MCP server exposes your systems once through a standard interface that many AI clients can discover and use. This gives you easier reuse, central security, and simpler maintenance.
Yes, we offer ongoing support that covers monitoring, security patches, protocol updates, and new tool requests. We track usage, errors, and response times so problems are caught early. You can choose a support plan that fits how fast your AI use cases grow.
