Model Context Protocol engineering for AI-ready products
Codersarts builds Model Context Protocol (MCP) servers and clients that let AI assistants such as Claude, ChatGPT, Cursor and Copilot use your product's data and actions safely. Our engineers design MCP tools, resources and prompts, add OAuth and per-user permissions, deploy remote MCP servers, and test them across AI clients, so your product shows up wherever customers now work with AI.
What Is Model Context Protocol Development?
Model Context Protocol development is the building of servers and clients that follow MCP, an open standard for connecting AI applications to external tools and data. It covers designing tools, resources and prompts, local and remote transports, OAuth 2.1 authorization, per-user permissions, input validation, rate limiting, logging, testing with multiple AI clients, publishing to MCP registries and maintaining servers as the specification evolves.
MCP has become the common way AI assistants connect to business software. A SaaS product with a well-designed MCP server can be used directly from Claude, ChatGPT and AI coding tools without custom integrations for each.
MCP Development at a Glance
Item | Detail |
|---|---|
What we build | Remote MCP servers for SaaS products, internal MCP servers, MCP clients and agent tool layers |
MCP features | Tools, resources, prompts, streamable HTTP, OAuth 2.1 and elicitation |
AI clients | Claude, ChatGPT, Cursor, VS Code Copilot and custom agents |
SDKs | TypeScript, Python, Java, C# and Go |
Engagement | Fixed-price MCP server, security audit, maintenance or ongoing support |
What we can do with MCP
Service | What it covers |
|---|---|
SaaS MCP server development | Your product's API exposed as AI-ready tools |
Internal MCP servers | Company data and systems available to AI assistants |
Tool design | Clear names, schemas and descriptions models use correctly |
Authentication | OAuth 2.1, scoped tokens and per-user access |
Remote deployment | Hosted MCP servers with monitoring |
ChatGPT and Claude apps | Listing-ready AI app experiences |
MCP security review | Prompt injection, permissions and data exposure checks |
Maintenance | Spec updates, client changes and new tools |
What are you trying to accomplish with MCP?
Goal | How we help |
|---|---|
Be AI-ready | Let customers use your product from AI assistants |
Win deals | Meet buyer requests for MCP support |
Connect internal data | Give AI assistants governed access to company systems |
Stay secure | Permissions and safeguards on every tool |
Reach new channels | Appear in AI app directories |
Keep working | Stay compatible as MCP evolves |
What can we build with MCP?
Application | Example |
|---|---|
SaaS product servers | CRM, project, billing or analytics actions in Claude |
Knowledge servers | Search over docs, tickets and wikis |
Database servers | Read-only, governed SQL access for AI |
DevOps servers | Deployments, logs and incidents from AI coding tools |
Workflow servers | Approvals and automations triggered by AI |
Agent tool layers | Standard tools for in-house AI agents |
MCP solutions for different teams
Team | Typical need |
|---|---|
SaaS companies | A public MCP server for customers |
Enterprise IT | Internal MCP servers with access control |
Developer tool companies | MCP for AI coding assistants |
AI product teams | MCP clients and tool orchestration |
Agencies | MCP capacity for client products |
Get the MCP expertise you need
Role | Focus |
|---|---|
MCP engineer | Servers, tools and transports |
AI engineer | Tool design and agent behavior |
Security engineer | Auth, permissions and threat review |
Back-end engineer | API integration and hosting |
MCP technology ecosystem
From MCP requirement to production
Scope. Choose the user tasks AI assistants should perform.
Design. Define tools, resources and permission scopes.
Build. Implement the server on your existing API.
Secure. Add OAuth, validation and rate limits.
Test. Check behavior in Claude, ChatGPT and coding tools.
Launch and maintain. Deploy, monitor and keep up with spec changes.
MCP in production: what we take care of
Task-focused tools rather than one tool per API endpoint.
Per-user authorization so AI acts only with the user's rights.
Confirmation for destructive actions like deletes and payments.
Prompt injection safeguards on tool outputs.
Audit logs for every tool call.
Compatibility tests across major AI clients.
How you can work with Codersarts
Model | Best for |
|---|---|
A production MCP server for your product | |
An app experience inside ChatGPT | |
Reviewing an existing server | |
Ongoing updates and support |
Why Codersarts for MCP?
Strength | What it means for you |
|---|---|
Early MCP experience | Servers built on current best practice |
Security focus | Safe access for AI assistants |
SaaS understanding | Tools designed around customer tasks |
Multi-client testing | Works in Claude, ChatGPT and coding tools |
Flexible capacity | Fixed-price build or ongoing support |
Related MCP solutions
Solution | What it covers |
|---|---|
Fixed-scope MCP server build | |
Security review for MCP servers | |
Autonomous AI assistants | |
Claude development |
Frequently Asked Questions
What MCP services does Codersarts provide?
We provide SaaS and internal MCP server development, tool design, authentication, remote deployment, ChatGPT and Claude apps, MCP security reviews and maintenance.
What is the Model Context Protocol?
MCP is an open standard that lets AI assistants connect to tools and data through a common interface, instead of one-off integrations.
Why does our SaaS product need an MCP server?
Customers increasingly work inside AI assistants. An MCP server lets them use your product there, which helps adoption and buyer evaluations.
Can you build an MCP server on our existing API?
Yes. We wrap your existing API with task-focused tools and your current authentication.
How do you secure an MCP server?
We use OAuth 2.1, scoped permissions, input validation, confirmations for risky actions, rate limits and audit logs.
Which AI clients will our MCP server work with?
We test with Claude, ChatGPT, Cursor, VS Code Copilot and other MCP-compatible clients.
How long does an MCP server take to build?
A focused remote MCP server for a SaaS product usually takes a few weeks, depending on tools and auth.
Do we own the code?
Yes. All code and documentation are yours.
Have an MCP requirement?
Tell us about your product and what AI assistants should be able to do. We will reply with an approach, timeline and price.