Custom MCP Server
Connect your product to ChatGPT, Claude, Cursor, Perplexity and other AI platforms through a custom-made MCP server
We take full ownership of your MCP server development, helping you move from idea to launch in record time.Your product becomes available through the AI tools your customers already use. We build a dedicated integration layer on top of your existing architecture - zero changes to your core codebase, fully secure and production-ready.
Choose the Right MCP Server for Your Product
Three complete packages designed for different levels of product complexity
Best for Start-ups and SMB products with modern REST APIs
Best for SaaS products with multiple services and more complex APIs and integrations
Best for large organizations with legacy systems and complex infrastructure
What's Inside the Week-1 Assessment
Day 1
Kickoff & discovery
We meet your team to understand your product, your APIs and how your users (or their AI tools) should interact with it. We align on goals, constraints and what a successful MCP server looks like for you.
Days 2-3
API & architecture review
Our team analyzes your existing APIs, data and workflows, identifies what can be exposed to AI agents, and designs the right MCP server architecture, including authentication and permission boundaries.
Day 4
Scope & roadmap
Next, we define which tools and resources to build, how to sequence them, and what production deployment will require. You get a clear implementation roadmap with scope and timeline.
Day 5
Review & handover
We walk you through the findings, the proposed architecture and the roadmap. You leave with a technical assessment of your product’s AI-readiness, a recommended MCP server architecture, a prioritized implementation roadmap, and a fixed-scope quote for Phases 2-3.
What Your Product Can Do With an MCP Server
More reach across AI tools. Faster adoption. One integration instead of many.
Reach more users
Let people use your product right inside ChatGPT, Claude, Cursor and other AI tools.
Accelerate customer adoption
Cut the setup that slows customers down. With an MCP server, users connect once and start right away.
Reduce integration costs
Build one MCP server and connect across the growing MCP ecosystem instead of maintaining multiple AI integrations.
How MCP Servers Work in Real Products
See how AI assistants discover, understand and interact with your software through an MCP server.
Retrieve portfolio data, execute financial operations, generate reports and answer users’ questions.
Access workout history, create personalized plans and recommend exercises based on user goals.
Search content libraries, publish articles and retrieve digital assets.
Search products, check inventory, create orders and answer customer questions.
Recommend courses, track progress and issue certificates.
Search availability, compare options and create bookings.
What We Don't Build
The MCP space is full of prototypes and thin wrappers. We build the opposite.
Not another AI wrapper.
Not a demo.
Not a prototype.
Not generic AI consulting.
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Production-ready MCP infrastructure, built specifically for your product
Not sure if your product needs an MCP server?
See integrations example
Custom MCP Server for Agent-Ready Fintech API Integration
A banking-infrastructure company (card issuance, payments, FX, Apple/Google Pay) whose clients, including banks, neobanks and fintech startups, build on top of its APIs.
Before
As the client base grew, developers kept hitting the same integration questions: which endpoints to use, in what order, how to handle edge cases. The documentation existed, but the AI tools developers actually work in couldn’t reach it. So every question landed in the support queue, and each answer had to go through the support team.
What we built
A custom MCP server that exposes the platform’s APIs and documentation to AI tools like Claude and Cursor — permission-aware, with read-only access for guidance and guarded, authorized access for actions.
after
The same integration questions now resolve directly inside the AI tools developers already use. They self-serve and integrate independently, and client developers ship faster, with no changes to the underlying APIs. The result: around 42% fewer support tickets, driven by the drop in repetitive integration questions.
Why choose Touchlane for your custom MCP server
We don't just build MCP servers - we help products become AI-native. Every server we deliver is guided by major principles that ensure long-term value, adoption and maintainability.
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Deep backend and API engineering
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Experience with AI-powered systems
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Fast delivery cycles
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Focus on usability & long-term maintainability
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End-to-end ownership from architecture to launch
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Security & compliance control
Your MCP Product Lifecycle
We take full ownership of your MCP server, from first assessment to long-term support. You get a product that keeps working and evolving, not a one-off delivery.
Map your product, APIs and AI-readiness, and define the roadmap.
Design the server structure, authentication and permission model.
Build the tools and resources that expose your product to AI agents.
Ship the server to your infrastructure, production-ready and secure.
Publish to the MCP Registry so AI clients can discover and connect to it.
Hand over full source code and walk your team through running it.
Keep the server maintained and evolving as your product and the MCP ecosystem grow.
Start with your product assessment
Whether you're planning your first MCP server or improving an existing one, we'll help you define the right architecture, scope and implementation approach.
FAQ
We are eager to clarify anything that you might be interested in.
MCP (Model Context Protocol) is an open standard that lets AI assistants and agents connect to external products, APIs and data in a structured, secure way. Instead of every tool building its own custom integration, MCP gives them one common language to discover what your product can do and to act on it. An MCP server is the component that exposes your product through this standard.
An MCP server makes your product usable directly inside AI tools like ChatGPT, Claude and Cursor. Without one, AI assistants can’t reliably discover or operate your APIs. With one, your product becomes something users can access from the AI tools they already work in, which opens a new distribution and adoption channel with no changes to your core product.
Your API is built for developers who read documentation and write integration code. An MCP server is built for AI agents: it describes your capabilities in a way models can understand, handles permissions and safe execution, and lets an assistant use your product without a custom integration for each client. In short, an API is something people integrate; an MCP server is something AI tools can use directly.
Any MCP-compatible client, including Claude, Cursor, ChatGPT and a growing list of AI assistants and coding environments. Because MCP is an open standard, one server works across all of them, so you don’t maintain a separate integration per tool.
Most MCP servers launch in about 2 to 4 weeks depending on complexity. We offer three fixed packages (Essential, Professional and Enterprise) starting at $8,999. You can see the full breakdown in the packages section above.
Yes. Where relevant, we publish your server to the MCP registry, the public catalog of available MCP servers, so AI clients can find it. Registry publication is included in our packages.
Yes. We build permission-aware servers with authentication, scoped access, validation and audit logging in line with the MCP specification’s security requirements. Sensitive or write operations can require explicit authorization and additional guardrails, so AI tools only do what you allow.