Turn your API into AI-native tools for ChatGPT & Claude
We wrap your existing REST or GraphQL API in a Model Context Protocol (MCP) server so ChatGPT, Claude, and other MCP-aware clients can call it directly—securely, with real business outcomes.
Keep your existing API
No rebuild. We map your current endpoints, auth, and data model into clean MCP tools.
Enterprise-grade controls
OAuth, rate limits, scopes, and auditability tuned for real production systems.
AI-ready in weeks, not quarters
From “we have an API” to “users can call it inside ChatGPT & Claude” on a focused project timeline.
The reality today
Your API is powerful. But AI can’t see it—yet.
- • Users copy/paste IDs, URLs, and JSON between your app and ChatGPT.
- • Every integration is a one-off script, plugin, or brittle scraping setup.
- • Security teams hate the idea of giving AI direct DB access.
- • Product wants “AI features”, but you don’t want a second backend to maintain.
Our approach: MCP-native integration
Wrap your API in MCP tools ChatGPT & Claude can call safely
We don't bolt on a generic chatbot. We expose your API as a set of carefully-designed MCP tools. That means:
- • AI assistants discover your capabilities from tool schemas.
- • Every action is a structured call into your API, not prompt hacking.
- • You keep your existing auth, permissions, and observability stack.
How we connect your API to ChatGPT & Claude with MCP
A focused implementation project: from your OpenAPI or Postman collection to MCP tools deployed in your environment and registered in AI clients.
Audit & intent mapping
We review your API surface (OpenAPI, Postman, gRPC, or docs) and map real user intents to a smaller set of high-value actions.
MCP tool design
We group endpoints into AI-friendly MCP tools (query, update, admin). Each tool has clear JSON Schemas, guardrails, and descriptions.
MCP server implementation
We implement an MCP server in your preferred language (TypeScript, Python, Go, C#, PHP, Rust) and connect it to your existing auth and APIs.
ChatGPT & Claude integration
We register your MCP server as a connector in ChatGPT / Claude and validate real-world flows with your sample users and data.
The AI never talks directly to your database. It calls structured tools on the MCP server, which in turn calls your existing API with your existing permissions model.
Why use MCP instead of plugins, web scraping, or custom glue?
MCP is emerging as the standard way for AI assistants to talk to tools and APIs. That matters for durability and discoverability.
Fragile approaches
- Custom plugins: tightly coupled to one platform, hard to maintain across models.
- Web scraping / HTML: brittle whenever you change your UI.
- Direct DB access: security and governance nightmare, especially with write operations.
- Per-model integrations: N×M problem as you add more models and tools.
MCP-native integration
- Single protocol, many models: one MCP server can be used by ChatGPT, Claude, IDE agents, and future AI hosts.
- Explicit tool schemas: tools are documented in JSON Schema so models can reliably discover and call them.
- Separation of concerns: AI does reasoning, your API does business logic, MCP mediates between them.
- Future-proof surface: as more platforms adopt MCP, your API is already “AI-native” instead of tied to a single plugin system.
Where this is going: the AI-native internet
We're moving from a web of pages built for humans to click through, to a mesh of services that AI agents can discover and call directly.
From web pages to AI-callable services
Historically, websites exposed HTML and humans clicked buttons. In an AI-native world, your core functionality is exposed as tools that agents can call directly, no UI required.
MCP as the “USB-C” of AI tools
MCP is rapidly becoming the common connector: one standard that lets AI assistants plug into many apps, operating systems, and enterprise platforms. Building an MCP server is how your API joins that ecosystem.
Agents, not just chatbots
As operating systems, IDEs, and enterprise platforms adopt MCP, agents will orchestrate work across multiple services. Exposing your API via MCP means it becomes part of that agentic fabric.
What you actually receive
Clear deliverables you can run, extend, and operate—not a black-box “AI integration”.
MCP server implementation
A production-ready MCP server in your chosen language, containerized and wired to your staging / production environments.
Tool schemas & documentation
JSON Schemas, usage guidelines, and examples so your own teams (or other integrators) can extend the toolset confidently.
ChatGPT & Claude wiring
Configured connectors in ChatGPT / Claude (as appropriate), plus example prompts and flows your team can use immediately.
Security & governance recommendations
We align the integration with your authentication model, logging strategy, and compliance posture (GDPR/SOC2-friendly patterns).
Premium integration, not a plugin shop
This is high-skill B2B work that touches your core systems. We price based on scope, complexity, and long-term impact—not chat volume or seat count.
API → MCP Integration
Connect My API to ChatGPT & Claude
Investment: premium, by proposal
Typical clients are B2B SaaS, platforms, and enterprises where a single integration unlocks meaningful revenue or efficiency.
What's usually included
- • API audit & intent mapping workshop
- • MCP server design & implementation
- • ChatGPT / Claude connector setup
- • Security review & observability hooks
Optional extensions
- • Multi-tenant / multi-region architectures
- • Ongoing AI ops and iteration retainer
- • Additional MCP tools & domains
- • Support for other AI hosts and agents
If your API is a core part of how customers or employees get work done, an MCP integration can turn it into something AI agents actively use—not just something humans click around.
Request a scoping callReady to make your API AI-native?
Share your API docs, your main use cases, and where you want ChatGPT or Claude to plug in. We'll come back with a concrete MCP integration plan and timelines.