AI services

MCP Servers & AI Integrations

Put your product or data in front of ChatGPT, Claude and other AI hosts through the Model Context Protocol. Secured with OAuth 2.1 and tested against each host's rules.

At a glance

  • MCP servers for search, data and actions in your product
  • OAuth 2.1 with Entra, APIM or your identity provider
  • Conformance with the rules of each AI host
  • Load, security and evaluation testing before launch

Your website and your product have a new kind of visitor: AI agents. The Model Context Protocol (MCP) is how ChatGPT, Claude, VS Code, Gemini CLI and a growing list of hosts let an agent search, read and act on an outside system. An MCP server decides what an agent can see and do with yours, and who controls that.

Who it's for

  • SaaS companies whose customers already ask ChatGPT or Claude questions the product could answer
  • Marketplaces, booking and commerce businesses that want agents to search and transact, not scrape
  • Enterprises exposing internal data and tools to their own AI assistants, safely

What we build

  • Remote MCP servers that expose search, discovery, records and actions from your existing APIs, with the tool design that makes an agent use them well.
  • Authentication and authorization. OAuth 2.1 with PKCE, Entra External ID or your identity provider, API Management in front, scoped tokens, and per-user permissions carried through to every tool call.
  • MCP Apps and rich results. Tool results that render as cards, lists and forms inside the host, where the host supports it.
  • Host compliance. Tool names, annotations, descriptions, timeouts and payload sizes that satisfy the different rulebooks of the MCP specification, Claude, ChatGPT, VS Code, Cursor, Kiro and Gemini CLI, and the connector-directory submissions that follow.
  • Testing and operations. Inspection, conformance checks, evaluation sets, load tests, OpenTelemetry tracing and cost dashboards.

How it's done

We start from the questions your users ask an agent, not from your API surface. A good MCP server has few tools, each named and described so a model picks the right one, each returning compact, well-typed results. We prototype against a real host within the first two weeks, then harden: auth, rate limits, observability, abuse cases.

Our founder leads the architecture of a global hospitality brand's guest-facing MCP platform, which exposes hotel search, discovery and booking through ChatGPT and Claude, and publishes comparisons of MCP server tooling, testing tools and host requirements that we use in our own work.

Proof

The research we publish and work from, including a comparison of 44 MCP tools for websites, the rules eight AI hosts apply to an MCP server, and 31 MCP testing tools compared, is at pournasserian.com/writing.

Engagement shape

A two-week design sprint that produces a tool design, an auth design and a working prototype against one host, then a production build.

Frequently asked questions

Should our product have an MCP server?

If your customers already use ChatGPT or Claude to ask questions your product could answer, or if agents are scraping your site to do it badly, yes. If nobody is asking yet, a short assessment tells you whether to build now or wait.

Which hosts matter?

For most products, ChatGPT and Claude first, because of reach and because their connector directories bring users. Developer products add VS Code, Cursor and the CLIs. Each host applies its own rules, which we design for from the start.

How is authentication handled?

OAuth 2.1 with PKCE is the standard the hosts expect. We put an identity provider such as Entra External ID behind it, an API gateway in front, and carry the user's permissions into every tool call, so an agent can never do more than the person it acts for.

How do you test an MCP server?

Protocol conformance, each host's rules, evaluation sets that check the model picks the right tool with the right arguments, load tests, and security tests for prompt injection through tool results.

Related services

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Role-based agents that act on real systems: tool calling, memory, structured replies and human approval where it matters. Built on Microsoft Agent Framework, LangChain and OpenAI-compatible models.

RAG & Knowledge Systems

Retrieval built for your material, not generic chunking: hybrid search, GraphRAG, code-aware indexing and incremental refresh on pgvector, Azure AI Search or Neo4j.

AI-Native Product Development

A product designed around the model from day one, with the platform engineering to run it: multi-tenancy, queues, observability and cost control.

Talk to the people who would build it

Tell us what you are trying to do with mcp servers & ai integrations. We will come back with an honest take and a plan.

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