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The Model Context Protocol mark inlaid in white enamel on a bevelled obsidian tile in a dark setting, for MCP servers

MCP Server and AI
Connector Development

Vivid Concept builds Model Context Protocol servers in Los Angeles that connect AI assistants to your data and systems securely, with a person confirming every change to your records.

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What MCP is and why assistants use it

An AI assistant becomes useful to a business once it can look things up and get things done in the systems you already run. We build that access as an extension of our work on connecting systems through APIs, using the Model Context Protocol, or MCP, an open standard for connecting AI applications to data and tools.

An MCP server stands in front of your systems and tells an assistant what it is allowed to read and what it is allowed to do. The assistant, whether ChatGPT, Claude or one built through AI application development, gets one checked, documented route into your business. You decide how wide that route is.

MCP began at Anthropic, which donated it to the Agentic AI Foundation, a fund under the Linux Foundation, on 9 December 2025, so it is an open standard shared across the industry, not one company's feature. A server built on it works with any assistant that supports the protocol.

A server exposes three kinds of capability. Tools are functions the assistant can call, such as look up an order or create a draft invoice. Resources are data it can read, such as a policy document or a client record. Prompts are reusable templates a person can start from.

A server can run on the same machine as the assistant or be reached securely over the web, so it fits however your team works. Claude supports custom connectors that point to a remote MCP server, and OpenAI's API and ChatGPT accept remote MCP servers as well.

IN-HOUSE ADVANTAGE

Connected With Our Full Los Angeles Team

Our developers build and test each connector, so an AI assistant works with your systems exactly the way you set it up.

What Is Included in MCP Server Development

A scoped list of the tools and resources your assistant needs

A working MCP server over Streamable HTTP, built on your API or database

OAuth-based authorization with the least privilege each role needs

A full action history and a person confirming any change to a record

Testing against real assistants, hosting and monitoring

Your MCP Server Development Process

  1. DiscoveryWe review your setup, audience and goals, and you receive a clear scope before work starts.
  2. DesignKey choices are settled with you before work starts.
  3. BuildBuilt by our in-house team with no handoffs.
  4. LaunchEvery change is tested and reviewed by a person before it goes live.
  5. SupportThe team that built it stays available after launch.

Designing tools, resources and prompts

Two engineers connect an assistant to a business tool on a laptop, one explaining the connection while the other nods.

Good MCP design is narrow design. A tool such as search orders by email and date works better than one that runs any query, because the assistant chooses correctly from clear names, precise descriptions and tight input rules.

Each tool we write is a small, single-purpose function: it validates every input, returns compact results and phrases errors so the assistant can recover. The strongest servers stand on a well-built interface, so where yours is missing or messy, this work pairs with custom API development from our software developers.

The tool list is a business decision as much as a technical one, because it defines exactly what an assistant can do inside your company. It deserves the same thought as giving a new employee the keys, which is why we settle the list with you before any code is written, with our technology consultants at the table when the choice is a big one.

A developer sketches a few narrow tools on a card for an assistant, a colleague nodding.

Secure sign-in, narrow scopes and a record of every call

For servers reached over HTTP, the MCP specification builds authorization on OAuth: the server acts as a resource server, the assistant as a client, and access tokens are issued for that one server and carry named scopes.

We use that model so a person signs in with your existing identity system and the assistant acts as that person, never as a master key. That is least privilege in practice: scopes limit each connection to the tools its role needs, and a server asks for a wider scope only when a task requires it.

Every call is recorded with the user, the tool, the inputs and the outcome, so any action can be traced to a person and a moment. The wider threat picture, including prompt injection through data an assistant reads, is covered by our web application security work.

A person confirms every change to your records

A security engineer and a developer check sign-in permissions on a laptop, careful and calm.

The specification says hosts must obtain explicit user consent before invoking any tool, and ChatGPT asks for confirmation before write actions by default in developer mode. Our servers build on that principle, so reading and changing are handled differently.

Reading is open to the roles that hold read access. Anything that creates, edits, sends or deletes becomes its own tool, flagged as consequential and routed through an approval step with a preview of exactly what will change. The person approving sees the result before it happens, the same principle behind AI-assisted design and imagery.

Repeat tasks and conversations can run on the same server. Automating routine work with AI agents adds scheduled and multi-step flows on top of it, AI chatbot and voice agent development builds the conversation, and the same approvals apply.

An office manager confirms a change on screen before it is saved, a colleague watching.

Testing, hosting and where MCP fits

Each server is tested against real assistants with scripted scenarios, including requests that should be refused, and those tests run again with every release. Hosting runs on infrastructure we monitor around the clock, with alerts on errors, latency and unusual call patterns, backed by our server maintenance service and the web design and development team behind the surrounding site.

MCP is one part of a wider plan. An AI readiness review sets which systems each assistant works with, AI search optimization makes your content findable by AI answers, and how we build AI for business shows how the pieces fit together. Because one team designs the server, the product around it and the rules it follows, every piece fits from the first release.

23 Years in Los Angeles

MCP Server Development Questions & Answers

What is the Model Context Protocol?

An open standard for connecting AI applications to data and tools. A server describes the tools, resources and prompts it offers, and any compatible assistant can use them. It was donated to the Linux Foundation's Agentic AI Foundation in December 2025, which keeps it open to every assistant.

Which assistants can use an MCP server?

Any assistant or developer tool with an MCP client. Claude supports custom connectors that point to a remote MCP server, and OpenAI's API and ChatGPT accept remote servers too. Your server is tested against the assistants your team actually uses.

How do you limit what an assistant can do?

In three layers. Each tool does one narrow job, OAuth scopes limit every connection to the tools its role needs, and actions that change records wait for a person to approve them from a preview. Every call is recorded with the user, the inputs and the result.

Can an MCP server wrap our existing API?

Yes, and it is the fastest route to a working connector. The server translates assistant requests into calls to your API, reuses its authentication and rules, and exposes only the operations you approve. Where no API exists yet, we build one first.

How is an MCP server hosted and monitored?

As a web service reachable over HTTPS, on infrastructure we monitor, with deployments from an automated release process, alerts on errors and latency, and a full activity history you can review. A server meant for one machine can instead run locally over standard input and output.

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