MCP server development

An MCP server exposes tools, data and prompts from one of your systems through the Model Context Protocol, an open standard for connecting AI applications to outside systems. Any host that speaks the protocol, such as a chat assistant, a coding tool or your own agent, can discover the tools and call them through the same contract, so you build the integration once.

When more than one AI application should use the same system, or when you want people to reach it from the assistants they already use. A single agent calling a single API can use plain function calls. Once a second agent, an assistant or a partner needs the same actions, an MCP server saves you building and securing the integration again for each one.

Any client that implements the protocol: desktop and web chat assistants, coding tools and IDEs, and agents your team builds with frameworks such as Pydantic AI. They do not behave the same way. Hosts differ in how they pick tools, pass arguments, show results and ask the user to confirm, so we test every release on the hosts your people actually use.

Remote servers authenticate users with OAuth as the MCP specification describes, and every tool runs with that user's scoped permissions. Rate limits, input validation and an audit log of every call sit in the server. Tool results are treated as untrusted content, because text returned by one tool can carry instructions aimed at the model.

Yes, if the server owns the rules. Hosts differ in whether and how they ask the user before a tool runs, so the server cannot rely on them. Irreversible steps such as placing an order, sending a payment link or deleting data require an explicit confirmation the server checks itself. We spoke about this at AGNTCon + MCPCon 2026, from a payment MCP server we built.

With tools that search and filter on the server and return only what the task needs, paged and in a compact shape. The model asks for products that match, gets a handful back and asks for a quote on one of them. The full catalog never reaches the model's context, which keeps answers accurate and calls cheap.

Without blocking the conversation. A tool that finishes within a few seconds returns its result directly. Longer work, such as generating a design, returns a job ID, and the host checks it with a single polling tool until the result is ready. We built a print platform's MCP server this way, so design generation never times out the host. The MCP specification (2025-11-25) also defines experimental Tasks for long-running work.

A local server runs next to the host on a user's machine and suits developer tools and files on that machine. A remote server runs in your cloud over Streamable HTTP, with central authentication, logging and updates, and suits company systems shared by many users. Most of our servers for company systems are remote.

Vstorm is the first agentic AI engineering company in the Agentic AI Foundation, the Linux Foundation project that now hosts the Model Context Protocol. We build MCP servers for client systems, and our engineers spoke at AGNTCon + MCPCon North America 2026 about running a payment MCP server across four different hosts.

The server code, tool schemas, tests per host, deployment configuration and audit logs, all in your repository and your cloud account, with documentation and runbooks for adding the next tool. Your team can extend the server or connect new hosts without us.