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An open standard that lets any compatible AI assistant use your system through one server.
An MCP server wraps a system such as a database or CRM, and any AI app connecting as a client can use what it offers.
The Model Context Protocol (MCP) is an open protocol, introduced by Anthropic in November 2024, for connecting AI applications to outside systems. In December 2025 Anthropic donated MCP to the Agentic AI Foundation, a fund under the Linux Foundation. The specification lives at modelcontextprotocol.io.
An MCP server wraps a system (a database, a CRM, a file store, an internal API) and describes what it offers. An AI application such as Claude, an IDE or your own agent connects as an MCP client and can then use those capabilities.
The AI app opens one client connection per MCP server it uses.
The AI application (host) starts one client connection per MCP server it uses.
The server lists its tools, readable resources and reusable prompts.
The server lists what it offers: tools (actions the model can call), resources (data it can read) and prompts (reusable templates).
The host sends tool calls as JSON-RPC messages and gets results back.
When the model wants to act, the host sends a tool call to the server as a JSON-RPC 2.0 message and gets a result back.
Local servers use standard input and output; remote servers use HTTP with authorisation.
Local servers usually talk over standard input and output; remote servers use HTTP, with authorisation handled by the host and server.
One MCP server works from Claude, coding tools and your own agent.
An MCP server for your order system works from Claude, from coding tools and from your own AI agent, without rewriting the connector.
The server decides which actions exist; the model reaches nothing else.
The server decides exactly which actions exist and what each may do. The model cannot reach anything the server does not expose.
An MCP server lets customers use your software from their own assistant.
Shipping an MCP server lets customers use your software from their own assistant.
Fint, Desk and Cybe Social have MCP servers. Through Fint's, an assistant can read ledgers but only propose a bill for a person to confirm.
Our products Fint, Desk and Cybe Social each have MCP servers. A user connects, say, Fint to their assistant and asks "which invoices are outstanding for this customer?". The assistant calls Fint's tools to read the ledger. When the user asks it to record a bill, the server only allows a proposal, which a person must confirm in Fint. The rules live in the server, not in the model's goodwill.
One tool per API route gives too many choices. Design a few task-shaped tools.
Mapping every API route to a tool gives the model too many choices. Design a few task-shaped tools with clear descriptions.
Returned emails, pages and records may hide injected instructions. Treat them as data.
Text returned by a server (emails, web pages, records) can contain injected instructions. Treat it as data.
A local MCP server runs code on your machine. Review it like any software.
A local MCP server runs code on your machine. Review it as you would any software you install.
Function calling is how a model asks for a tool inside one application. MCP standardises how tools are described and served, so the same tools can be plugged into many applications.
No. It is an open standard with SDKs in several languages, and many AI clients and developer tools support it.
Yes, MCP server and tool development is one of our services. Start with a consultation and a list of the actions you want an assistant to be able to take.
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