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MCP servers that let AI assistants work with your systems, safely.
MCP is an open protocol: build one server offering tools, resources and prompts, and any compatible AI client can use it.
The Model Context Protocol (MCP) is an open protocol, introduced by Anthropic in November 2024, that standardises how AI applications connect to outside systems. Before MCP, every AI tool needed its own custom plug-in for every app. With MCP, you build one MCP server for your system and any compatible client can use it.
An MCP server offers three kinds of things to the AI client:
list_invoices, create_lead or schedule_post, each with a typed input schema and a description the model reads to decide when to use it.The server runs either locally next to the client (over stdio) or as a web service the client connects to over HTTP, with the user signing in through OAuth. The specification and official SDKs are public, so an MCP server you commission is not tied to us or to one AI vendor.
Each of our products exposes its features as MCP tools. A user connects their AI assistant and works with the product in plain language, under the same permissions they have in the app.
Reads books and GST summaries; proposed entries wait for a person to confirm.
Fint's MCP tools let an assistant read books, ledgers, outstanding receivables and GSTR-1 and GSTR-3B summaries, and propose expenses, deposits and draft invoices. Proposals land in Fint's Inbox; nothing is recorded until a person confirms it. The server enforces that rule, not the prompt.
CRM and project tools that act in one organisation at a time.
Desk's tools cover the CRM and project wings: search clients and leads, create contacts and opportunities, log calls, schedule meetings, update project tasks. One login can hold several organisations and the tools act in one at a time, so an assistant never mixes up two companies.
Drafts, media, captions and schedules prepared by an assistant, never published on their own.
Cybe Social's tools let an assistant create drafts, upload media, suggest captions and hashtags, schedule posts and read post analytics. Drafts never publish on their own, so an assistant can prepare a week of posts for a person to review.
One well described tool per real task, not dozens of thin database endpoints.
One tool per real task, named the way a user would describe it, with descriptions that say when to use it and when not to. Forty thin CRUD endpoints confuse a model; twelve well described tasks do not.
Permissions and approvals live in server code, which a model cannot talk around.
Permissions, approvals and limits are enforced by the server code. The model can be talked into anything; the server cannot. That is why Fint's tools can only propose.
Trimmed results with IDs for the next tool; large files go straight to storage.
Results are trimmed to what the model needs, with IDs it can pass to the next tool. Large files go through upload tools that move bytes directly to storage instead of through the conversation.
Each result states its consequence plainly, such as saved as draft, not published.
A tool result states the consequence plainly, for example "saved as draft, not published", so the assistant reports the right thing back to the user.
User requests grouped into tools marked read only, draft creating or excluded.
Which things should a user be able to ask an assistant to do in your system? We group them into tools and decide which are read only, which create drafts and which are excluded.
Local stdio for internal and developer use, remote with OAuth for customers and staff.
A local stdio server suits internal tools and developer use. A remote server with OAuth suits customers and staff using Claude on the web or on a phone.
The server reuses your existing API's authentication and permission checks.
The MCP server usually sits in front of your current API or database and reuses its authentication and permission checks. If there is no API, we build a narrow one first.
Tool names and descriptions rewritten until the model reliably picks the right one.
We run realistic requests through Claude and other clients, read the tool calls they make, and rewrite tool names and descriptions until the model picks the right tool reliably.
Every tool call logged, with rate limits and per role scopes set.
Every tool call is logged with the user, inputs and result. Rate limits and per role scopes are set, and the server is deployed where your other services run.
Software companies that want their product usable from AI assistants, and businesses that want their staff to use Claude or a similar assistant on internal systems (CRM, ERP, ticketing, a database) without copying data in and out by hand.
No. Anthropic introduced it, but it is an open specification and many AI clients and developer tools support it. A server built to the spec works with any compliant client.
It is as safe as the server's rules. Each user signs in with their own account, sees only what they could see in the app, and the server limits what the tools can change. We default to read-only tools and draft-style writes.
An MCP server is the set of tools. An agent is the program that decides which tools to call. One MCP server can be used by many agents and assistants. See AI agent development.
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