The Model Context Protocol (MCP) is an open standard, introduced by Anthropic, that lets AI assistants connect to tools and data sources through a common interface. Its costs come from building or configuring MCP servers, hosting them, model usage, security work and maintenance. Return comes from time saved on tasks that previously required copying data between systems, so ROI must be measured per workflow, not for “MCP” as a whole.
What is MCP and why are companies adopting it?
Before MCP, connecting an AI assistant to a CRM, a database or a file store usually meant a custom integration for each assistant and each tool. MCP defines a client-server model: an MCP server exposes tools, resources and prompts, and any MCP-compatible client, such as Claude, ChatGPT, Cursor or other assistants that support it, can use them.
Adoption grew because the same server can serve several assistants. A company that builds one MCP server for its analytics warehouse can let analysts query it from their preferred AI tool. Many vendors now publish official MCP servers for their products, which lowers the starting cost.
What are the main cost components?
Budget lines fall into one-time and running costs. Most teams underestimate the running side, especially model usage and security reviews.
- Discovery and design: choosing workflows, defining which tools and data an assistant may access
- Build or setup: configuring vendor MCP servers or writing custom ones in TypeScript, Python or another SDK
- Hosting: running remote servers on a cloud platform, with authentication and logging
- Model usage: tokens consumed when assistants call tools and read results
- Security: access control, OAuth setup, permission scoping, review of tool descriptions
- Maintenance: updates when APIs change, monitoring, user support
- Training: teaching staff which tasks to give the assistant and how to check results
How much does building an MCP server cost?
Costs vary too much for a single number, so think in effort bands. Using an existing official server from a vendor may take hours of configuration and testing. A simple custom server that wraps two or three read-only API endpoints may take a developer a few days, including tests. A server that writes data, handles OAuth for many users and connects to internal systems can take weeks, mainly because of security and edge cases.
Use your own developer day rate to turn those bands into a budget, and add a margin for security review. Read-only servers are cheaper and safer to start with; write access should come after the team trusts the workflow.
Do tokens become expensive?
They can. Every tool call returns data that the model reads, and large results consume many tokens. A tool that returns an entire CRM export when the user needed five records wastes money on every call. Design tools to return small, filtered results, add pagination and test token usage per typical task before rolling out.