How are AI Agents used? Evidence from 177,000 AI agent tools

Recent years have seen the rise of AI agents , large language models (LLMs) that take multi-step actions on behalf of users. These systems have improved rapidly, and can now send emails, browse the web, execute code, and make financial transactions, often with minimal human oversight.

How are these AI agents actually being used in the real world? Despite growing adoption, little public data exists on where and how agents are being deployed. To address this gap, we analysed 177,436 AI agent tools published between November 2024 and February 2026 by monitoring public Model Context Protocol (MCP) server repositories. In this blog post, we outline our approach and key findings.

MCP is the dominant open standard for giving AI agents access to external tools and services. It works like a universal plug: an MCP server wraps a specific capability, whether that is a Google Calendar, a cryptocurrency wallet, or a web browser, and makes it available to any compatible AI agent. Because these servers are published on public platforms like GitHub, tracking them provides visibility into what tools developers are building for agents and how widely those tools are being adopted.

Most MCP tools are currently used for software development and other IT tools: Tools for software development and IT account for 67% of all published tools and 90% of downloads, suggesting that most agents are used to accelerate technical workflows However, 14% of tools support finance and business management tasks, and tools exist for domains including scientific research, healthcare, education, and legal compliance.

Most action tools support medium stakes tasks, but finance is an outlier. Using O*NET impact ratings, we find that most action tools support medium-stakes occupations such as computer systems administration. Finance is one exception to this rule: high-stakes occupations including financial services agents, accountants, and financial managers, have more action tools than predicted by the overall pattern. MCP servers with payment execution capabilities grew from 46 in January 2025 to over 1,200 in January 2026, with a notable concentration in cryptocurrency tools that enable direct, potentially irreversible transactions.

The scale of AI agent tooling is rising quickly: Over the last year, the number of publicly released MCP tools rose from around 5,000 to 177,000, accompanied by a substantial increase in download activity (from 80,000 to 14 million).

Agents are shifting from observation to action. Early in the period, most tools supported perception or analysis tasks such as reading files or querying data sources. By late 2025, download patterns had shifted toward tools that allow agents to take direct actions in external systems, such as executing code or using computers Action‑enabling tools now represent the majority of use, especially for tools released by established companies.

Agents are increasingly operating in unconstrained environments. General purpose tools operating in unconstrained environments like the open web grew from 41% to 50% of downloads. Critically, 95% of general-purpose tool downloads involved action capabilities. This means that potentially consequential agent actions are increasingly occurring in the least controlled environments – such as agents browsing the web or controlling a computer – rather than through restricted, secure API integrations.