The State of AI Agent Tools — February 2026

The Numbers Tell a Story

At SkillsIndex, we set out to answer a deceptively simple question: how many AI agent tools actually exist, and how good are they?

After months of scraping, scoring, and cataloging, we now track 11,393 tools across five distinct ecosystems. That number alone is remarkable. A year ago, you could count the serious agent tools on two hands. Today, the landscape is an ocean — and most developers are navigating it without a map.

Here is what the data reveals.

Five Ecosystems, One Explosive Trend

The tools in our index break down across five ecosystems:

MCP Servers: 4,133 — The largest and fastest-growing category by far
OpenClaw Skills: 2,471 — The ClawSecure.ai registry, focused on enterprise-grade agent capabilities
GPT Actions: 1,818 — OpenAI's plugin successor, connecting ChatGPT to external APIs
IDE Plugins: 1,760 — VS Code (1,341) and JetBrains (419) extensions for AI-assisted coding
Claude Skills: 1,211 — Tools built for Anthropic's Claude, many open-source on GitHub

The dominance of MCP Servers is not a coincidence. It reflects the most significant shift in AI infrastructure since the transformer paper.

MCP: The Protocol That Won

The Model Context Protocol has experienced growth that defies normal adoption curves. Consider: MCP SDK downloads hit 97 million per month, representing a 970x increase over just 12 months. That is not linear growth. That is a phase transition.

Several factors converged to make this happen:

Universal adoption: OpenAI, Google, and Microsoft all adopted MCP as their standard for tool integration. When the three largest AI companies agree on a protocol, the industry follows. This kind of convergence is genuinely unprecedented in tech.
Governance maturity: In early 2026, MCP governance was donated to the Agentic AI Foundation under the Linux Foundation, co-founded by Anthropic, Block, and OpenAI. This removed single-vendor risk and signaled long-term stability.
MCP Apps: Launched in January 2026, MCP Apps introduced interactive UIs rendered directly inside chat interfaces. This moved MCP from "plumbing" to "platform" — a crucial distinction for adoption.

The result is clear: MCP is not just a protocol anymore. It is the de facto interoperability layer for AI agents.

The Quality Problem

Here is the uncomfortable truth hidden behind the growth narrative: the average overall score across all 11,393 tools is just 44.7 out of 100.

Our scoring methodology evaluates every tool on four dimensions:

Security (30% weight) — Does it request excessive permissions? Are there dangerous code patterns? Has it been audited?
Utility (30%) — Does it solve a real problem? How broad is its applicability?
Maintenance (25%) — Is it actively maintained? How responsive are the maintainers?
Uniqueness (15%) — Does it offer something no other tool provides?

A score of 44.7 means the median tool is, frankly, mediocre. The distribution is heavily skewed: a small number of excellent tools (scoring 80+) coexist with a long tail of abandoned experiments, weekend projects, and forks that add no value.

This is exactly why scoring and curation matter. When 65% of developers now use AI coding assistants weekly (per the Stack Overflow 2025 Developer Survey), the tools they connect to those assistants need to be trustworthy. An MCP server with filesystem access and no security review is not just unhelpful — it is a risk.

What the Top Categories Reveal

Looking at tool counts by category tells us what developers are actually building:

Database and data access — The largest single use case. Developers want their AI agents to query, summarize, and transform data without switching contexts.
Code execution and development tools — Reflecting the AI-assisted coding revolution. Tools that let agents read codebases, run tests, and manage git workflows.
Browser automation and web scraping — AI agents that can navigate the web, extract information, and interact with web applications.
Communication and collaboration — Slack, email, and calendar integrations that let agents participate in team workflows.
Search and knowledge retrieval — Connecting agents to web search, documentation, and internal knowledge bases.

The pattern is clear: developers are building tools that give AI agents the same capabilities they use daily — databases, browsers, code editors, and communication channels.

Looking Ahead

We are at an inflection point. The infrastructure is maturing (MCP governance, universal adoption), developer usage is mainstream (65% weekly AI coding), and the tool ecosystem is exploding (11,000+ and counting). But quality has not kept pace with quantity.

The next six months will separate the tools that endure from the tools that fade. Maintenance scores will diverge as casual projects get abandoned. Security will become a gating factor as enterprises adopt agent tooling. And the tools that combine real utility with solid engineering will rise to the top.

We built SkillsIndex to make that signal visible. Browse the full directory to see where your tools — or your competitors — stand.

Data current as of February 23, 2026. We re-score the entire index weekly.