The State of MCP Servers in 2026
Six months ago, the Model Context Protocol was a niche idea from Anthropic — a way for Ai models to talk to external tools. Today, there are nearly 1,500 MCP-compatible projects in the wild, and the ecosystem is growing fast.
We track the MCP ecosystem at AiList, pulling data from GitHub, npm, and multiple MCP registries. Here’s what the numbers tell us about where MCP stands right now.
The Numbers
As of February 2026, AiList tracks 1,496 projects across 10 categories. The breakdown:
| Category | Count | Share |
|---|---|---|
| MCP Servers | 1,238 | 83% |
| APIs | 101 | 7% |
| Web Apps | 67 | 4% |
| Agents | 29 | 2% |
| Models | 25 | 2% |
| CLI Tools | 18 | 1% |
| Libraries | 12 | <1% |
| Plugins | 4 | <1% |
| Datasets | 2 | <1% |
MCP servers dominate at 83%. That makes sense — the protocol was designed for tool integration, and developers have been building integrations for everything from databases to design tools to blockchain wallets.
What Developers Are Building
The most active areas of MCP development right now:
Developer tools are the largest subcategory. GitHub’s official MCP server has over 27,000 stars. Chrome DevTools MCP has 26,000+. Tools like Serena (20,500+ stars) provide semantic code retrieval and editing. The pattern is clear: developers want their Ai coding assistants to interact directly with their existing toolchains.
Workflow automation is the second wave. Projects like n8n-mcp (13,800+ stars) and Trigger.dev (13,800+ stars) connect Ai models to automation pipelines. Activepieces (20,900+ stars) lets you connect any MCP client to a hosted server for no-code workflows.
Research and analysis tools are growing quickly. GPT Researcher (25,300+ stars) conducts autonomous deep research. TrendRadar (46,900+ stars) handles multi-platform trend monitoring with RSS aggregation and Ai analysis.
Creative and 3D tools are the surprise category. Blender MCP (17,200+ stars) gives Ai models natural language control over 3D scene creation. UI-TARS (28,100+ stars) from ByteDance is an open-source multimodal agent stack.
The Language Split
TypeScript is the dominant language for MCP servers, which makes sense given the protocol’s JavaScript SDK origins. Python is a strong second, particularly for research-focused and data-heavy servers. A handful of projects are built in Rust, Go, and even .NET — Microsoft’s MCP for Beginners curriculum (14,400+ stars) covers five languages.
Where to Find MCP Servers
The ecosystem is still fragmented across multiple registries:
- npm — The largest source. Most MCP servers are installable via
npx. - GitHub — Search the
mcp-servertopic for thousands of repos. - Smithery — A registry focused on hosted MCP servers.
- Glama, mcp.so, MCPServers.org — Community-run directories.
- AiList — Aggregates from all sources into one searchable directory. Also available as an MCP server itself, so your Ai agent can search the directory directly.
What’s Next
A few trends to watch:
Enterprise adoption is accelerating. GitHub, Microsoft, and ByteDance all have major MCP projects. When the big players invest, the ecosystem follows.
Multi-agent orchestration is emerging. Projects like Claude Flow (14,300+ stars) enable swarm intelligence and multi-agent coordination over MCP. This is early, but it’s where things are heading.
The framework layer is solidifying. FastAPI MCP (11,500+ stars) lets you expose any FastAPI endpoint as an MCP tool with one line of code. As these framework integrations mature, building MCP servers will get dramatically easier.
Memory and persistence are becoming table stakes. Ai models that forget everything between sessions are increasingly frustrating. Persistent memory systems — like CogmemAi for Claude Code — are filling this gap.
The MCP ecosystem went from zero to nearly 1,500 projects in under a year. At this rate, we’ll see 5,000+ by the end of 2026. The question isn’t whether MCP will become the standard protocol for Ai tool integration — it’s how fast.
Browse the full directory: AiList — 1,400+ Ai Projects
