MCP Ecosystem Report — June 2026
We generate this report monthly from our live database of 39,762 MCP servers. Here's the state of the ecosystem as of June 2026. Ecosystem Pulse Metric Value Total servers indexed 39,762 Active (p
We generate this report monthly from our live database of 39,762 MCP servers. Here's the state of the ecosystem as of June 2026.
Ecosystem Pulse
| Metric | Value |
|---|---|
| Total servers indexed | 39,762 |
| Active (push within 30d) | 100% |
Grade Distribution (Additive Model)
Our scoring combines Quality Score + Community Bonus + Trust Bonus.
| Grade | Count | % | What it means |
|---|---|---|---|
| B+ | 1,123 | 2.8% | Very good — close to elite |
| B | 7,187 | 18.1% | Good — solid quality + some community |
| C+ | 5,477 | 13.8% | OK — decent quality |
| C | 21,464 | 54.0% | Average — good foundation, no community signal |
| D | 4,351 | 10.9% | Needs work |
| F | 160 | 0.4% | Critical |
Key insight: 54% of MCP tools have solid code quality but zero community adoption. They're invisible to AI agents. The path from C to B is clear: get 10+ GitHub stars + stay active.
What Changed This Month
We rebuilt our scoring engine from static analysis only to three-dimensional additive model. The old system scored 85.7% of tools as Grade B — useless for differentiation. The new model creates natural spread by incorporating community signals (stars, activity, official status) and trust data as bonuses on top of quality scores.
Methodology
Full details: https://agent-tool-intel-production.up.railway.app/scoring/methodology
Check your server's grade: https://agent-tool-intel-production.up.railway.app
GitHub (open source): https://github.com/agent-tool-intel/agent-tool-intel
Report auto-generated by Agent Tool Intelligence. Published monthly.
Originally published by Dev.to AI. Aggregated on AIWithGhost for educational purposes — full credit and traffic to the original publisher.