Analysis of 2,181 Remote MCP Server Endpoints Shows Reliability Issues

✍️ OpenClawRadar📅 Published: April 5, 2026🔗 Source
Analysis of 2,181 Remote MCP Server Endpoints Shows Reliability Issues
Ad

A recent analysis of 2,181 remote-capable MCP server endpoints reveals significant reliability issues across the ecosystem. The automated health checks were conducted against servers from the official registry, mcp.so, PulseMCP, and Smithery.

Key Findings

  • 52% of endpoints are completely dead (timeout, connection refused, 404)
  • 37% respond but require authentication (401/403)
  • 9% are confirmed up and healthy
  • 1.5% are degraded (slow or intermittent errors)
  • Among live servers, 516 maintain 99%+ uptime
  • 58% of servers with GitHub repos haven't had a commit in 30 days

Category Breakdown

  • Dev-tools has the most servers (1,238)
  • Finance servers have the worst average latency (2,558ms)
  • Security servers have the lowest average uptime at 27%

Fastest Servers Found

  • GitHub MCP: 101ms
  • Timescale pg-aiguide: 104ms
  • Supabase: 109ms

The full dataset is available for further analysis, and the author is open to questions about methodology or specific servers.

📖 Read the full source: r/LocalLLaMA

Ad

👀 See Also

Claude Code v2.1.157: Auto-Load Plugins from .claude/skills, Improved Agents & Worktrees
News

Claude Code v2.1.157: Auto-Load Plugins from .claude/skills, Improved Agents & Worktrees

Claude Code v2.1.157 automatically loads plugins from .claude/skills, adds claude plugin init scaffolding, honors agent setting in settings.json, and fixes numerous bugs across agents, worktrees, and terminal integration.

OpenClawRadar
Sarvam AI releases 30B and 105B open-source LLMs with Indian training infrastructure
News

Sarvam AI releases 30B and 105B open-source LLMs with Indian training infrastructure

Sarvam AI has open-sourced Sarvam 30B and Sarvam 105B, two reasoning models trained from scratch in India on compute provided under the IndiaAI mission. Both models use Mixture-of-Experts architecture with sparse expert routing and are optimized for efficient deployment across hardware from GPUs to laptops.

OpenClawRadar
Anthropic Reverses Policy on Third-Party Agent SDK and claude-p, Cuts Effective Inference Value by 25-40x for Max Subscribers
News

Anthropic Reverses Policy on Third-Party Agent SDK and claude-p, Cuts Effective Inference Value by 25-40x for Max Subscribers

Anthropic reversed its ban on third-party agents using subscription credentials but moved claude-p and the Agent SDK to a separate, non-rollover credit pool billed at API rates, reducing effective inference value by 25-40x for Max subscribers.

OpenClawRadar
Local vs Cloud Models: Qwen-3.6-27B, Gemma-4-31B, Claude Haiku, Codex-Spark on Hard Code Gen
News

Local vs Cloud Models: Qwen-3.6-27B, Gemma-4-31B, Claude Haiku, Codex-Spark on Hard Code Gen

A user tested Qwen-3.6-27B (q4_k_m) locally on an RTX 5080 against API-based Gemma-4-31B, Claude Haiku 4.5, and Codex-Spark on a complex code task. Only Codex-Spark produced complete code (but with import errors); all others failed partially. Cost: Gemma used $0.112 for 803k input tokens.

OpenClawRadar