No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by itsmostafa · MCP Server · ★ 127
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is typesafe-mcp safe to install? View the security audit →
Typesafe MCP Give your AI agent typed judgments instead of free text. is an MCP server that lets Claude Code, Claude Desktop, and Codex call TypeSafe's Jev model and get back probabilities they can branch on. One command, , registers it with all three. Why this exists Problem: When an agent needs a yes/no call, a routing decision, or a severity rating, it usually asks an LLM, then parses prose and hopes the format holds. The answer has no probability attached, so the agent cannot tell a confident "yes" from a coin flip. Solution: exposes one tool, , that sends state plus typed questions to Jev and returns structured answers with probabilities. Nothing to parse and no prompt formatt
| Stars | 127 |
| Forks | 15 |
| Language | Go |
| Category | MCP Server |
| License | MIT |
| Quality Score | 70.8711503334541/100 |
| Last Updated | 2026-09-20 |
| Created | 2026-09-17 |
| Platforms | go, mcp |
| Est. Tokens | ~7k |
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typesafe-mcp is mcp connector to give your AI agent direct access to typesafe ai's jev model. It is categorized as a MCP Server with 127 GitHub stars.
typesafe-mcp is primarily written in Go. It covers topics such as mcp, mcp-tools.
You can find installation instructions and usage details in the typesafe-mcp GitHub repository at github.com/itsmostafa/typesafe-mcp. The project has 127 stars and 15 forks, indicating an active community.
typesafe-mcp is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to typesafe-mcp on Agent Skills Hub include director, mcpstore, volcano-agent-sdk. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: