reshapr — security grade SAFE, quality 71/100

Security audit verdict: SAFE · quality 71/100

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 reshaprio · MCP Server · ★ 128

Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h

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About reshapr

) Welcome to reShapr 🚀 Website [Community](https://reshapr.io/community

agent-toolsai-agentsai-gatewayai-infrastructureai-integrationai-nativecontext-optimizationcontext-windowdeveloper-toolsgraphql-to-mcp

Quick Facts

Stars128
Forks36
LanguageJava
CategoryMCP Server
LicenseApache-2.0
Quality Score71.2570914052931/100
Open Issues53
Last Updated2026-10-02
Created2026-03-04
Platformsjava, mcp
Est. Tokens~17k

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Frequently Asked Questions

What is reshapr?

reshapr is The open source, no-code MCP Server for AI-Native API Access. It is categorized as a MCP Server with 128 GitHub stars.

What programming language is reshapr written in?

reshapr is primarily written in Java. It covers topics such as agent-tools, ai-agents, ai-gateway.

How do I install or use reshapr?

You can find installation instructions and usage details in the reshapr GitHub repository at github.com/reshaprio/reshapr. The project has 128 stars and 36 forks, indicating an active community.

What license does reshapr use?

reshapr is released under the Apache-2.0 license, making it free to use and modify according to the license terms.

What are the best alternatives to reshapr?

The top alternatives to reshapr on Agent Skills Hub include entroly, mcpproxy-go, LAP. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.

How this security grade is produced

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:

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