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 DeHor-Labs · MCP Server · ★ 306
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mcp-fiscal-brasil safe to install? View the security audit →
mcp-name: io.github.dehor-labs/mcp-fiscal-brasil A camada open source para agentes de IA trabalharem com compliance fiscal brasileiro 📚 Documentação · Instalação · Ferramentas
| Stars | 306 |
| Forks | 63 |
| Language | Python |
| Category | MCP Server |
| License | MIT |
| Quality Score | 67.1502212832716/100 |
| Open Issues | 13 |
| Last Updated | 2026-09-19 |
| Created | 2026-03-27 |
| Platforms | claude-code, mcp, python |
| Est. Tokens | ~17k |
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mcp-fiscal-brasil is Servidor MCP fiscal brasileiro: CNPJ, NF-e, NFS-e, CT-e, SPED, eSocial, Simples Nacional, Reforma 2026. 44 tools, zero-cadastro, tabelas offline. Python.. It is categorized as a MCP Server with 306 GitHub stars.
mcp-fiscal-brasil is primarily written in Python. It covers topics such as accounting, ai, brasil.
You can find installation instructions and usage details in the mcp-fiscal-brasil GitHub repository at github.com/DeHor-Labs/mcp-fiscal-brasil. The project has 306 stars and 63 forks, indicating an active community.
mcp-fiscal-brasil is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mcp-fiscal-brasil on Agent Skills Hub include mcp-brasil, openaccountants, youtube-connector-mcp. 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: