mcp-fiscal-brasil: Servidor MCP fiscal brasileiro: CNPJ, NF-e, NFS-e, CT-e, SPED, eSocial, Simples Nacional, Reforma 2026

by DeHor-Labs · MCP Server · ★ 335

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

How to install mcp-fiscal-brasil

pip install mcp-fiscal-brasil
claude mcp add fiscal-brasil -- mcp-fiscal-brasil

Taken from the project's README on GitHub.

Security audit verdict: SAFE · quality 67/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours.

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

accountingaibrasilbrazilclaudecnpjesocialfiscalllmmcp

Quick Facts

Stars335
Forks71
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score67/100
Open Issues17
Last Updated2026-10-23
Created2026-03-27
Platformsclaude-code, mcp, python
Est. Tokens~17k

About mcp-fiscal-brasil

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

Compatible Skills

These tools work well together with mcp-fiscal-brasil for enhanced workflows:

  • norman-mcp-server — semantic(0.75)+same_lang+similar_pop+shared_platform (55%)
  • shinkoku — semantic(0.53)+complementary+same_lang+shared_platform (54%)
  • korean-jangbu-for — semantic(0.41)+rare_topics+same_lang+similar_pop+shared_platform (49%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. 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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