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AMEOBIUS-space

MCP Permission Guard

by AMEOBIUS-space
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Python

MCP Permission Guard — Pre-Action Authorization for AI Agents

Intent-based permission system with risk scoring, rule engine, and audit trail. Zero dependencies, pure Python stdlib.

The Problem

Agents execute destructive actions without guardrails. Binary allow/deny prompts don't work (93% auto-approved). The "lethal trifecta" — private data + untrusted content + external communication — creates risks no single tool can detect.

Related MCP server: blackwall-mcp

The Solution

MCP Permission Guard classifies tool calls into 14 intent categories, scores risk 0-100, evaluates rules, and produces deterministic allow/deny/ask decisions with full audit trail.

Tools (11)

Tool

What it does

classify_intent

Map tool call → intent category (14 types)

risk_assess

Score 0-100 with lethal trifecta detection

register_rule

Add allow/deny/ask rule by intent

evaluate

Full evaluation: classify + risk + rules → decision

audit_log

Query decision history

list_rules

Show all registered rules

remove_rule

Delete a rule

set_policy

Global policy: allow_all / deny_all / ask_all / rules_based

get_policy

Read current policy

get_stats

Decision statistics

reset

Clear all state

Intent Categories

filesystem_read (10) · filesystem_write (40) · filesystem_delete (80) · network_outbound (50) · network_inbound (30) · code_execution (60) · shell_command (70) · database_write (65) · database_read (20) · credentials_access (90) · config_change (55) · user_management (85) · service_restart (60) · package_install (75)

Tests

python -m pytest tests/ -v  # 38 tests, all passing

Inspiration

License

MIT — AMEOBIUS

This server cannot be installed

A
license - permissive license
-
quality - not tested
C
maintenance

How are these scores calculated?

Maintenance

–Maintainers
–Response time
–Release cycle
–Releases (12mo)
Commit activity

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Related MCP Connectors

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