MCP Security Crisis: Two Open-Source Frameworks Solving the Agent Security Problem

9.93% of MCP servers have description-code inconsistencies. Leading models suffer ~100% attack success under tool description poisoning. Here are two frameworks that actually solve this.

The Problem

The Model Context Protocol (MCP) has become the standard interface for connecting LLMs to external tools. As of mid-2026, the ecosystem encompasses over 2,200 public MCP servers. But the security landscape is dire:

• 
  9.93% of MCP servers have description-code inconsistencies — the tool description says one thing, the code does another (Shi et al., 2026)


• 
  ~100% attack success rate under tool description poisoning on leading models (Liu et al., 2026)


• 
  53.7% security drop on 27B models under multi-agent attacks — larger models are MORE vulnerable, not less (McAllister et al., 2026)

The repo sits between the agent and ALL MCP servers. Every tool call passes through 7 sequential verification phases:

The architecture is defense-in-depth: each phase catches what the previous one might miss. Phase 3 (DCI Checker) directly addresses the 9.93% inconsistency rate found by Shi et al. Phase 4 (TDP Detector) catches the tool description poisoning that Liu et al. showed has ~100% success rate.

Stats: 127+ tests. Python 3.10/3.11/3.12. AGPL-3.0. Production-ready.

Solution 2: Agent Fixer Stage

Lightweight output verification for multi-agent AI workflows.

Both projects are open-source, tested, and production-ready. Feedback and contributions welcome.

License: AGPL-3.0-or-later