"Coze" matching MCP servers:

Security analysis for Python codebases through the guard-core engine: SecurityConfig validation that catches keys pydantic silently drops, docs search over the vendored corpus, live detection probing, and verified setup guidance for Python, Typescript, Rust, PHP and Go adapters.

Strict AI code reviewer powered by Groq. Finds bugs, vulnerabilities and security issues in your code. Supports analyze, compare versions, explain code, generate tests, and HTML reports.

Experimental Python MCP server exposing traceguard_shield_exec to evaluate proposed tool calls through an authorization hook. Returns approval or rejection messages over stdio; it does not execute the requested tools.

Deterministic AI safety policy engine with Z3 formal verification. Write, verify, simulate, and enforce machine-verifiable safety constraints for AI agents. Completely outside the LLM.

A security filter that blocks dangerous code patterns by comparing normalized structural syntax trees against a blacklist of known threats using vector embeddings. It acts as a gatekeeper to prevent malicious code execution by identifying dangerous structures regardless of specific identifiers or literals.

Checks a code repository without running it: Safe, Works and Complete scores with an A-F grade, catching leaked keys, dangerous calls, invented or lookalike packages, broken imports and missing project files. Standard-library Python; MCP tools scan, orphans and explain.

Provides comprehensive management of Microsoft 365 services including Exchange, SharePoint, Teams, Azure AD, Intune device management, security & compliance frameworks, and universal access to 1000+ Microsoft Graph API endpoints with advanced features like batch operations, delta queries, and real-time webhooks.

Enables automated pull request review, security vulnerability analysis, and code quality improvement suggestions through any MCP client.

Enterprise-grade code intelligence server for AI assistants that analyzes codebases for due diligence, tech debt, security vulnerabilities, and code quality.

Enables AI agents to interact with Fortinet FortiManager through a minimal MCP interface using sandboxed JavaScript for querying the API spec and executing JSON-RPC calls.

A local capability layer for AI agents providing persistent memory, credential management, connectors, and activity tracking.

MCP server providing a searchable security knowledge base for AI coding assistants, enabling retrieval of secure-coding instructions and feature-specific security guidance.

An MCP server that proxies tool calls to a running SentinelX Core instance with OIDC/OAuth authentication. It enables secure integration of SentinelX Core's HTTP agent capabilities through the MCP protocol.

Enables an agent or MCP client to scan MCP server configs and AI-agent tool manifests on demand, exposing tools that list detection rules and report hidden prompt injections, data-exfiltration attempts, embedded secrets, deceptive Unicode, unpinned packages and shell-launching tools. Each finding comes with a severity and location so risky tool descriptions can be caught before an attacker exploits them.

The only Multi-LLM Compliance Engine (GPT-4o + Claude + DeepSeek). Auto-fix GDPR/LGPD risks and more 15 frameworks. code.guard.eu

MCP server that provides AI coding assistants with access to call graphs, data flows, and security analysis for multi-file vulnerability detection.

Provides shared MCP-server machinery for Flask apps, including streamable HTTP transport, token-based auth, caller identity resolution, tool registry with permissions, and audit logging, enabling developers to build consistent and secure MCP servers.

A production-ready MCP gateway and control plane that provides credential vault, policy engine, audit logging, and managed runtime for routing tool calls between AI agents and downstream MCP servers.

Deterministic zero-trust policy enclave, fail-closed schema validator, DPDP Act 2023 PII sanitizer, and cryptographic WORM audit ledger for MCP tools.

A governed MCP server that enforces a trust layer between AI agents and databases, requiring sign-off on joins and metrics and producing auditable receipts for every query.