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Alternatives to mcp-sentinel

No user-submitted related servers found.

    Related Servers

    • A
      license
      A
      quality
      A
      maintenance
      Enables users to deterministically audit MCP servers for security issues across manifests, source, runtime responses, multi-call state, identity, and install scripts, and to run an inline gateway that gates tool calls and scans responses before returning them.
      3
      1
      Apache 2.0
    • A
      license
      Not graded
      quality
      A
      maintenance
      This MCP server enables security auditing for MCP configurations and AI agents, including prompt injection testing, data flow tracing, and security policy generation.
      47 npm
      MIT
    • A
      license
      B
      quality
      C
      maintenance
      Enables deterministic security testing of AI agents that use tools by serving synthetic MCP environments with poisoned data, fake secrets, and privileged actions. Records agent tool calls and evaluates security invariants (e.g., canary leaks, forbidden access, approval binding) without an LLM judge or real systems.
      8
      MIT
    • A
      license
      Not graded
      quality
      C
      maintenance
      Proxies MCP traffic between a client and a downstream server to enforce runtime policies on tool declarations, call arguments, and results, including allowlisting, sandboxing, secret and egress controls, and injection detection. It also includes a deterministic benchmark for measuring which security controls stop which attacks.
      MIT
    • A
      license
      Not graded
      quality
      F
      maintenance
      Enables transparent security for any MCP server by intercepting tool calls, blocking prompt injection attempts, masking PII in responses, and writing immutable audit logs.
      1,667 npm
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      This MCP server lets agents inspect every Model Context Protocol server on the machine, verify tools by running them in a sandbox, and check each call against a human-approved baseline so that changed or newly appeared tools are blocked before they execute. It also surfaces token cost, capability facts, and drift history for each tool entirely locally, without uploading inventory or requiring an account.
      1,065 PyPI
      Apache 2.0