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AgentSentinel MCP Gateway

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    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables autonomous agents to run shell and subprocess commands through a guardrail that blocks destructive system mutations, path traversals, and reverse shells. It also adds prompt-injection detection, spend circuit breakers, loop detection, and privacy sanitization so agent execution stays within safe, budgeted bounds.
      7
      MIT
    • A
      license
      A
      quality
      B
      maintenance
      Enables agents to score, inspect, and gate tool calls before they execute, resolving shell commands the way a shell would in order to block destructive actions and detect multi-step attack sequences. It also redacts credentials and PII in either direction, requires signed single-use approval tokens for high-impact actions, and keeps a tamper-evident audit trail.
      6
      70 npm
      Apache 2.0
    • A
      license
      Not graded
      quality
      B
      maintenance
      Enables autonomous agents to enforce financial guardrails by tracking real-time token burn and cost velocity, automatically throttling or freezing execution when budgets are exceeded. It also blocks destructive shell commands, prompt injections, and runaway planning loops while sanitizing outputs before they are returned.
      7
      MIT
    • F
      license
      A
      quality
      B
      maintenance
      Enables AI agents to safely interact with GitHub, query Postgres read-only, check deployment status, and search/read files within an allowlisted sandbox—all with dry-run writes and SQL/path safety gates.
      8
      -
    • A
      license
      Not graded
      quality
      C
      maintenance
      Enables LLM agents to access third-party APIs through a least-privilege set of audited tools with allowlists, per-caller scopes, label gates, write confirmation, limits, and PII-masked logging. It restricts reads to labeled data, prevents sending or destructive actions, and records every allowed or denied call.
      MIT
    • A
      license
      Not graded
      quality
      A
      maintenance
      Enforces policy controls for AI agents, including spend limits, action approvals, kill switch, scoped credentials, dry-run diffs, loop prevention, and auditable hash-chained logs.
      MIT

    TDQS

    A3.7/5.0

    Scored across 5 tools

    Disambiguation5/5

    Each tool targets a clearly distinct security capability: shell execution, filesystem operations, network fetching, policy pre-checking, and audit inspection. Although safe_exec could overlap with safe_fs or safe_fetch via shell commands, the sandbox descriptions make the intended boundaries clear.

    Naming Consistency4/5

    All tools use snake_case with a consistent sentinel_ prefix, which is highly predictable. The pattern shifts slightly from sentinel_safe_<action> for operational tools to sentinel_<noun>_<verb> for policy and audit tools, but remains readable and coherent.

    Tool Count5/5

    Five tools is well-scoped for a security gateway, covering execution, filesystem, network, policy, and audit without redundancy. Each tool earns its place in the minimal surface.

    Completeness4/5

    The core agent action surfaces are covered, plus policy evaluation and audit inspection. Minor gaps exist: safe_fs lacks explicit delete/remove, and there is no tool to manage policies or reset circuit breakers, but agents can likely work around these limitations.

    Maintenance

    ActivityMaintained
    ResponsivenessNo issues