Verdict L402

Ask if an action is safe before your agent runs it: injection, shell, MCP installs. Free samples.

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
100%
Qualität der Benennung
80%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~567Tokens (Tool-Definitionen)
~934 BTypische Antwortgröße
Minimale Auswirkung auf die Aufmerksamkeit (0.44% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "verdictl402": {
      "command": "npx",
      "args": [
        "@kechlab/verdict-mcp"
      ]
    }
  }
}

Ausführbare Pakete

npm@kechlab/verdict-mcp0.2.1stdio

Remote-Endpunkte

https://verdict.kechlab.com/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (4)

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🟢verdict_guard(text)

Free sample. Before your agent reads untrusted text (web page, email, document, tool output), check it for prompt injection, data exfiltration requests, secrets and toxic content. Returns allow, review or block with scores and reasons. Use it on text you did not write. Do not use it to judge an action: use verdict_firewall.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "text": {
      "type": "string",
      "description": "The untrusted text to examine (4000 characters maximum in free samples)."
    }
  },
  "required": [
    "text"
  ]
}
🟢verdict_command_gate(command, intent)

Free sample. Before a coding agent runs a shell command, ask whether to allow, review or block it: root deletion, download and run, privilege escalation, secret exfiltration. Pass the human intent when you know it. Not a sandbox: it reads the command, it does not run it.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "command": {
      "type": "string",
      "description": "The shell command the agent wants to run."
    },
    "intent": {
      "type": "string",
      "description": "Optional. What the human asked the agent to do."
    }
  },
  "required": [
    "command"
  ]
}
⚪verdict_mcp_vet(name, description, tools, permissions)

Free sample. Before installing an MCP server, skill or tool: is its description poisoned (hidden orders to the agent, excessive permissions, look-alike names)? Returns allow, review or block with reasons.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "name": {
      "type": "string",
      "description": "Name of the tool or server."
    },
    "description": {
      "type": "string",
      "description": "The description text it exposes to the agent."
    },
    "tools": {
      "type": "array",
      "description": "Optional list of { name, description } of the functions it exposes.",
      "items": {
        "type": "object"
      }
    },
    "permissions": {
      "type": "array",
      "description": "Optional permissions requested, for example filesystem:write, network, shell.",
      "items": {
        "type": "string"
      }
    }
  },
  "required": [
    "name",
    "description"
  ]
}
🟢verdict_firewall(goal, content, action)

Free sample. Before an agent acts on untrusted content (pay, send, delete, click, reply), give the human goal, the content it read and the action it proposes. Returns allow, review or block: it catches actions that follow orders hidden in the content.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "goal": {
      "type": "string",
      "description": "The objective the human gave."
    },
    "content": {
      "type": "string",
      "description": "The untrusted content the agent read."
    },
    "action": {
      "type": "string",
      "description": "The action the agent wants to run (tool call, command, payment)."
    }
  },
  "required": [
    "goal",
    "content",
    "action"
  ]
}

Community

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Nachweis

Aktuelle Beobachtungen

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