英語 · ロシア語 (RU)
Pentool 向け MCP サーバー — Web ペネトレーションテストのためのローカル AI アシスタント
あらゆる MCP クライアントに AI 機能をもたらす、自己完結型の stdio JSON-RPC 2.0 サーバー。特に Pentool 向けです（BYO-LLM: チェックの選択、WAF バイパス、非自明なエンドポイントの発見）。uv で 1 コマンドでインストールできます。
MCP は、LLM モデルとツールを接続するためのオープンプロトコルです。pentool-mcp-server は Pentool の MCP レイヤーを再利用可能な PyPI パッケージとしてカプセル化します：ローカル接続（stdio）、ネットワーク不要、ポート不要、数秒でインストールでき、重い依存関係もありません。
Related MCP server: pentestMCP
このサーバーは Pentool の MCP コンポーネントです。プロフェッショナルな Web セキュリティテスト端末（Burp 互換プロキシ、スキャナー、スパイダー、イントルーダー — すべて TUI 内で完結）であり、Pentool の AI を支えます：
🎯 特定のターゲットに適した スキャンチェックの選択;
🛡 WAF バイパス / ペイロードの提案;
🕷 スパイダークロール中に 非自明なエンドポイントを発見。
フルスタックを試すには:
uv tool install \"pentool[ai]\"→pentool ai setup→pentool。
# Install uv (if not present): https://docs.astral.sh/uv/
curl -LsSf https://astral.sh/uv/install.sh | sh
# Standalone — just the MCP server
uv tool install pentool-mcp-server
pentool-mcp-server --version
# Or together with Pentool and its AI extras
uv tool install "pentool[ai]"ヘルスチェック (stdio):
echo '{"jsonrpc":"2.0","id":1,"method":"ping"}' | pentool-mcp-server
# → {"jsonrpc": "2.0", "id": 1, "result": {"status": "ok"}}uv は標準のインストールパスです — 分離された環境で、まさに Pentool 自体がインストールされる方法と同じです。サーバーをシステム Python の外に保ちます。
サーバーは ポートを開かず、ローカルサブプロセスとして実行されるため、どのホストでも安全です。uv を使えばシステム Python の外に留まります：
uv tool install pentool-mcp-server完全なユーザーガイド — docs/GUIDE.md。ロシア語版 — README.ru。
ツール | 説明 |
| MCP プロトコルのハンドシェイク |
| 利用可能なツールの一覧表示 |
| タスクに対する LLM 回答を生成 ( |
| 準備状態の確認（モデルがインストールされているか） |
| GGUF モデルのパスを指定 |
| プロセスの生存確認 |
デフォルトではネットワークなし。 pentool-mcp-server はローカルプロセス内の stdio（stdin → stdout）のみで待ち受けます。外部アクセスは不可能です — 他のプロセスからも、別のホストからも。
認証なしで TCP/0.0.0.0 に公開しないでください。 外向きにネットワークポートで実行すると、stdin/ポートに書き込める全員が tools/call generate を利用できます（リソース使用 + ターゲットデータの LLM への送信）。MCP には 組み込みの認証がありません — 127.0.0.1 にバインドし、ファイアウォールでゲートしてください。
ターゲットのプライバシー。 データ（URL、ペイロード）がホストを離れるのは、外部 LLM プロバイダーが接続されている場合のみです。ローカルの GGUF モデルでは、トラフィックはマシン内に留まります。
詳細 — docs/GUIDE.md。
stdio JSON-RPC 2.0 サーバー (initialize / tools/list / tools/call / ping)
generate、health、configure ツール
model.py 内の実際の LLM ランナー (llama-cpp-python)
HMAC 認証付きのオプション TCP モード
AGPL-3.0 — Pentool と同じライセンスです。
Принять параметры конфигурации сервера (напр. путь модели).
| Name | Required | Description | Default |
|---|---|---|---|
| model | No | Путь к GGUF-файлу модели |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must fully disclose behavior. It only says 'accept parameters' without explaining side effects, persistence, validation, or restart requirements. This is insufficient for a configuration tool.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no wasted words. It is concise and to the point, though it sacrifices valuable behavioral detail for brevity.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool has one optional parameter, no output schema, and no annotations, the description still leaves key context missing: what configuration does, whether it is required for sibling tools, and what happens after invocation. It is too sparse to be fully complete.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description's mention of 'model path' merely mirrors the schema's own description for the 'model' parameter. No additional meaning is added, so the baseline score of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states it accepts server configuration parameters with an example (model path), which clearly identifies the tool's purpose. It is distinct from siblings 'generate' and 'health', though the verb 'accept' is somewhat passive and the exact effect is implied rather than explicit.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives, nor are prerequisites or ordering requirements (e.g., configure before generate) mentioned. The description implies usage context but gives no explicit when/when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Сгенерировать ответ LLM по задаче. payload — строка запроса (данные цели), model — путь к GGUF-файлу модели (необязательно). При отсутствии модели вернёт ошибку setup required.
| Name | Required | Description | Default |
|---|---|---|---|
| task | Yes | Имя задачи (напр. choose_checks, crawl_endpoints) | |
| model | No | Путь к GGUF-файлу модели | |
| payload | Yes | Данные цели / контекст (JSON-строка) |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden. It discloses that the model parameter is optional and that omitting it causes an error ('setup required'), which is useful behavioral insight. However, it does not mention whether the tool is read-only or has side effects (e.g., logging), or any rate limits or auth requirements. The behavior is partially transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is very concise at two sentences, front-loading the main purpose. Every phrase adds value, though the error clause could be more specific (e.g., 'returns an error' instead of 'вернёт ошибку'). No extraneous text, well-structured for quick parsing.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description should clarify what the tool returns (e.g., generated response text). It does not, leaving a gap. With only 3 params all documented in schema, the description is adequate for basic use but incomplete for understanding return behavior or error responses beyond the 'setup required' case.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100%, and the description adds some value by explaining that model is optional and payload is the query data. It does not elaborate on 'task' beyond the example values in schema, and does not clarify the format of payload (JSON string) beyond the schema. As coverage is high, baseline 3 is appropriate given the minimal added context.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states the tool generates an LLM response for a task, specifying the payload as the query string and model as an optional GGUF file path. It clearly identifies the verb (генерировать) and resource (ответ LLM по задаче), and distinguishes from siblings like 'health' and 'configure' by its focus on generation rather than status checks or setup.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when an LLM response is needed for a task, and mentions that missing model leads to a 'setup required' error. However, it does not explicitly state when to use this vs. 'configure' (e.g., to set the model first) or when not to use it (e.g., if no model is configured). The sibling tools are named but no direct comparison is made.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Проверка готовности: вернёт статус, установлена ли модель.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden. It indicates the tool is read-only (returns status) and non-destructive, but does not disclose error behavior, return format details, or potential side effects. The description is minimally transparent.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, short sentence that is front-loaded and contains no redundant information. Every word earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's simplicity, the description is mostly adequate for a health check. However, it lacks specification of the return value format (e.g., boolean, JSON) and does not mention any edge cases. With no output schema, more detail would improve completeness.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The tool has zero parameters and schema coverage is 100% (trivially). Per the baseline rule for 0 parameters, the description does not need to add parameter semantics, and it appropriately omits any.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool performs a readiness check and returns whether the model is installed. The verb 'Проверка готовности' (readiness check) and resource 'model' are specific. However, it does not explicitly differentiate from sibling tools 'generate' and 'configure', though the purpose is distinct enough.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus alternatives. There is no mention of preconditions, context, or scenarios where 'health' is appropriate compared to 'generate' or 'configure'.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
v0.1.0configuregeneratehealthScored across 3 tools
Each tool has a clearly distinct purpose: 'generate' for LLM inference, 'health' for readiness checking, and 'configure' for setting parameters. There is no overlap or ambiguity between them.
All tool names are simple, lowercase verbs that accurately describe their action ('generate', 'health', 'configure'). The pattern is uniform and predictable.
With 3 tools, the count is appropriate for a minimal MCP server focused on LLM inference. It could potentially benefit from a status tool or a list-models tool, but the current set is sufficient for its core purpose.
The tool surface covers basic lifecycle: configure (setup), health (readiness), and generate (infer). However, there is no tool for cancelling generation, listing available models, or inspecting current configuration, which are notable gaps for a production server.
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