AIエージェントに、いつも推測してしまうポスト量子移行の事実を与えましょう。
MCP上で6つのツールを提供します。資格情報のサイズ、フラグメント数、再組み立てウィンドウ、39ファミリーの障害分類、ベンチマークスコアリングです。Claudeに*「私たちのML-KEM-768ハンドシェイクはBLE MTUに収まるか？」*と尋ねると、推定ではなく答えを計算します。
📖 完全なドキュメント、チュートリアル、概念ガイド: https://nickharris808.github.io/pqc-toolkit/
エージェントはますますPQC移行作業を行うようになっていますが、重要な点について自信を持って間違っています。資格情報が実際にどれだけ大きいか、それがいくつのフラグメントになるか、そしてあなたの並行性で安全な再組み立て上限が存在するかどうか。これらは判断ではなく算術です。だからエージェントに算術を渡しましょう。
ここでのプロトコル層は依存関係がありません。MCPは行区切りのstdio上のJSON-RPC 2.0であり、直接実装するのに十分小さく、インストールを簡単に保ちます。
Related MCP server: attestix
pip install git+https://github.com/nickharris808/pqc-migration-mcpこれにより、pqc-sizes と pqc-mfb もそれぞれのリポジトリから取得されます。まだPyPIにはないため、pip install pqc-migration-mcp は現在機能しません。
# talk to it directly -- it is line-delimited JSON-RPC on stdio
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | pqc-migration-mcpclaude_desktop_config.json に追加します:
{
"mcpServers": {
"pqc-migration": {
"command": "pqc-migration-mcp"
}
}
}Claude Desktopを再起動します。6つのツールがコネクタの下に表示されます。
ツール | 回答 |
| KEM+署名の資格情報は、コンポーネントごとに何バイトですか？ |
| このトランスポートではいくつのフラグメントになりますか？また、フラグメンテーションは現在必須ですか？ |
| 安全な容量上限はそもそも存在しますか？存在しない場合、どの並行性なら機能しますか？ |
| 全39の障害ファミリーと、ケース数および公開された類似例 |
| このファミリーでは何が壊れ、どの設計で、それぞれが何をしましたか？ |
| PQC-MFB提出物をスコアリング: カバレッジ、回帰、ゼロカバレッジのファミリー |
トランスポートは行区切りのJSONです。各行に完全なオブジェクトが1つあります。リクエストは1行に保ってください。2行にまたがるリクエストは2つの不完全なものとして到着し、2つの-32700解析エラーとして返されます。
$ echo '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"reassembly_window","arguments":{"largest_legitimate_object":12000,"memory_budget":32768,"concurrency":3}}}' | pqc-migration-mcpサーバーは1行に1つのJSONオブジェクトで応答します。整形して表示すると、その応答のcontentペイロードは次のようになります:
{
"budget": 32768,
"ceiling": 10922,
"concurrency": 3,
"explanation": "EMPTY WINDOW: floor 12,000 B > ceiling 10,922 B (short by 1,078 B). No capacity cap is both feasible and safe. Raise the budget to at least 36,000 B, reduce concurrency to at most 2, or choose a smaller credential.",
"floor": 12000,
"is_empty": true,
"max_safe_concurrency": 2,
"recommended_cap": null
}エージェントは判定とそれを修正する数値を取得するため、問題を報告するのではなく具体的な変更を提案できます。
検出は公開しますが、修復は公開しません。
エージェントは、設計がkrack_retransmissionに失敗することと、未修復の設計が正確に何をしたかを知ることができます。しかし、それを閉じるメカニズムを取得することはできません。その境界は意図的です。修復を返すMCPツールがあれば、任意のユーザーが午後だけで閉じたセット全体を列挙できてしまいます。
全39ファミリーと他のすべてのツールに対してdescribe_familyを呼び出し、応答を連結し、出力のどこかにrepair_mechanism、repaired_detail、またはrepaired_heldが現れた場合に失敗するテストがあります。
ドメインエラー（未知のアルゴリズム、未知のファミリー）は、isError: true と有効なオプションを挙げたメッセージを含むツール結果として返されます。これによりエージェントは自分で修正できます。プロトコル障害のみがJSON-RPCエラーになります（-32601 未知のメソッド/ツール、-32602 不正な引数、-32700 解析不能な行）。
不正な行があってもループは停止しません。サーバーは解析エラーで応答し、サービスを継続します。
pip install -e ".[dev]" && pytest # 57 passedテストは、プロトコル、すべてのツール、モート境界、およびサブプロセスとして駆動される実際のstdioトランスポートをカバーしています。stderrが空のままであることを確認するチェックも含まれます。MCPクライアントはstdoutをプロトコルとして読み取るため、迷いの警告は混乱を招きます。
算術、分類の参照、スコアリング。暗号化、ネットワーク、テレメトリはありません。実装を検査することはありません。クリーンな回答は、あなたの設定が健全であることを意味し、コードがそれを強制していることを意味するわけではありません。
pqc-sizes · pqc-mfb · pqc-guard-action · pqc-dos-embedded
39ファミリーを閉じることがクローズドコアの役割です。関連する主題は、提出済みの仮特許出願によって保護されています。完全なエンベロープの商用利用については、GitHub Discussion またはこのリポジトリのissueを開いてください。
これが証明すること。 エージェントが推論に使用する算術と分類が正しいこと: 実際の資格情報サイズ、実際のフラグメント数、実際のウィンドウ判定、実際の障害分類。
証明しないこと。
エージェントが回答を使用したことではありません。 これは事実を提供するものであり、それらがどう使われるかを監督するものではありません。
コードの検査ではありません。 ここにあるツールは実装を読み取りません。
修復チャネルではありません。 すべてのツールは検出のみを公開します。テストは全39ファミリーと他のすべてのツールに対してdescribe_familyを呼び出し、出力のどこかに修復フィールドが現れた場合に失敗します。
エラー。 ドメインの問題は、isError: true と有効なオプションを挙げたメッセージを含むツール結果として返されます。これによりエージェントは自己修正できます。プロトコル障害のみがJSON-RPCエラーになります。
認証付き鍵交換をポスト量子に移行するチームのための11の無料ツール。それらは発見し測定します。修復はしません。
ツール | 説明 | 場所 |
サイズ、フラグメント数、両側再組み立てウィンドウ | ソース | |
Nodeとブラウザ向けの同じ算術 | ソース | |
ウィンドウが空のときにビルドを失敗させる | GitHub Action | |
C言語169行: 実際の64KBデバイスでの障害 | ソース | |
デバイス上で境界を再検証、SMTソルバー不要 | ソース | |
Lean 4での同じ境界 — | ソース | |
合成可能なRTLのゲート、Yosysによる5つの証明 | ソース | |
pqc-migration-mcp ← あなたはここ | AIエージェント向けの6つのMCPツール | ソース |
322ケース · 39障害ファミリー · スコアラー | ソース | |
データセットとしてのベンチマーク | HF | |
名前付き形式結果122件、プローバー6つ | HF | |
ブラウザで試す、インストール不要 | HF Space |
初めてですか？ エンドツーエンドのチュートリアル は、約10分で1つの現実的な移行をすべてのツールを通して説明します: サイズ -> ウィンドウ -> CIゲート -> ベンチマーク。
急いでいますか？ pqc-sizes は、資格情報がフラグメント化されるかどうか、安全な上限が存在するかどうかを5秒で教えてくれます。pqc-explorer はブラウザで同じことを、インストールなしで行います。
39の障害ファミリー（ダウングレードバインディング、再送信安全なインストール、フラグメンテーショントランスクリプト、ローミング前方秘匿性、マルチリンク鍵分離、アドミッション制御、グループ鍵バインディング）を閉じることは、別のプロプライエタリなコードベースです。関連する主題は、提出済みの仮特許出願によって保護されています。
その分割は主張ではなく測定されています。複製ノイズ制御の下では、32のうち4つの修復メカニズムのみが外部から区別可能であり、これらの検出器を公開しても修復は開示されません。
商用ライセンスについては、GitHub Discussion またはこれらのリポジトリのいずれかにissueを開いてください。
Apache-2.0。LICENSE と CONTRIBUTING.md を参照してください。
Total on-wire bytes for a KEM + signature credential, with a per-component breakdown.
| Name | Required | Description | Default |
|---|---|---|---|
| kem | No | KEM name, e.g. ML-KEM-768 | ML-KEM-768 |
| sig | No | Signature name, e.g. ML-DSA-65 | ML-DSA-65 |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions a 'per-component breakdown' but does not specify the output format, side effects, or constraints like required permissions. Minimal disclosure.
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?
A single sentence that efficiently conveys the core function. However, front-loading could be improved by adding an explicit verb. Still well-structured.
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?
Adequate for a simple tool with two optional parameters, but lacks details on the return value format (e.g., boolean? object?). Without an output schema, more context would help.
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% with descriptions for both parameters. The description repeats the concept but adds no new meaning beyond what the schema provides. Baseline 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 clearly states the tool computes on-wire bytes for a credential with a breakdown, which distinguishes it from sibling tools like list_failure_families. However, the verb is implied rather than explicit (e.g., 'calculate').
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 on when to use this tool, when not to, or alternatives. The sibling tools are unrelated, but the description does not help the agent decide context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Detail for one failure family: the invariants it breaks, the unrepaired designs that fail it, and what each did. Does not return repairs.
| Name | Required | Description | Default |
|---|---|---|---|
| family | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations exist. Description mentions what is returned and what is not (repairs), but lacks information on side effects, permissions, or whether it is a read-only operation. Basic disclosure but not comprehensive.
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?
Single sentence, efficient and front-loaded with purpose. No redundant words, but a structured list of what is included might improve clarity without expanding length significantly.
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?
Describes output content (invariants, designs) but not structure or format. No output schema. Lacks guidance on the parameter value. Adequate for narrow use but insufficient for full autonomy.
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 0% for the only parameter 'family'. Description does not explain what the parameter value should be (e.g., family ID or name) or provide format examples. Fails to add meaning beyond the schema's type and required status.
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?
Description clearly states the tool provides detailed information for one failure family, including invariants and unrepaired designs, and explicitly excludes repairs. This distinguishes it from sibling tool list_failure_families, which likely lists all families.
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?
Implies use when details on a specific family are needed, but does not explicitly state when to use versus siblings like list_failure_families or other tools. No alternatives or exclusions provided.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
How many fragments an object becomes on a transport, and whether fragmentation is therefore mandatory.
| Name | Required | Description | Default |
|---|---|---|---|
| object_bytes | Yes | ||
| frame_payload | Yes | usable payload bytes per frame |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must carry the full burden of behavioral disclosure. It indicates the tool calculates fragment count and mandatory status, but it does not disclose side effects, authorization needs, error conditions, or whether the operation is read-only. For a computation tool, the lack of safety information is a gap.
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, concise sentence that immediately conveys the tool's purpose with no extraneous words. It is well-structured and front-loaded.
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?
For a simple 2-parameter tool, the description tells what the tool computes, but it lacks information about the return format (the output schema is absent). The agent must infer whether the result is a number, boolean, or structured object. This is a moderate completeness gap.
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 only 50% (frame_payload has a description). The tool description adds context by relating the parameters to object transport, but it does not explain what object_bytes is or provide details beyond the schema. It fails to compensate for the missing schema description of object_bytes.
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 that the tool computes 'how many fragments an object becomes on a transport' and determines if fragmentation is mandatory. This is a specific verb+resource that distinguishes it from sibling tools like list_failure_families and reassembly_window.
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 given on when to use this tool versus alternatives. The description does not mention prerequisites, exclusions, or comparisons with sibling tools. The agent must guess the appropriate context.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
All 39 post-quantum migration failure families, with case counts and published prior-art analogues.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It mentions output content but doesn't disclose behavioral traits such as read-only nature, permissions needed, rate limits, or any side effects. For a tool with no annotations, this is insufficient.
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?
A single, clear sentence with no extraneous information. Every word adds value.
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 provides reasonable context about return values (case counts, analogues). However, it lacks details like ordering, filtering, or any prerequisites. With no annotations, additional behavioral context 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?
There are 0 parameters, so the schema provides no information. The description adds meaning by explaining what the tool returns, which is the full list. Baseline for 0 params is 4.
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 specifies the verb 'list' and resource 'failure families', explicitly states 'All 39', and includes details on return content (case counts and prior-art analogues). This distinguishes it from sibling tools like 'describe_family' which likely focuses on one family.
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 use case: get a comprehensive list of all failure families. It doesn't explicitly state when not to use or name alternatives, but the contrast with 'describe_family' is clear. No explicit exclusions or when-not guidance.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
The two-sided reassembly-capacity window. Returns is_empty=true when NO capacity cap is both feasible and safe, plus the maximum concurrency that would be safe.
| Name | Required | Description | Default |
|---|---|---|---|
| concurrency | Yes | ||
| memory_budget | Yes | ||
| largest_legitimate_object | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, and the description does not fully disclose behavior. It lacks information on side effects, authentication, safety, or what 'feasible and safe' means. The description is insufficient for an agent to understand the tool's full behavior.
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 brief with one sentence, but it could be more structured. It front-loads jargon and then specifies returns. No superfluous words, but clarity is sacrificed 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?
With no output schema, the description only partially describes the return value (is_empty and max concurrency). It does not cover error conditions, edge cases, or other potential return fields. The description is incomplete for effective use.
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 input schema has no descriptions, and the tool's description does not explain the meaning of each parameter ('largest_legitimate_object', 'memory_budget', 'concurrency'). Minimal context is provided, leaving the agent guessing.
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 gives a basic idea of the tool's purpose (computing a capacity window), but uses jargon ('two-sided reassembly-capacity window') and doesn't clearly state the action (e.g., 'compute' or 'get'). The return values are specified, providing some clarity.
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 on when to use this tool versus its siblings. The description does not mention context, prerequisites, or alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Score a PQC-MFB submission ({case_id: bool}). Returns coverage, regressions, and which families have zero coverage.
| Name | Required | Description | Default |
|---|---|---|---|
| submission | Yes |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
The description discloses return values (coverage, regressions, zero-coverage families) but does not mention side effects, required authentication, or whether the operation is read-only. Since no annotations are provided, the description bears full burden, and the lack of side-effect clarity is a gap.
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 concise sentence that front-loads the verb and resource. However, the notation '{case_id: bool}' is somewhat cryptic and could be integrated into the schema or clarified.
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 lack of output schema and detailed input schema, the description should provide more context on the input object structure and the exact format of the return values. It covers outputs but omits input details, making it incomplete for proper use.
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 input schema has 0% description coverage, and the description only hints at a 'case_id' field via '{case_id: bool}', which is not defined in the schema. The structure of the required 'submission' object is left entirely unexplained, so the description adds minimal value beyond the schema.
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 action (score) and the specific resource (PQC-MFB submission), and lists the outputs (coverage, regressions, zero-coverage families). This distinguishes it from sibling tools like list_failure_families or describe_family, which serve different purposes.
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 the tool is used when you need to evaluate a submission, but it does not provide explicit guidance on when to use it vs. siblings, nor does it mention prerequisites or avoidance scenarios.
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.
v0.1.0credential_sizedescribe_familyfragmentslist_failure_familiesreassembly_windowscore_submissionScored across 6 tools
Each tool targets a distinct aspect of PQC migration analysis: failure families, reassembly capacity, submission scoring, credential size, family details, and fragmentation. No overlaps in functionality.
Most tools follow a verb_noun pattern with underscores (list_failure_families, score_submission, describe_family). 'credential_size' and 'reassembly_window' are noun-like but still clear; 'fragments' is a single noun, slightly deviating.
The set includes 6 tools, which is well within the ideal 3-15 range. Each tool addresses a specific need without redundancy, making the scope manageable and focused.
The tools cover querying failure families and scoring submissions, but lack submission management, repair retrieval (noted in describe_family), and listing submissions. Some gaps exist for a full workflow.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
Production-grade cryptography toolkit with 31 MCP tools for classical, PQC, and KMS workflows.
QuantumOracle — 18 post-quantum crypto tools: Kyber, Dilithium, hybrid schemes, migration.
AI-security knowledge as MCP: standards-mapped tools (OWASP, NIST, MITRE) for AI agents.
Tamper-evident proof creation and verification for AI agents via MCP, A2A, and REST.