AI 에이전트가 계속 추측하고 있는 양자 내성 마이그레이션 사실을 제공하세요.
MCP를 통한 여섯 가지 도구: 자격 증명 크기, 조각 수, 재조립 창, 39개 실패 분류 체계, 벤치마크 점수. Claude에게 *"우리의 ML-KEM-768 핸드셰이크가 BLE MTU에 들어갈까?"*라고 물어보면 추정하는 대신 답을 계산합니다.
📖 전체 문서, 튜토리얼 및 개념 가이드: https://nickharris808.github.io/pqc-toolkit/
에이전트가 점점 더 PQC 마이그레이션 작업을 수행하고 있으며, 정확히 중요한 것들에 대해 자신 있게 틀리고 있습니다: 자격 증명이 실제로 얼마나 큰지, 몇 개의 조각이 되는지, 동시성에서 안전한 재조립 상한이 존재하는지. 그것들은 판단이 아니라 산술입니다 — 그러니 에이전트에게 산술을 넘겨주세요.
여기서 프로토콜 계층은 의존성이 없습니다. MCP는 줄 단위로 구분된 stdio를 통한 JSON-RPC 2.0이며, 직접 구현할 수 있을 만큼 작아 설치가 간단합니다.
Related MCP server: attestix
pip install git+https://github.com/nickharris808/pqc-migration-mcp이것은 pqc-sizes와 pqc-mfb를 해당 저장소에서도 가져옵니다. 아직 PyPI에 없으므로 pip install pqc-migration-mcp는 현재 작동하지 않습니다.
# 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.json에 추가:
{
"mcpServers": {
"pqc-migration": {
"command": "pqc-migration-mcp"
}
}
}Claude Desktop을 다시 시작하세요. 여섯 가지 도구가 커넥터 아래에 나타납니다.
도구 | 답변 |
| KEM+서명 자격 증명은 구성 요소별로 몇 바이트인가? |
| 이 전송에서 몇 개의 조각이 생기며, 조각화가 이제 필수인가? |
| 안전한 용량 상한이 전혀 존재하는가? 그렇지 않다면 어떤 동시성이 작동할까? |
| 모든 39개 실패 계열, 사례 수 및 게시된 유사 사례 |
| 이 계열에서 어떤 설계에서 무엇이 깨지며, 각각 무엇을 했는가? |
| PQC-MFB 제출물 점수: 적용 범위, 회귀, 제로 적용 범위 계열 |
전송은 줄 단위로 구분된 JSON입니다 — 각 줄에 하나의 완전한 객체. 요청을 한 줄에 유지하세요. 두 줄에 걸쳐 감싸진 요청은 두 개의 불완전한 요청으로 도착하여 두 개의 -32700 구문 분석 오류로 돌아옵니다.
$ 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-mcp서버는 각 줄에 하나의 JSON 객체로 응답합니다. 예쁘게 출력된 해당 응답의 content 페이로드는 다음과 같습니다:
{
"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
}에이전트는 판정 그리고 그것을 고칠 숫자를 얻으므로, 문제를 보고하는 대신 구체적인 변경을 제안할 수 있습니다.
탐지를 노출합니다. 수리를 노출하지 않습니다.
에이전트는 설계가 krack_retransmission에 실패하고 수리되지 않은 설계가 정확히 무엇을 했는지 배울 수 있습니다. 그것을 닫는 메커니즘은 얻을 수 없습니다. 그 경계는 의도적입니다: 수리를 반환하는 MCP 도구는 어떤 사용자가 오후에 전체 폐쇄 집합을 열거할 수 있게 합니다.
모든 39개 계열에 대해 describe_family를 호출하고 다른 모든 도구를 호출한 다음 응답을 연결하고 repair_mechanism, repaired_detail 또는 repaired_held가 출력 어디에도 나타나면 실패하는 테스트가 있습니다.
도메인 오류 — 알 수 없는 알고리즘, 알 수 없는 계열 — 는 유효한 옵션을 명명하는 메시지와 함께 isError: true인 도구 결과로 돌아오므로 에이전트가 스스로 수정할 수 있습니다. 프로토콜 결함만 JSON-RPC 오류가 됩니다 (-32601 알 수 없는 메서드/도구, -32602 잘못된 인수, -32700 구문 분석 불가능한 줄).
잘못된 줄은 루프를 죽이지 않습니다. 서버는 구문 분석 오류로 응답하고 계속 서비스합니다.
pip install -e ".[dev]" && pytest # 57 passed테스트는 프로토콜, 모든 도구, 해자 경계, 그리고 하위 프로세스로 구동되는 실제 stdio 전송을 다룹니다 — stderr가 비어 있는지 확인하는 검사를 포함합니다. MCP 클라이언트는 stdout을 프로토콜로 읽고 잘못된 경고가 혼란을 주기 때문입니다.
산술, 분류 조회 및 점수. 암호화, 네트워크, 원격 측정 없음. 구현을 검사하지 않습니다. 깨끗한 답변은 구성이 건전하다는 의미이지 코드가 그것을 강제한다는 의미가 아닙니다.
pqc-sizes · pqc-mfb ·
pqc-guard-action · pqc-dos-embedded
39개 계열을 닫는 것은 폐쇄 코어가 하는 일입니다. 관련 주제는 출원된 가특허 출원으로 보호됩니다. 전체 범위의 상업적 사용에 대해서는 GitHub Discussion 또는 이 저장소의 이슈를 여세요.
이것이 증명하는 것. 에이전트가 추론하는 산술과 분류가 정확하다는 것: 실제 자격 증명 크기, 실제 조각 수, 실제 창 판정, 실제 실패 분류.
증명하지 않는 것.
에이전트가 답을 사용했다는 것이 아님. 이것은 사실을 제공합니다. 그것으로 무엇을 하는지 감독하지 않습니다.
코드 검사가 아님. 여기 어떤 도구도 구현을 읽지 않습니다.
수리 채널이 아님. 모든 도구는 탐지만 노출합니다. 테스트는 모든 39개 계열에 대해 describe_family를 호출하고 다른 모든 도구를 호출하며 수리 필드가 출력 어디에도 나타나면 실패합니다.
오류. 도메인 문제는 유효한 옵션을 명명하는 메시지와 함께 isError: true인 도구 결과로 돌아오므로 에이전트가 스스로 수정할 수 있습니다. 프로토콜 결함만 JSON-RPC 오류가 됩니다.
인증된 키 교환을 양자 내성으로 전환하는 팀을 위한 11가지 무료 도구. 찾고 측정합니다. 수리하지 않습니다.
도구 | 하는 일 | 위치 |
크기, 조각 수, 양면 재조립 창 | 소스 | |
Node 및 브라우저용 동일한 산술 | 소스 | |
창이 비어 있을 때 빌드 실패 | GitHub Action | |
C 169줄: 실제 64 KB 장치에서의 실패 | 소스 | |
SMT 솔버 없이 장치에서 경계 재검증 | 소스 | |
Lean 4의 동일한 경계 — | 소스 | |
합성 가능한 RTL의 게이트, Yosys 증명 5개 | 소스 | |
pqc-migration-mcp ← 여기 있습니다 | AI 에이전트용 MCP 도구 6개 | 소스 |
사례 322개 · 실패 계열 39개 · 스코어러 | 소스 | |
데이터셋으로서의 벤치마크 | HF | |
명명된 형식 결과 122개, 프로버 6개 | HF | |
설치 없이 브라우저에서 사용해 보세요 | HF Space |
처음이신가요? 종단 간 튜토리얼은 약 10분 안에 하나의 현실적인 마이그레이션을 모두 안내합니다: 크기 -> 창 -> CI 게이트 -> 벤치마크.
서두르시나요? pqc-sizes는 5초 안에 자격 증명이 조각화되는지와 안전한 상한이 존재하는지 알려줍니다. pqc-explorer는 설치 없이 브라우저에서 동일한 작업을 수행합니다.
39개 실패 계열을 닫는 것 — 다운그레이드 바인딩, 재전송 안전 설치, 조각화 기록, 로밍 전방 비밀성, 다중 링크 키 분리, 허용 제어, 그룹 키 바인딩 — 은 별도의 독점 코드베이스입니다. 관련 주제는 출원된 가특허 출원으로 보호됩니다.
그 분할은 주장이 아니라 측정된 것입니다: 복제 노이즈 대조군에서 32개 수리 메커니즘 중 4개만 외부에서 구별 가능하므로, 이러한 탐지기를 게시해도 수리가 공개되지 않습니다.
상업적 라이선스에 대해서는 GitHub Discussion 또는 이 저장소 중 하나에 이슈를 여세요.
Apache-2.0. LICENSE 및 CONTRIBUTING.md를 참조하세요.
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.
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