Geben Sie Ihrem KI-Agenten die Post-Quanten-Migrationsfakten, die er ständig errät.
Sechs Tools über MCP: Credential-Größen, Fragmentanzahlen, das Reassembly-Fenster, die 39-Familien-Fehler-Taxonomie und Benchmark-Bewertung. Fragen Sie Claude „Wird unser ML-KEM-768-Handshake in eine BLE-MTU passen?“ und es berechnet die Antwort, statt eine zu schätzen.
📖 Vollständige Dokumentation, Tutorial und konzeptioneller Leitfaden: https://nickharris808.github.io/pqc-toolkit/
Agenten übernehmen zunehmend PQC-Migrationsarbeit und liegen bei genau den Dingen selbstbewusst falsch, die zählen: wie groß ein Credential tatsächlich ist, in wie viele Fragmente es zerfällt und ob bei Ihrer Parallelität eine sichere Reassembly-Grenze existiert. Das ist Arithmetik, kein Urteilsvermögen – also geben Sie dem Agenten die Arithmetik.
Die Protokollschicht hier ist abhängigkeitsfrei. MCP ist JSON-RPC 2.0 über zeilengetrenntes stdio, was klein genug ist, um es direkt zu implementieren, und die Installation trivial hält.
Related MCP server: attestix
pip install git+https://github.com/nickharris808/pqc-migration-mcpDies zieht auch pqc-sizes und pqc-mfb aus ihren Repositories. Noch nicht auf PyPI, daher funktioniert pip install pqc-migration-mcp heute nicht.
# talk to it directly -- it is line-delimited JSON-RPC on stdio
echo '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' | pqc-migration-mcpFügen Sie zu claude_desktop_config.json hinzu:
{
"mcpServers": {
"pqc-migration": {
"command": "pqc-migration-mcp"
}
}
}Starten Sie Claude Desktop neu. Die sechs Tools erscheinen unter dem Connector.
Tool | Antworten |
| Wie viele Bytes hat ein KEM+Signatur-Credential, Komponente für Komponente? |
| Wie viele Fragmente auf diesem Transport – und ist Fragmentierung jetzt Pflicht? |
| Gibt es überhaupt eine sichere Kapazitätsgrenze? Wenn nicht, welche Parallelität würde funktionieren? |
| Alle 39 Fehlerfamilien, mit Fallzahlen und veröffentlichten Analogien |
| Was bricht in dieser Familie, in welchen Designs, und was hat jede getan? |
| Bewerte eine PQC-MFB-Einreichung: Abdeckung, Regressionen, Familien ohne Abdeckung |
Der Transport ist zeilengetrenntes JSON – ein vollständiges Objekt pro Zeile. Halten Sie die Anfrage in einer einzigen Zeile; eine über zwei Zeilen umbrochene Anfrage kommt als zwei unvollständige an und wird als zwei -32700-Parse-Fehler zurückgegeben.
$ 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-mcpDer Server antwortet mit einem JSON-Objekt pro Zeile. Hübsch formatiert ist die content-Nutzlast dieser Antwort:
{
"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
}Der Agent erhält ein Urteil und die Zahl, die es beheben würde, sodass er eine konkrete Änderung vorschlagen kann, statt ein Problem zu melden.
Er legt Erkennung offen. Er legt keine Reparaturen offen.
Ein Agent kann erfahren, dass ein Design krack_retransmission nicht besteht und genau, was das nicht reparierte Design getan hat. Er kann nicht den Mechanismus erhalten, der es schließt. Diese Grenze ist bewusst: Ein MCP-Tool, das Reparaturen zurückgibt, würde jedem Benutzer ermöglichen, die gesamte geschlossene Menge an einem Nachmittag aufzulisten.
Es gibt einen Test, der describe_family für alle 39 Familien plus jedes andere Tool aufruft, die Antworten verkettet und fehlschlägt, wenn repair_mechanism, repaired_detail oder repaired_held irgendwo in der Ausgabe erscheint.
Domänenfehler – ein unbekannter Algorithmus, eine unbekannte Familie – kommen als Tool-Ergebnis mit isError: true und einer Nachricht zurück, die die gültigen Optionen nennt, damit der Agent sich selbst korrigieren kann. Nur Protokollfehler werden zu JSON-RPC-Fehlern (-32601 unbekannte Methode/Tool, -32602 falsche Argumente, -32700 unparsebare Zeile).
Eine fehlerhafte Zeile beendet die Schleife nicht; der Server antwortet mit einem Parse-Fehler und bedient weiter.
pip install -e ".[dev]" && pytest # 57 passedTests decken das Protokoll, jedes Tool, die Burggraben-Grenze und den echten stdio-Transport als Unterprozess ab – einschließlich einer Prüfung, dass stderr leer bleibt, da MCP-Clients stdout als Protokoll lesen und verirrte Warnungen sie verwirren.
Arithmetik, Taxonomie-Nachschlage und Bewertung. Keine Kryptografie, kein Netzwerk, keine Telemetrie. Es inspiziert Ihre Implementierung nicht. Eine saubere Antwort bedeutet, dass Ihre Konfiguration solide ist, nicht dass Ihr Code sie durchsetzt.
pqc-sizes · pqc-mfb ·
pqc-guard-action · pqc-dos-embedded
Das Schließen der 39 Familien ist das, was der geschlossene Kern tut. Relevanter Gegenstand ist durch eine eingereichte vorläufige Patentanmeldung abgedeckt. Für die kommerzielle Nutzung der vollständigen Hülle eröffnen Sie eine GitHub-Diskussion oder ein Issue in diesem Repository.
Was dies beweist. Dass die Arithmetik und Taxonomie, mit der ein Agent argumentiert, korrekt sind: echte Credential-Größen, echte Fragmentanzahlen, ein echtes Fenster-Urteil und die echte Fehler-Taxonomie.
Was es NICHT beweist.
Nicht, dass der Agent die Antwort verwendet hat. Dies liefert Fakten; es überwacht nicht, was damit geschieht.
Keine Inspektion Ihres Codes. Kein Tool hier liest Ihre Implementierung.
Kein Reparaturkanal. Jedes Tool legt nur Erkennung offen. Ein Test ruft describe_family für alle 39 Familien plus jedes andere Tool auf und schlägt fehl, wenn ein Reparaturfeld irgendwo in der Ausgabe erscheint.
Fehler. Domänenprobleme kommen als Tool-Ergebnisse mit isError: true und einer Nachricht zurück, die gültige Optionen nennt, sodass ein Agent sich selbst korrigieren kann. Nur Protokollfehler werden zu JSON-RPC-Fehlern.
Elf kostenlose Tools für Teams, die authentifizierten Schlüsselaustausch auf Post-Quanten umstellen. Sie finden und messen; sie reparieren nicht.
Tool | Was es tut | Wo |
Größen, Fragmentanzahlen und das zweiseitige Reassembly-Fenster | Quellcode | |
Dieselbe Arithmetik für Node und den Browser | Quellcode | |
Build fehlschlagen lassen, wenn das Fenster leer ist | GitHub Action | |
169 Zeilen C: der Fehler auf einem echten 64-KB-Gerät | Quellcode | |
Die Grenze auf dem Gerät erneut verifizieren, ohne SMT-Löser | Quellcode | |
Dieselbe Grenze in Lean 4 — 0 | Quellcode | |
Das Gate in synthetisierbarem RTL, 5 Yosys-Beweise | Quellcode | |
pqc-migration-mcp ← Sie sind hier | Sechs MCP-Tools für KI-Agenten | Quellcode |
322 Fälle · 39 Fehlerfamilien · Bewerter | Quellcode | |
Der Benchmark als Datensatz | HF | |
122 benannte formale Ergebnisse, 6 Beweiser | HF | |
Probieren Sie es in Ihrem Browser aus, keine Installation | HF Space |
Neu hier? Das End-to-End-Tutorial führt eine realistische Migration in etwa zehn Minuten durch alle: Größen -> Fenster -> CI-Gate -> Benchmark.
In Eile? pqc-sizes sagt Ihnen in fünf Sekunden, ob Ihr Credential fragmentiert und ob eine sichere Grenze existiert. pqc-explorer macht dasselbe im Browser, ohne Installation.
Das Schließen der 39 Fehlerfamilien – Downgrade-Bindung, übertragungssichere Installation, Fragmentierungs-Transkripte, Roaming-Vorwärtsgeheimnis, Multi-Link-Schlüsseltrennung, Zulassungskontrolle, Gruppen-Schlüssel-Bindung – ist eine separate proprietäre Codebasis. Relevanter Gegenstand ist durch eine eingereichte vorläufige Patentanmeldung abgedeckt.
Diese Trennung ist gemessen, nicht behauptet: Unter einer Replikat-Rauschkontrolle sind nur 4 von 32 Reparaturmechanismen extern unterscheidbar, sodass die Veröffentlichung dieser Detektoren die Reparaturen nicht offenlegt.
Für kommerzielle Lizenzierung eröffnen Sie eine GitHub-Diskussion oder ein Issue in einem dieser Repos.
Apache-2.0. Siehe LICENSE und CONTRIBUTING.md.
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.
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