Atom-of-thoughts
Atom der Gedanken (AoT)
Eine Model Context Protocol (MCP)-Serverimplementierung von Atom of Thoughts, einem auf Dekomposition basierenden Schlussfolgerungsrahmen.
Hinweis : Diese Implementierung basiert auf dem Forschungspapier „Atom of Thoughts for Markov LLM Test-Time Scaling“ (Teng et al., 2025).
Englische Dokumentation
Dieses Repository implementiert Atom of Thoughts (AoT), ein dekompositionsbasiertes Reasoning-Framework, als Model Context Protocol (MCP)-Server. Diese Implementierung basiert auf den Konzepten des Papers „Atom of Thoughts for Markov LLM Test-Time Scaling“ (Teng et al., 2025).
Verfügbare Tools
Es stehen zwei Haupttools zur Verfügung:
AoT (Vollversion) : Ein vollständiges Atom of Thoughts-Tool mit allen Funktionen für gründliche Analysen und komplexe Problemlösungen.
AoT-light (Lightweight-Version) : Eine optimierte Version, die für eine schnellere Verarbeitung und schnellere Ergebnisse optimiert ist.
AoT-light: Leichtgewichtige Version
AoT-light ist für eine schnellere Verarbeitung in zeitkritischen Situationen konzipiert:
Hauptmerkmale :
Geringere maximale Tiefe (3 statt 5) für eine schnellere Verarbeitung
Vereinfachter Verifizierungsprozess
Sofortiger Schlussfolgerungsvorschlag für Hypothesen mit hoher Konfidenz
Reduzierter Rechenaufwand und Antwortnutzlast
Optimiert für Geschwindigkeit statt für umfassende Analyse
Anwendungsfälle :
Schnelle Brainstorming-Sitzungen, die eine atomare Gedankenorganisation erfordern
Zeitkritische Problemlösung, bei der Geschwindigkeit Vorrang vor umfassender Analyse hat
Einfachere Denkaufgaben, die keine tiefe Zerlegung erfordern
Erste Erkundung vor der Verwendung des vollständigen AoT für eine tiefergehende Analyse
Für Lern- oder Demonstrationszwecke, bei denen die Reaktionszeit wichtig ist
Anwendungsfälle
Atom of Thoughts ist in den folgenden Szenarien wirksam:
Lösen von Problemen, die komplexes Denken erfordern
Generieren von Hypothesen, die aus mehreren Perspektiven überprüft werden müssen
Ableitung hochzuverlässiger Schlussfolgerungen in Szenarien, in denen Genauigkeit entscheidend ist
Minimieren logischer Fehler bei kritischen Aufgaben
Entscheidungsfindung, die mehrere Überprüfungsschritte erfordert
Atomtypen
AoT verwendet fünf Arten von Atomen:
Prämisse : Grundlegende Annahmen oder gegebene Informationen zur Problemlösung
Denken : Logischer Denkprozess basierend auf anderen Atomen
Hypothese : Vorgeschlagene Lösungen oder Zwischenschlussfolgerungen
Verifizierung : Verfahren zur Bewertung der Gültigkeit anderer Atome (insbesondere Hypothesen)
Schlussfolgerung : Verifizierte Hypothesen oder endgültige Problemlösungen
Kernfunktionen
1. Zersetzungs-Kontraktions-Mechanismus
Ein Mechanismus, um Atome in kleinere Subatome zu zerlegen und sie nach der Überprüfung wieder zusammenzuziehen.
Zerlegung : Aufspaltung komplexer Atome in kleinere Unteratome.
startDecomposition(atomId): Atomzerlegung startenaddToDecomposition(decompositionId, atomId): Unteratom zur Zerlegung hinzufügencompleteDecomposition(decompositionId): Zerlegungsprozess abschließen
Kontraktion : Kontraktion zurück zum ursprünglichen Atom, sobald alle Unteratome überprüft wurden.
Berechnen Sie die Konfidenz des ursprünglichen Atoms basierend auf den Konfidenzniveaus der Subatome
Automatische Schlussfolgerungen für verifizierte Hypothesen mit hoher Zuverlässigkeit vorschlagen
2. Automatischer Kündigungsmechanismus
Wird automatisch beendet, wenn die maximale Tiefe erreicht ist oder eine Schlussfolgerung mit hoher Zuverlässigkeit gefunden wird.
getTerminationStatus(): Gibt den aktuellen Kündigungsstatus und Grund zurückgetBestConclusion(): Gibt die Schlussfolgerung mit der höchsten Konfidenz zurück
Parameterbeschreibungen
atomId : Eindeutige Kennung für das Atom (z. B. „A1“, „H2“)
Inhalt : Tatsächlicher Inhalt des Atoms
atomType : Atomtyp (einer von: Prämisse, Argumentation, Hypothese, Verifizierung, Schlussfolgerung)
Abhängigkeiten : Liste der IDs anderer Atome, von denen dieses Atom abhängt
Vertrauen : Vertrauensniveau dieses Atoms (Wert zwischen 0-1)
isVerified : Ob dieses Atom verifiziert wurde
Tiefe : Tiefenebene dieses Atoms im Zersetzungs-Kontraktionsprozess
Verwendungsmethode
Verstehen Sie das Problem und definieren Sie die notwendigen Prämissenatome
Erstellen Sie Argumentationsatome basierend auf Prämissen
Erstellen Sie Hypothesenatome basierend auf Argumentation
Erstellen Sie Verifizierungsatome, um Hypothesen zu verifizieren
Leiten Sie Schlussfolgerungen zu Atomen auf der Grundlage überprüfter Hypothesen ab
Nutzen Sie die Atomzerlegung, um bei Bedarf tiefer zu forschen
Präsentieren Sie das Schlussfolgerungsatom mit hoher Konfidenz als endgültige Antwort
Vergleich von sequentiellem Denken und Gedankenatom (weitere Tests erforderlich)
Nach der Anwendung beider Denkwerkzeuge auf dasselbe Thema wurden die folgenden Unterschiede und Leistungsmerkmale beobachtet:
Strukturelle Unterschiede
Sequentielles Denken:
Linearer Denkprozess: schreitet sequenziell von einem Gedanken zum nächsten fort
Sagt die Gesamtzahl der Gedanken im Voraus voraus
Jede Denkphase baut auf den vorherigen Phasen auf
Atom der Gedanken:
Nichtlineare Netzwerkstruktur: Mehrere Denkeinheiten (Atome) sind durch Abhängigkeiten miteinander verbunden
Bildet eine systematische Struktur entsprechend den Atomtypen (Prämisse, Argumentation, Hypothese, Verifikation, Schlussfolgerung)
Bewertet explizit das Vertrauensniveau jedes Atoms
Vergleichende Stärken
Stärken im sequentiellen Denken:
Intuitiver Fluss: ähnlich den natürlichen menschlichen Denkprozessen
Einfachheit: Die einfache Struktur ermöglicht eine schnelle Anwendung auf einfache Probleme
Flexibilität: Kann vorherige Phasen modifizieren oder während des Denkprozesses die Richtung ändern
Stärken von Atom of Thoughts:
Vertrauensbewertung: Misst explizit das Vertrauen jedes Gedankens, um die Gültigkeit der Schlussfolgerung zu verbessern
Verifizierungsprozess: evaluiert Hypothesen durch systematische Verifizierungsphasen
Abhängigkeitsverfolgung: Verfolgt klar, welche Prämissen oder Argumente bestimmte Schlussfolgerungen beeinflusst haben
Parallele Verarbeitung: kann mehrere Gedankenatome gleichzeitig berücksichtigen
Effizienz und Genauigkeit
Effizienz:
Sequentielles Denken: effizienter bei einfachen Problemen, mit schnellerem Gedankenfortschritt
Atom of Thoughts: effizienter für komplexe Probleme, hat aber anfänglichen Overhead beim Aufbau systematischer Strukturen
Genauigkeit:
Sequentielles Denken: Möglichkeit der Anhäufung von Fehlern aus früheren Phasen, wenn der Denkprozess tiefer wird
Atom of Thoughts: Reduzierte Fehlerwahrscheinlichkeit durch Verifizierungsphasen und Vertrauensbewertung, was zu zuverlässigeren Schlussfolgerungen führt
Eignung nach Zweck
Für sequentielles Denken geeignete Fälle:
Einfache bis mittelkomplexe Probleme
Zeitlich begrenzte Situationen
Wenn natürliches Geschichtenerzählen oder Erklärungen erforderlich sind
Passende Fälle für Atom of Thoughts:
Hochkomplexe Probleme
Situationen, in denen Genauigkeit und Zuverlässigkeit entscheidend sind
Hypothesen, die aus mehreren Perspektiven überprüft werden müssen
Argumentation mit komplexen Abhängigkeitsbeziehungen
Abschluss
Beide Tools können dazu beitragen, die Denkfähigkeiten künstlicher Intelligenz zu verbessern. Das geeignete Tool variiert jedoch je nach Problemstellung und Anforderungen. Sequential Thinking ist nützlich, wenn intuitive und schnelle Denkprozesse erforderlich sind, während Atom of Thoughts sich besser für komplexe Probleme eignet, die systematische Verifizierung und hohe Zuverlässigkeit erfordern.
Befehlstool (Atombefehle)
Ein Befehlstool zur Steuerung des Zerlegungs-Kontraktionsmechanismus und der automatischen Beendigung von Atom of Thoughts.
Verfügbare Befehle :
decompose : Zerlegen Sie ein angegebenes Atom in kleinere Subatome
Erforderlicher Parameter:
atomId
complete_decomposition : Schließt einen laufenden Zerlegungsprozess ab
Erforderlicher Parameter:
decompositionId
termination_status : Überprüfen Sie den Beendigungsstatus des aktuellen AoT-Prozesses
best_conclusion : Erhalten Sie die verifizierte Schlussfolgerung mit der höchsten Zuverlässigkeit
set_max_depth : Ändert die maximale Tiefenbegrenzung
Erforderlicher Parameter:
maxDepth
Installation über Smithery
So installieren Sie Atom of Thoughts für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install @kbsooo/mcp_atom_of_thoughts --client claudeMCP-Serverkonfiguration
Um den Atom of Thoughts MCP-Server zu verwenden, müssen Sie ihn in Ihren Claude Desktop- oder Cline MCP-Einstellungen registrieren. Hier ist eine Beispielkonfiguration:
{
"mcpServers": {
"atom-of-thoughts": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/atom-of-thoughts/build/index.js"],
"disabled": false,
"autoApprove": []
}
}
}Ersetzen Sie /ABSOLUTE/PATH/TO/PARENT/FOLDER durch den tatsächlichen absoluten Pfad zum Projekt auf Ihrem System. Starten Sie nach dem Speichern der Konfiguration Claude Desktop oder Cline neu, um den Atom of Thoughts MCP-Server zu verwenden.
Eine ausführliche Implementierung und Dokumentation auf Codeebene finden Sie im Quellcode in diesem Repository.
Related MCP server: Sequential-Thinking
한국어 설명
Atom der Gedanken?
Atom of Thoughts (AoT) ist eine neue Version von Atom of Thoughts (AoT). 문제를 해결하는 도구입니다. 이 도구는 전통적인 순차적 사고 방식과 달리, 사고의 기본 단위인 '원자'들이 서로 의존성을 갖고 구성되어 더 강력한 문제 해결을 가능하게 합니다. Aus dem Buch „Atom of Thoughts for Markov LLM Test-Time Scaling“ (Teng et al., 2025) wurde die aktuelle Version erstellt.
제공되는 도구
Die folgenden Schritte sind nicht möglich:
AoT (전체 버전) : 심층적인 분석과 복잡한 문제 해결을 위한 완전한 기능을 갖춘 Atom of Thoughts 도구입니다.
AoT-light (벽량 버전) : 더 빠른 처리와 신속한 결과를 위해 최적화된 경량 버전입니다.
AoT-Licht: 벽량 버전
AoT-light:
Weitere Informationen :
Mehr als 3 Monate (5 Monate, 3) Mehr als 3 Monate
밄소화된 검증 프로세스
높은 신뢰도의 가설에 대한 즉각적인 결론 제안
축소된 계산 오버헤드 및 응답 데이터
철저한 분석보다 속도에 최적화
Beschreibung des Inhalts :
원자적 사고 구성이 필요한 빠른 브레인스토밍 세션
철저한 분석보다 속도가 중요한 Ԁ밄에 민감한 문제 해결
은은 분해가 필요하지 않은 단순한 추론 작업
Die AoT-Mitgliedschaft hat eine lange Zeit mit der Zeit begonnen
응답 요한 학습 또는 연연 목적
사용 나리오
다음과 같은 경우에 Atom of Thoughts를 사용하면 효과적입니다:
복잡한 추론이 필요한 문제 해결
여러 관점에서 검증이 필요한 가설 생성
정확도가 중요한 문제에서 신뢰도 높은 결론 도출
논리적 오류를 최소화해야 하는 작업
여러 단계의 검증이 필요한 의사결정
원자 유형
Atom of Thoughts
Prämisse (전제) : 문제 해결을 위한 기본 가정이나 주어진 정보
Begründung (추론) : 다른 원자들을 기반으로 한 논리적 추론 과정
Hypothese (Sprüche) :
Verifizierung (Verifizierung): 다른 원자(특히 가설)의 유효성 을 평가하는 과정
Fazit (결론) : 검증된 가설이나 최종 문제 해결책
핵심 기능
1. 분해-수축 메커니즘 (Zerlegung-Kontraktion)
원자를 더 작은 하위 원자로 분해하고 원자를 더 작은 하위 원자로 분해하고 검증 후 다수축하는 메커니즘입니다.
원자 분해 (Zerlegung) : 복잡한 원자를 더 작은 하위 원자로 분해합니다.
startDecomposition(atomId): Die neue Version wird angezeigtaddToDecomposition(decompositionId, atomId): Dies ist eine neue FunktioncompleteDecomposition(decompositionId): Der Wert wird geändert
원자 수축 (Kontraktion) : 하위 원자들이 모두 검증되면 원래 원자로 다수축합니다.
하위 원자들의 신뢰도에 기반하여 원래 원자의 신뢰도를 계산
지면 자동으로 결론을 제안
2. 자동 종료 메커니즘 (Automatische Beendigung)
최대 신뢰도의 결론을 찾으면 자동 종료됩니다.
getTerminationStatus(): Diese Funktion wird mit der Funktion „GetTerminationStatus()“ verknüpftgetBestConclusion(): Die neue Funktion wird verwendet
매개변수 설명
atomId : 원자의 고유 식별자 (Änderungen: 'A1', 'H2' 등)
Inhalt : 원자의 실제 내용
atomType : Prämisse (Prämisse, Argumentation, Hypothese, Verifizierung, Schlussfolgerung)
Abhängigkeiten: 이 원자가 의존하는 다른 원자들의 원자들의 ID 목록
Selbstvertrauen: 이 원자의 신뢰도 (0~1 사이의 값)
isVerified : 이 원자가 검증되었는지 여부
Tiefe: 이 원자의 깊이 (분해-수축 프로세스에서의 수준)
사용 방법
문제를 이해하고 필요한 전제(premise) 원자들을 정의
전제를 바탕으로 추론(Argumentation) 원자 생성
추론을 바탕으로 가설(hypothese) 원자 생성
원자 생성
원자 도출
사용하여 더 깊이 탐색
높은 신뢰도의 결론 원자를 최종 답변으로 제제로지
Sequentielles Denken (Atom der Gedanken)
두 지지 사고 도구를 동일한 주제에 적용한 후 관찰된 차이점과 성능 특성은 다음과 Beschreibung:
차이점
Sequentielles Denken:
선형적 사적으로 진행
전체 사고 수를 미리 예측
사고 단계는 이전 단계를 기반으로 구축됨
Atom der Gedanken:
비선형, 네트워크 여조: 여러 사고 단위(원자)가 의존성을 가지고 연결됨
원자 유형(전제, 추론, 가설, 검증, 결론)에 따라 체계적인 구조 형성
원자의 신뢰도 수준을 명적으로 평가
비교 강점
Folgerichtiges Denken:
직관적 흐름: 자연스러운 인간의 사고 과정과 유사
단순성: 단순성 빠르게 적용 가능
Gewählt: 사정하거나 방향을 변경할 수 있음
Atom of Thoughts Beschreibung:
Gewünschte Einstellungen: 사뢰도의 신뢰도를 명적으로 측정하여 결론의 유효성 개선
Gewünschte Bedeutung: 체계적인 검증 단계를 통해 가설 평가
의존성 추적: 어떤 전제7
병렬 처리: 여러 사고 원자를 동Ի에 고려 가능
효율성과 정확성
Inhalt:
Sequentielles Denken: 단순한 문제에 더 효율적이며, 사고가 빠르게 진행됨
Atom of Thoughts: 복잡한 문제에 더 효율적이지만, 체계적인 구조를 만드는 초기 오버헤드가 있음
Beschreibung:
Sequentielles Denken: 사고 과정이 깊어질수록 이전 단계에서의 오류 누적 가능성
Atom of Thoughts: 더 신뢰할 수 있는 결론 도출
목적별 적합성
Sequential Thinking – Kurzbeschreibung:
단순하거나 중간 정도 복잡한 문제
제약이 있는 상황
자연스러운 스토리텔링이나 설명이 필요한 경우
Atom of Thoughts-Inhalt:
매우 복잡한 문제
정확성과 신뢰성이 중요한 상황
여러 관점에서 검증이 필요한 가설
복잡한 의존 관계가 있는 추론
Nein
두 도구 모두 인공 지능의 추론 능력을 향상third 키는 데 기여할 수 있지만, 적절한 도구는 문제의 특성과 요구 사항에 따라 달라집니다. Sequential Thinking ist eine von vielen Büchern, die von Atom of Thoughts und Atom of Thoughts entwickelt wurden 신뢰성이 필요한 복잡한 문제에 더 적합합니다.
명령어 도구 (Atombefehle)
Atom of Thoughts.
Weitere Informationen zum Inhalt :
zerlegen :
Beispielname:
atomId
Complete_decomposition : Die vollständige Zerlegung wird durch eine vollständige Dekomposition ersetzt.
Möglicher Wert:
decompositionId
termine_status : Der AoT-Status wurde für die Dauer des Tests aktiviert.
best_conclusion : 높은 신뢰도의 검증된 결론을 가져옵니다.
set_max_ Depth : Die maximale Anzahl an Objekten wird angezeigt.
Maximale Tiefe:
maxDepth
Installation über Smithery
So installieren Sie Atom of Thoughts für Claude Desktop automatisch über Smithery :
npx -y @smithery/cli install @kbsooo/mcp_atom_of_thoughts --client claudeDie MCP-Mitgliedschaft ist in vollem Gange
Atom of Thoughts MCP-Version von Claude Desktop und Cline-Version von MCP. 다음은 서버 구성의 예입니다:
{
"mcpServers": {
"atom-of-thoughts": {
"command": "node",
"args": ["/ABSOLUTE/PATH/TO/PARENT/FOLDER/atom-of-thoughts/build/index.js"],
"disabled": false,
"autoApprove": []
}
}
}Wählen Sie /ABSOLUTE/PATH/TO/PARENT/FOLDER aus, um den Ordner „/ABSOLUTE/PATH/TO/PARENT/FOLDER“ zu öffnen. Gegründet von Claude Desktop und Cline und der Atom of Thoughts MCP-Mitgliedergruppe.
Available Tools
3 toolsAoTA
Atom of Thoughts (AoT) is a tool for solving complex problems by decomposing them into independent, reusable atomic units of thought. Unlike traditional sequential thinking, this tool enables more powerful problem solving by allowing atomic units of thought to form dependencies with each other.
When to use:
Solving problems requiring complex reasoning
Generating hypotheses that need verification from multiple perspectives
Deriving high-confidence conclusions in scenarios where accuracy is crucial
Minimizing logical errors in critical tasks
Decision-making requiring multiple verification steps
Atom types:
premise: Basic assumptions or given information for problem solving
reasoning: Logical reasoning process based on other atoms
hypothesis: Proposed solutions or intermediate conclusions
verification: Process to evaluate the validity of other atoms (especially hypotheses)
conclusion: Verified hypotheses or final problem solutions
Parameter descriptions:
atomId: Unique identifier for the atom (e.g., 'A1', 'H2')
content: Actual content of the atom
atomType: Type of atom (one of: premise, reasoning, hypothesis, verification, conclusion)
dependencies: List of IDs of other atoms this atom depends on
confidence: Confidence level of this atom (value between 0-1)
isVerified: Whether this atom has been verified
depth: Depth level of this atom (in the decomposition-contraction process)
Additional features:
Decomposition-Contraction mechanism:
Decompose atoms into smaller sub-atoms and contract back after verification
startDecomposition(atomId): Start atom decomposition
addToDecomposition(decompositionId, atomId): Add sub-atom to decomposition
completeDecomposition(decompositionId): Complete decomposition process
Automatic termination mechanism:
Automatically terminate when reaching maximum depth or finding high-confidence conclusion
getTerminationStatus(): Return termination status and reason
getBestConclusion(): Return highest confidence conclusion
Usage method:
Understand the problem and define necessary premise atoms
Create reasoning atoms based on premises
Create hypothesis atoms based on reasoning
Create verification atoms to verify hypotheses
Derive conclusion atoms based on verified hypotheses
Use atom decomposition to explore deeper when necessary
Present the high-confidence conclusion atom as the final answer
| Name | Required | Description | Default |
|---|---|---|---|
| atomId | Yes | Unique identifier for the atom | |
| content | Yes | Actual content of the atom | |
| atomType | Yes | Type of atom | |
| dependencies | Yes | List of IDs of other atoms this atom depends on | |
| confidence | Yes | Confidence level of this atom (value between 0-1) | |
| isVerified | No | Whether this atom has been verified | |
| depth | No | Depth level of this atom in the decomposition-contraction mechanism |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: the decomposition-contraction mechanism with specific sub-commands (e.g., 'startDecomposition'), automatic termination based on depth or confidence, and a structured usage method. However, it lacks details on error handling, performance limits, or authentication needs.
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 well-structured with clear sections (e.g., 'When to use,' 'Atom types,' 'Parameter descriptions'), but it is overly verbose at 400+ words. Some details, like the step-by-step 'Usage method,' could be condensed, and the 'Additional features' section includes implementation-level commands that may not all be necessary for an agent to understand the tool's core purpose.
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 high complexity (7 parameters, no output schema, no annotations), the description does a good job of explaining the conceptual model, atom types, and mechanisms. However, it lacks information on output format, error cases, or how results are presented, which would be helpful for an agent to use it effectively without an output schema.
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 description coverage is 100%, so the schema already documents all parameters thoroughly. The description's 'Parameter descriptions' section mostly repeats what the schema provides, adding minimal extra context (e.g., examples like 'A1' for atomId). It does not explain interactions between parameters or provide usage examples beyond what the schema offers.
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's purpose as 'solving complex problems by decomposing them into independent, reusable atomic units of thought' and distinguishes it from 'traditional sequential thinking.' It also implicitly differentiates from sibling tools like 'AoT-light' by describing a comprehensive reasoning framework with multiple atom types and mechanisms.
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 includes an explicit 'When to use' section with five specific scenarios (e.g., 'Solving problems requiring complex reasoning,' 'Decision-making requiring multiple verification steps'), providing clear guidance on when this tool is appropriate versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
AoT-lightA
A lightweight version of Atom of Thoughts (AoT) designed for faster processing and quicker results. This streamlined version sacrifices some depth of analysis for speed, making it ideal for time-sensitive reasoning tasks.
When to use:
Quick brainstorming sessions requiring atomic thought organization
Time-sensitive problem solving where speed is prioritized over exhaustive analysis
Simpler reasoning tasks that don't require deep decomposition
Initial exploration before using the full AoT for deeper analysis
Learning or demonstration purposes where response time is important
Key differences from full AoT:
Lower maximum depth (3 instead of 5) for faster processing
Simplified verification process
Immediate conclusion suggestion for high-confidence hypotheses
Reduced computational overhead and response payload
Optimized for speed rather than exhaustive analysis
Atom types and parameters are the same as the full AoT tool.
| Name | Required | Description | Default |
|---|---|---|---|
| atomId | Yes | Unique identifier for the atom | |
| content | Yes | Actual content of the atom | |
| atomType | Yes | Type of atom | |
| dependencies | Yes | List of IDs of other atoms this atom depends on | |
| confidence | Yes | Confidence level of this atom (value between 0-1) | |
| isVerified | No | Whether this atom has been verified | |
| depth | No | Depth level of this atom (optional, defaults to 0) |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden and does well by disclosing key behavioral traits: it's optimized for speed with 'lower maximum depth (3 instead of 5),' 'simplified verification process,' 'immediate conclusion suggestion,' and 'reduced computational overhead.' However, it doesn't mention potential limitations like accuracy trade-offs or error handling.
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 well-structured with clear sections (overview, when to use, key differences) and every sentence adds value. However, it could be more front-loaded by integrating the 'When to use' points into the opening paragraph for quicker scanning, and some phrasing is slightly verbose (e.g., 'Learning or demonstration purposes where response time is important').
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 tool with 7 parameters, 100% schema coverage, no annotations, and no output schema, the description provides strong contextual completeness by explaining the tool's purpose, use cases, and behavioral differences from siblings. The main gap is lack of output information, but given the schema handles inputs well and the description covers operational context, it's mostly 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 description coverage is 100%, so the schema already documents all 7 parameters thoroughly. The description adds no parameter-specific information beyond stating 'Atom types and parameters are the same as the full AoT tool,' which merely references the schema without adding semantic value. Baseline 3 is appropriate when schema does the heavy lifting.
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 this is a 'lightweight version of Atom of Thoughts (AoT) designed for faster processing and quicker results' with 'streamlined version sacrifices some depth of analysis for speed.' It explicitly distinguishes from its sibling 'AoT' by being a faster alternative, and from 'atomcommands' by focusing on thought organization rather than commands.
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 provides explicit 'When to use' guidance with five specific scenarios (e.g., 'Quick brainstorming sessions,' 'Time-sensitive problem solving'), and includes a 'Key differences from full AoT' section that explains when to choose this over the sibling tool. It clearly delineates appropriate use cases versus alternatives.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
atomcommandsB
A command tool to control the decomposition-contraction mechanism and automatic termination of Atom of Thoughts.
Use this tool to access advanced features of AoT:
Decomposition (decompose): Decompose a specified atom into smaller sub-atoms
Complete decomposition (complete_decomposition): Complete an ongoing decomposition process
Check termination status (termination_status): Check the termination status of the current AoT process
Get best conclusion (best_conclusion): Get the verified conclusion with the highest confidence
Change settings (set_max_depth): Change the maximum depth limit
Command descriptions:
command: Command to execute (decompose, complete_decomposition, termination_status, best_conclusion, set_max_depth)
atomId: Atom ID to use with the command (only required for decompose command)
decompositionId: ID of the decomposition process (only required for complete_decomposition command)
maxDepth: Maximum depth value to set (only required for set_max_depth command)
| Name | Required | Description | Default |
|---|---|---|---|
| command | Yes | Command to execute | |
| atomId | No | Atom ID to use with the command | |
| decompositionId | No | ID of the decomposition process to complete | |
| maxDepth | No | Maximum depth value to set |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries full burden but lacks behavioral details. It mentions 'automatic termination' and 'decomposition-contraction mechanism' but doesn't explain what these entail, such as side effects, permissions needed, or response formats. For a multi-command tool with mutation operations (e.g., decompose, set_max_depth), this is a significant gap in safety and operational context.
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 well-structured with a clear purpose statement, numbered command list, and parameter notes. It's front-loaded with the main purpose, but could be more concise by integrating parameter details more tightly. Every sentence adds value, though some redundancy exists between the command list and parameter descriptions.
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 complexity of a multi-command tool with no annotations and no output schema, the description is incomplete. It lacks behavioral context for mutations, doesn't explain return values or error handling, and omits prerequisites like authentication. For a tool with advanced features and potential side effects, more guidance is needed to ensure safe and 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?
Schema description coverage is 100%, so the schema already documents all parameters. The description adds value by specifying which parameters are required for each command (e.g., 'only required for decompose command'), but doesn't provide additional meaning beyond what the schema offers, such as format examples or constraints. Baseline 3 is appropriate given high schema coverage.
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 this is a 'command tool to control the decomposition-contraction mechanism and automatic termination of Atom of Thoughts,' providing specific verbs (decompose, check, get, change) and resources (atoms, decomposition processes, conclusions, settings). It distinguishes from sibling tools by mentioning 'advanced features of AoT' but doesn't explicitly contrast with AoT or AoT-light beyond this implication.
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 says 'Use this tool to access advanced features of AoT,' which implies when to use it (for advanced control) but doesn't specify when NOT to use it or explicitly name alternatives like AoT or AoT-light. It lists five commands but doesn't guide on choosing between them or contextual prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
3 tool updates
- First observed
AoT - First observed
AoT-light - First observed
atomcommands
TDQS
Scored across 3 tools
The tools have unclear boundaries and significant functional overlap. AoT and AoT-light appear to be variants of the same core functionality, differing mainly in performance characteristics rather than distinct purposes. The atomcommands tool seems to expose features already described within AoT's decomposition-contraction mechanism, creating confusion about which tool to use for those operations.
The naming conventions are inconsistent and lack a clear pattern. AoT uses an acronym format, AoT-light adds a suffix, and atomcommands uses a compound word with no clear verb-noun structure. There's no consistent naming scheme across the three tools, making them harder to distinguish and remember.
With only 3 tools, the count feels thin for the apparent scope of complex reasoning and problem-solving. The server seems to cover a sophisticated domain that might benefit from more granular tools, but the tools themselves are broad in scope. The count isn't extreme but feels under-specified for the domain.
There are significant gaps in the tool surface for the reasoning domain. While the tools cover creation and management of thought atoms, there are no tools for querying, filtering, or analyzing existing atoms, no way to modify atom properties after creation, and no tools for collaborative or multi-session reasoning. The surface feels incomplete for the described sophisticated reasoning workflows.
Maintenance
Related MCP Connectors
AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.
MCP server for building and testing AI agents with multi-model experimentation and insights.
Deterministic reasoning stack for AI agents: simulate, decide & compute, plus cross-domain tools.
Your product team's shared strategic memory — an MCP server your AI tools reason over.
Related MCP Servers
- FlicenseAqualityCmaintenanceAn advanced MCP server that implements sophisticated sequential thinking using a coordinated team of specialized AI agents (Planner, Researcher, Analyzer, Critic, Synthesizer) to deeply analyze problems and provide high-quality, structured reasoning.1307-
- AlicenseAqualityDmaintenanceA MCP server that implements sequential thinking protocols, provides structured problem-solving methods, decomposes complex problems into manageable steps, and supports iterative optimization and alternative reasoning paths.12Apache 2.0
- AlicenseNot gradedqualityDmaintenanceA powerful MCP server that enhances LLMs with advanced sequential thinking capabilities, supporting 19 thinking modes for structured reasoning and complex cognitive tasks.17MIT
- AlicenseAqualityAmaintenanceA reasoning orchestration MCP server that enables tree-of-thoughts thinking, multi-phase planning, and multi-agent evaluation for complex problem-solving.6443 npm3MIT