{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server","slug":"saptadey-adaptive-graph-of-thoughts-mcp-server","name":"Scientific Thinking  (Adaptive Graph of Thoughts)","type":"mcp","url":"https://saptadey.github.io/Adaptive-Graph-of-Thoughts-MCP-server/","page_url":"https://unfragile.ai/saptadey-adaptive-graph-of-thoughts-mcp-server","categories":["mcp-servers","deployment-infra"],"tags":["mcp","model-context-protocol","smithery:SaptaDey/adaptive-graph-of-thoughts-mcp-server"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server__cap_0","uri":"capability://planning.reasoning.dynamic.confidence.scoring.for.query.processing","name":"dynamic confidence scoring for query processing","description":"This capability utilizes a graph-based structure to evaluate and score the confidence of various scientific hypotheses or answers based on real-time data inputs. By dynamically adjusting scores as new evidence is gathered from external databases, it allows for more nuanced and accurate reasoning compared to static models. The integration with the Model Context Protocol ensures seamless communication with AI clients, enhancing adaptability.","intents":["How can I assess the reliability of different scientific claims?","What is the confidence level of this hypothesis based on current data?","Can I get real-time updates on evidence supporting my query?"],"best_for":["scientists and researchers needing real-time data validation"],"limitations":["Dependent on external database availability and response time","Complex queries may increase processing time"],"requires":["Python 3.8+","Docker for deployment"],"input_types":["text","structured data"],"output_types":["structured data","confidence scores"],"categories":["planning-reasoning","scientific-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server__cap_1","uri":"capability://data.processing.analysis.real.time.evidence.gathering.from.external.databases","name":"real-time evidence gathering from external databases","description":"This capability connects to various external databases to fetch real-time evidence that supports or refutes scientific queries. It employs API integrations to pull in data dynamically, allowing users to access the most current information available. The modular design ensures that it can easily adapt to different data sources without significant reconfiguration.","intents":["How can I pull the latest research data to support my hypothesis?","What external sources can I connect to for real-time evidence?","Can I integrate my own database for evidence gathering?"],"best_for":["research teams looking for up-to-date scientific data"],"limitations":["Limited to the APIs and data formats of connected databases","May require custom integration for less common data sources"],"requires":["API keys for external databases","Python 3.8+"],"input_types":["text","structured queries"],"output_types":["structured data","text"],"categories":["data-processing-analysis","evidence-gathering"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server__cap_2","uri":"capability://tool.use.integration.seamless.integration.with.ai.clients.via.model.context.protocol","name":"seamless integration with ai clients via model context protocol","description":"This capability allows for smooth integration with AI clients using the Model Context Protocol, facilitating efficient data exchange and context management. It leverages a standardized schema for communication, ensuring that various AI models can interact with the system without compatibility issues. This design choice enhances the adaptability of the system to different AI environments.","intents":["How can I connect my AI model to this system?","What protocols are supported for integration?","Can I use different AI models interchangeably with this system?"],"best_for":["developers building AI applications that require data from multiple sources"],"limitations":["Requires adherence to the Model Context Protocol specifications","Limited to AI models that support the protocol"],"requires":["Knowledge of Model Context Protocol","Python 3.8+"],"input_types":["text","structured data"],"output_types":["structured data","text"],"categories":["tool-use-integration","ai-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server__cap_3","uri":"capability://automation.workflow.modular.deployment.with.docker","name":"modular deployment with docker","description":"This capability allows users to deploy the system easily using Docker containers, which encapsulate the application and its dependencies. This modular approach ensures that the application can run consistently across different environments without configuration issues. The use of Docker also facilitates scaling and management of resources effectively.","intents":["How can I deploy this application in my environment?","What are the steps to set up the system using Docker?","Can I scale this application easily?"],"best_for":["DevOps teams managing containerized applications"],"limitations":["Requires Docker installation and familiarity with container management","Resource-intensive if not properly configured"],"requires":["Docker 20.10+","Python 3.8+"],"input_types":["Dockerfile","configuration files"],"output_types":["running application instance"],"categories":["automation-workflow","deployment"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"smithery_saptadey-adaptive-graph-of-thoughts-mcp-server__cap_4","uri":"capability://planning.reasoning.graph.based.reasoning.for.complex.queries","name":"graph-based reasoning for complex queries","description":"This capability employs a graph structure to represent and analyze complex relationships between scientific concepts, enabling advanced reasoning. By utilizing nodes and edges to map out connections, it allows for more sophisticated query handling than traditional linear approaches. This structure supports multi-faceted reasoning, making it ideal for scientific inquiries.","intents":["How can I analyze relationships between different scientific concepts?","What is the best way to handle complex queries in scientific research?","Can I visualize the relationships in my data?"],"best_for":["scientists and researchers dealing with complex data relationships"],"limitations":["Graph complexity may lead to performance issues with very large datasets","Requires understanding of graph theory for optimal usage"],"requires":["Python 3.8+","Graph database or library"],"input_types":["structured data","text"],"output_types":["graph visualizations","structured data"],"categories":["planning-reasoning","data-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":32,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","Docker for deployment","API keys for external databases","Knowledge of Model Context Protocol","Docker 20.10+","Graph database or library"],"failure_modes":["Dependent on external database availability and response time","Complex queries may increase processing time","Limited to the APIs and data formats of connected databases","May require custom integration for less common data sources","Requires adherence to the Model Context Protocol specifications","Limited to AI models that support the protocol","Requires Docker installation and familiarity with container management","Resource-intensive if not properly configured","Graph complexity may lead to performance issues with very large datasets","Requires understanding of graph theory for optimal usage","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.45,"ecosystem":0.49000000000000005,"match_graph":0.25,"freshness":0.5,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:28.138Z","last_scraped_at":"2026-05-03T15:19:37.912Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=saptadey-adaptive-graph-of-thoughts-mcp-server","compare_url":"https://unfragile.ai/compare?artifact=saptadey-adaptive-graph-of-thoughts-mcp-server"}},"signature":"hMtoPBrVO2N8V0SGLPGyx1O4FNEU/EbXXTK91md08iDWgEG7bN1lzN3oB2UkBRhTb0tNXtoWTZ5gfCI8+irwAA==","signedAt":"2026-06-21T03:20:51.993Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/saptadey-adaptive-graph-of-thoughts-mcp-server","artifact":"https://unfragile.ai/saptadey-adaptive-graph-of-thoughts-mcp-server","verify":"https://unfragile.ai/api/v1/verify?slug=saptadey-adaptive-graph-of-thoughts-mcp-server","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}