{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github_mcp-spences10-mcp-sequentialthinking-tools","slug":"mcp-spences10-mcp-sequentialthinking-tools","name":"mcp-sequentialthinking-tools","type":"mcp","url":"https://github.com/spences10/mcp-sequentialthinking-tools","page_url":"https://unfragile.ai/mcp-spences10-mcp-sequentialthinking-tools","categories":["mcp-servers"],"tags":["mcp","model-context-protocol","modelcontextprotocol","tool"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_0","uri":"capability://planning.reasoning.sequential.thought.decomposition.with.state.tracking","name":"sequential-thought-decomposition-with-state-tracking","description":"Breaks down complex problems into numbered sequential thoughts with full state management, supporting non-linear exploration through branching and revision. Uses a ThoughtData interface to track thought content, position, branch relationships, and associated recommendations. The ToolAwareSequentialThinkingServer class maintains a thought_history array and branches record, allowing LLMs to explore alternative solution paths while preserving the original reasoning chain.","intents":["I want to guide an LLM through multi-step problem solving while tracking all intermediate reasoning steps","I need to support branching exploration where the LLM can try alternative approaches and backtrack","I want to maintain a complete audit trail of how a solution was derived across multiple thinking steps"],"best_for":["LLM application developers building agentic reasoning systems","Teams implementing chain-of-thought workflows with tool orchestration","Builders creating explainable AI systems that need to show reasoning steps"],"limitations":["No built-in persistence — thought history exists only in server memory and is lost on restart","Branching creates exponential state growth if not pruned; no automatic garbage collection of abandoned branches","Thought numbering is linear; complex non-sequential workflows may require manual coordination"],"requires":["Node.js 18+","TypeScript 4.5+ (for type safety with ThoughtData interface)","MCP client implementation using tmcp v1.16.1 or compatible"],"input_types":["structured thought objects with thought_number, thought content, and optional branch_id","text descriptions of problem-solving steps"],"output_types":["structured ThoughtData objects with metadata","branch tracking records mapping branch IDs to thought arrays"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_1","uri":"capability://tool.use.integration.tool.recommendation.engine.with.confidence.scoring","name":"tool-recommendation-engine-with-confidence-scoring","description":"Analyzes each sequential thinking step and recommends which MCP tools should be applied next, returning structured recommendations with confidence scores and rationales. The processThought() method evaluates available_tools (stored as a Map of registered MCP tools) against the current thought context, generating StepRecommendation objects that include tool names, confidence levels, and reasoning. This enables LLMs to make informed tool-selection decisions rather than blindly attempting all available tools.","intents":["I want the system to suggest which tools are most relevant for the current problem-solving step","I need confidence scores to prioritize tool attempts when multiple tools could apply","I want explanations for why specific tools are recommended at each step"],"best_for":["Developers building multi-tool LLM agents that need intelligent tool routing","Teams with large tool inventories who want to reduce trial-and-error tool selection","Builders creating transparent AI systems that explain tool choices to users"],"limitations":["Recommendations are heuristic-based; no machine learning model training on tool effectiveness","Confidence scores reflect the server's analysis, not actual tool success probability","Tool availability must be registered upfront; dynamic tool discovery is not supported","No feedback loop to improve recommendations based on actual tool execution outcomes"],"requires":["MCP tools registered in the available_tools Map before processThought() is called","Tool metadata including descriptions and input schemas for analysis","LLM client to interpret and act on recommendations"],"input_types":["ThoughtData objects containing current problem context and thought history","Tool registry with MCP tool definitions and schemas"],"output_types":["StepRecommendation objects with tool names, confidence scores (0-1), and rationale text","structured JSON recommendations compatible with MCP protocol"],"categories":["tool-use-integration","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_2","uri":"capability://tool.use.integration.mcp.tool.registry.and.discovery","name":"mcp-tool-registry-and-discovery","description":"Maintains a Map of registered MCP tools with their schemas and metadata, enabling the server to discover available tools and analyze their applicability to problem-solving steps. The available_tools Map stores tool definitions that can be queried during recommendation generation. Version 0.0.3 added explicit tool listing capabilities, allowing clients to request the full inventory of registered tools and their specifications.","intents":["I want to query which MCP tools are currently available in the system","I need to register new tools dynamically so they can be recommended","I want to inspect tool schemas to understand what each tool can do"],"best_for":["Developers building extensible MCP-based systems with pluggable tools","Teams managing large tool ecosystems who need centralized tool discovery","Builders creating UI dashboards that display available tools to end users"],"limitations":["Tool registration is static per server instance; no hot-reloading of tools without restart","No tool versioning support; only one version of each tool can be registered","Tool schemas must be manually provided; no automatic schema inference from tool implementations","No tool dependency resolution; tools that require other tools must be managed externally"],"requires":["MCP tool definitions with schemas conformable to valibot validation","Tool metadata including name, description, and input/output specifications","MCP client implementation to call tool discovery endpoints"],"input_types":["MCP tool definitions with JSON schemas","tool registration requests with name and metadata"],"output_types":["tool inventory list with names and descriptions","detailed tool specifications including input schemas and capabilities"],"categories":["tool-use-integration","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_3","uri":"capability://memory.knowledge.configurable.thought.history.management.with.limits","name":"configurable-thought-history-management-with-limits","description":"Manages thought history with configurable memory limits to prevent unbounded growth of the thought_history array. Version 0.0.3 added explicit memory management capabilities, allowing configuration of maximum history size and automatic pruning of older thoughts when limits are exceeded. This prevents memory exhaustion in long-running reasoning sessions while preserving recent context.","intents":["I want to limit memory usage in long-running LLM reasoning sessions","I need to configure how many historical thoughts are retained for context","I want to ensure the server doesn't run out of memory during extended problem-solving"],"best_for":["Developers deploying MCP servers in resource-constrained environments","Teams running long-lived reasoning sessions that accumulate many thoughts","Builders creating production systems that need predictable memory footprints"],"limitations":["Pruning is destructive; once old thoughts are removed, they cannot be recovered","No selective pruning strategy; oldest thoughts are removed regardless of importance","History limits are per-server instance; no distributed memory management across multiple servers","No metrics or monitoring for history size; developers must manually track memory usage"],"requires":["Configuration parameter for maximum history size (default implementation-dependent)","MCP server restart to change history limits (no runtime reconfiguration)"],"input_types":["configuration objects with maxHistorySize parameter","thought objects to be added to history"],"output_types":["pruned thought_history array respecting configured limits","status indicating whether pruning occurred"],"categories":["memory-knowledge","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_4","uri":"capability://planning.reasoning.branching.and.revision.support.with.branch.tracking","name":"branching-and-revision-support-with-branch-tracking","description":"Enables non-linear problem-solving by supporting branching where the LLM can explore alternative solution paths and revise previous thoughts. The branches record maps branch IDs to separate thought arrays, allowing the server to maintain multiple solution hypotheses simultaneously. When a branch is created, a new thought array is initialized; when a branch is merged or abandoned, the server can switch context between branches without losing the original reasoning chain.","intents":["I want to explore multiple solution approaches in parallel without losing the original reasoning","I need to backtrack to a previous thought and try a different approach","I want to compare alternative solution paths and merge the best insights"],"best_for":["Developers building exploratory AI systems that need to try multiple hypotheses","Teams implementing complex reasoning workflows with decision points","Builders creating interactive systems where users can guide the LLM to explore alternatives"],"limitations":["Branching creates exponential state growth; no automatic pruning of low-confidence branches","No merge strategy defined; combining insights from multiple branches requires manual logic","Branch IDs must be managed by the client; no automatic branch naming or organization","No visualization or debugging tools for branch exploration; developers must track branches manually"],"requires":["branch_id parameter in ThoughtData to identify which branch a thought belongs to","Client logic to create, switch, and manage branches","Mechanism to track which branch is currently active"],"input_types":["ThoughtData objects with branch_id field","branch creation requests with parent branch ID","branch switch requests to change active branch"],"output_types":["branches record mapping branch IDs to thought arrays","current active branch identifier","branch metadata including creation timestamp and parent branch"],"categories":["planning-reasoning","memory-knowledge"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_5","uri":"capability://data.processing.analysis.valibot.schema.validation.for.thought.input","name":"valibot-schema-validation-for-thought-input","description":"Validates incoming thought data against a SequentialThinkingSchema defined using valibot, ensuring type safety and correctness before processing. The schema enforces required fields (thought content, thought_number), optional fields (branch_id, recommendations), and data type constraints. This validation occurs before the processThought() method executes, preventing malformed thoughts from corrupting server state.","intents":["I want to ensure only valid thought objects are processed by the server","I need type-safe validation that catches schema violations early","I want clear error messages when clients send malformed thought data"],"best_for":["Developers building robust MCP servers that need input validation","Teams using TypeScript who want compile-time and runtime type safety","Builders creating client libraries that need to validate data before sending to the server"],"limitations":["Valibot validation adds ~5-10ms latency per thought processing (minor but measurable)","Schema is defined at server compile-time; no runtime schema updates without restart","Validation errors are valibot-specific; custom error formatting requires wrapper logic","No schema versioning; schema changes require coordinated updates across all clients"],"requires":["valibot library (included in dependencies)","@tmcp/valibot integration for MCP protocol compatibility","TypeScript 4.5+ for type inference from schema"],"input_types":["raw JSON thought objects from MCP clients","ThoughtData objects with optional fields"],"output_types":["validated ThoughtData objects","valibot validation errors with field-level details"],"categories":["data-processing-analysis","safety-moderation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_6","uri":"capability://tool.use.integration.tmcp.based.mcp.protocol.implementation","name":"tmcp-based-mcp-protocol-implementation","description":"Implements the Model Context Protocol using tmcp (v1.16.1) instead of the original @modelcontextprotocol/sdk, providing type-safe MCP communication over standard I/O. The ToolAwareSequentialThinkingServer class extends or integrates with tmcp's server base, handling MCP message serialization, tool resource definitions, and protocol compliance. Version 0.0.4 migrated to tmcp for improved type safety and maintenance.","intents":["I want to build an MCP server that communicates with MCP clients using the standard protocol","I need type-safe MCP message handling without manual serialization","I want to expose server capabilities (tools, resources) to MCP clients"],"best_for":["Developers building MCP servers in TypeScript","Teams integrating with MCP-compatible clients like Claude Desktop or custom applications","Builders creating tool ecosystems that need standardized protocol compliance"],"limitations":["tmcp is a newer library with smaller ecosystem than @modelcontextprotocol/sdk","Migration from SDK to tmcp may require code changes in existing implementations","No built-in authentication; MCP communication is unencrypted over stdio","Limited to stdio transport; no HTTP or WebSocket support for remote MCP servers"],"requires":["tmcp v1.16.1 or compatible","Node.js 18+ with stdio stream support","MCP client implementation compatible with tmcp protocol version"],"input_types":["MCP protocol messages over stdin","JSON-RPC formatted requests from MCP clients"],"output_types":["MCP protocol responses over stdout","JSON-RPC formatted responses with tool recommendations and thought data"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github_mcp-spences10-mcp-sequentialthinking-tools__cap_7","uri":"capability://planning.reasoning.thought.metadata.enrichment.with.recommendations","name":"thought-metadata-enrichment-with-recommendations","description":"Enriches each thought with associated StepRecommendation objects that include tool suggestions, confidence scores, and rationales. When a thought is processed, the server analyzes the context and generates recommendations that are attached to the ThoughtData object. This allows clients to access both the raw thought and the server's analysis of what tools should be applied next, creating a rich decision context for the LLM.","intents":["I want each thought to include recommendations for what to do next","I need to see why the server recommends specific tools for a given step","I want to track which recommendations were actually followed vs ignored"],"best_for":["Developers building explainable AI systems that show reasoning and recommendations","Teams analyzing LLM decision-making patterns and tool usage","Builders creating interactive systems where users can see and override recommendations"],"limitations":["Recommendations are generated per-thought; no cross-thought optimization or planning","No feedback mechanism to learn from which recommendations were actually useful","Recommendation generation adds latency (~50-100ms per thought depending on tool count)","No caching of recommendations; identical thoughts generate new recommendations each time"],"requires":["available_tools Map populated with registered MCP tools","Tool schemas with descriptions for recommendation analysis","LLM or heuristic engine to generate recommendations (implementation-dependent)"],"input_types":["ThoughtData objects with thought content and context","Tool registry for analysis"],"output_types":["ThoughtData objects with embedded StepRecommendation array","recommendation objects with tool names, confidence scores, and rationale text"],"categories":["planning-reasoning","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":42,"verified":false,"data_access_risk":"moderate","permissions":["Node.js 18+","TypeScript 4.5+ (for type safety with ThoughtData interface)","MCP client implementation using tmcp v1.16.1 or compatible","MCP tools registered in the available_tools Map before processThought() is called","Tool metadata including descriptions and input schemas for analysis","LLM client to interpret and act on recommendations","MCP tool definitions with schemas conformable to valibot validation","Tool metadata including name, description, and input/output specifications","MCP client implementation to call tool discovery endpoints","Configuration parameter for maximum history size (default implementation-dependent)"],"failure_modes":["No built-in persistence — thought history exists only in server memory and is lost on restart","Branching creates exponential state growth if not pruned; no automatic garbage collection of abandoned branches","Thought numbering is linear; complex non-sequential workflows may require manual coordination","Recommendations are heuristic-based; no machine learning model training on tool effectiveness","Confidence scores reflect the server's analysis, not actual tool success probability","Tool availability must be registered upfront; dynamic tool discovery is not supported","No feedback loop to improve recommendations based on actual tool execution outcomes","Tool registration is static per server instance; no hot-reloading of tools without restart","No tool versioning support; only one version of each tool can be registered","Tool schemas must be manually provided; no automatic schema inference from tool implementations","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.3790404432104456,"quality":0.41,"ecosystem":0.52,"match_graph":0.25,"freshness":0.75,"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":"2026-05-24T12:16:22.065Z","last_scraped_at":"2026-05-03T14:23:34.856Z","last_commit":"2026-04-29T16:30:50Z"},"community":{"stars":578,"forks":85,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mcp-spences10-mcp-sequentialthinking-tools","compare_url":"https://unfragile.ai/compare?artifact=mcp-spences10-mcp-sequentialthinking-tools"}},"signature":"1HCsrgSFMZ5t50MqCdGeX8ApWOd3yT9IK0CLfTgQDiNo+aXI62Ai+hCn8vRbCHg7lpU54Meb4/jzoQ1SShRCBA==","signedAt":"2026-06-20T02:22:43.105Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mcp-spences10-mcp-sequentialthinking-tools","artifact":"https://unfragile.ai/mcp-spences10-mcp-sequentialthinking-tools","verify":"https://unfragile.ai/api/v1/verify?slug=mcp-spences10-mcp-sequentialthinking-tools","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"}}