Document AI reasoning and decision-making for audit trails. Log analysis, plans, and observations without altering code.
MIT
536,568 tools. Updated 2026-09-08 18:52
"Tools for slow thinking, step-back reasoning, and contextual memory capabilities" matching MCP tools:
- Deconstruct complex problems with a 7-stage reverse thinking process to eliminate hallucination and achieve rigorous reasoning.Apache 2.0
- Analyze complex problems through sequential thinking, track assumptions, and manage multi-step reasoning with confidence scoring and session-based context.MIT
- Access the complete Astria operating guide to learn available tools, recommended workflows, and best practices for persistent memory management.MIT
- Apply step-by-step reasoning with web grounding to complex questions. Ideal for math, logic, comparisons, and multi-step arguments. Returns reasoned answers with numbered citations. Supports recency, domain, and search context filters.MIT
- Replace a previous thought with updated reasoning, preserving the original in revision history. Corrects incomplete or wrong thinking.MIT
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- Generate a diagram of a thinking chain, displaying thoughts, branch points, and revisions in Mermaid or ASCII format for sharing or navigating reasoning structures.MIT
- Record numbered thinking steps for structured reasoning, enabling complex problems to be broken into sequential steps with branching and revision. Manage session history, reset when needed, and finalize to persist outcomes to Memory Bank.MIT
- Start a named thinking chain to guide multi-step reasoning, routing to master when no custom chain matches. Use default or custom chains for freeform or checklist-based problem-solving.MIT
- Record each step of a reasoning process by adding numbered thoughts to a persistent chain. Ideal for analysis, debugging, design, and research tasks that require structured, sequential thinking.MIT
- Break down complex questions with step-by-step web-grounded reasoning and numbered citations. Supports recency, domain, and search context filters for math, logic, comparisons, and arguments.MIT
- Access documentation and usage guides for n8n workflow automation tools to understand available capabilities and implementation methods.MIT
- Guide step-by-step reasoning through iterative revision, branching, and verification for deep problem-solving.MIT
- Reads and returns the current thinking profile from the system configuration, automatically falling back to default values if the file is missing.AGPL 3.0
- Export the current thinking chain as markdown or JSON. Optionally export a single branch. Use for sharing reasoning with humans or other systems.MIT
- Maps symptom keywords to recommended playbooks and investigation queries. Get clear guidance for diagnosing performance issues like slow ingestion, memory pressure, or replication lag.MIT
- Get a Redis health snapshot covering memory, clients, keyspace, persistence, replication, and recent slow queries to diagnose slowness or high memory usage.MIT
- List available models, default selections, reasoning capabilities, and active backend status to choose the right configuration for your requests.MIT
- Break down complex problems into structured reasoning steps to track thinking, expected outcomes, and next actions for systematic problem-solving.MIT
- Retrieve per-step execution stats for a Dagster run, including timing, materializations, and expectation results to identify slow steps and verify outputs.MIT
- Compute cycle times of completed task groups segmented by project template to identify slow workflows and bottleneck steps. Optionally include per-step durations for deeper analysis.MIT