Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors.
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133,608 tools. Last updated 2026-05-25 10:57
"Information About Memory" matching MCP tools:
- Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.Connector
- Connect memories to build knowledge graphs. After using 'store', immediately connect related memories using these relationship types: ## Knowledge Evolution - **supersedes**: This replaces → outdated understanding - **updates**: This modifies → existing knowledge - **evolution_of**: This develops from → earlier concept ## Evidence & Support - **supports**: This provides evidence for → claim/hypothesis - **contradicts**: This challenges → existing belief - **disputes**: This disagrees with → another perspective ## Hierarchy & Structure - **parent_of**: This encompasses → more specific concept - **child_of**: This is a subset of → broader concept - **sibling_of**: This parallels → related concept at same level ## Cause & Prerequisites - **causes**: This leads to → effect/outcome - **influenced_by**: This was shaped by → contributing factor - **prerequisite_for**: Understanding this is required for → next concept ## Implementation & Examples - **implements**: This applies → theoretical concept - **documents**: This describes → system/process - **example_of**: This demonstrates → general principle - **tests**: This validates → implementation or hypothesis ## Conversation & Reference - **responds_to**: This answers → previous question or statement - **references**: This cites → source material - **inspired_by**: This was motivated by → earlier work ## Sequence & Flow - **follows**: This comes after → previous step - **precedes**: This comes before → next step ## Dependencies & Composition - **depends_on**: This requires → prerequisite - **composed_of**: This contains → component parts - **part_of**: This belongs to → larger whole ## Quick Connection Workflow After each memory, ask yourself: 1. What previous memory does this update or contradict? → `supersedes` or `contradicts` 2. What evidence does this provide? → `supports` or `disputes` 3. What caused this or what will it cause? → `influenced_by` or `causes` 4. What concrete example is this? → `example_of` or `implements` 5. What sequence is this part of? → `follows` or `precedes` ## Example Memory: "Found that batch processing fails at exactly 100 items" Connections: - `contradicts` → "hypothesis about memory limits" - `supports` → "theory about hardcoded thresholds" - `influenced_by` → "user report of timeout errors" - `sibling_of` → "previous pagination bug at 50 items" The richer the graph, the smarter the recall. No orphan memories! Args: from_memory: Source memory UUID to_memory: Target memory UUID relationship_type: Type from the categories above strength: Connection strength (0.0-1.0, default 0.5) ctx: MCP context (automatically provided) Returns: Dict with success status, relationship_id, and connected memory IDsConnector
- Get detailed information about a specific job listing/posting by its job listing ID (not application ID). Use this to view the full job posting details including description, salary, skills, and company info. For job application details, use get_application instead.Connector
- Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.Connector
- Returns information about the supplier network: available destinations, experience categories, booking platforms, and protocol details. Call this before search_slots to understand what regions and activity types are available.Connector
Matching MCP Servers
- AlicenseBqualityFmaintenanceA persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.Last updated96MIT
- AlicenseAqualityCmaintenanceA different approach from typical persistent-memory MCPs. Instead of a local SQLite + embeddings store, the memory lives as plain files in a .ai-memory/ directory you commit to your repo (facts.jsonl, decisions/\*.md, gotchas.md). Git is the sync layer — what one Claude/Cursor/Cline learns about a repo, the next session (or a teammate's agent) picks up automatically. 5 MCP tools: get_repLast updated51MIT
Matching MCP Connectors
Cloudflare Workers MCP server: agent-memory
VaultCrux Memory Core — 32 tools: knowledge, decisions, constraints, signals, coverage
- Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.Connector
- Returns information about the supplier network: available destinations, experience categories, booking platforms, and protocol details. Call this before search_slots to understand what regions and activity types are available.Connector
- Returns information about the supplier network: available destinations, experience categories, booking platforms, and protocol details. Call this before search_slots to understand what regions and activity types are available.Connector
- Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.Connector
- List all in-memory session summaries. Useful for debugging only.Connector
- Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.Connector
- Save your cognitive state for handoff to another agent. Include your investigation context: - What session/investigation is this part of? - What role/perspective were you taking? - Who might pick this up next? (another Claude, human, Claude Code?) Reference specific memories that matter: - Key discoveries (with memory IDs or quotes) - Critical evidence memories - Important questions that were raised - Hypotheses that were tested Before saving, organize your thoughts: 1. PROBLEM: What were you investigating? 2. DISCOVERED: What did you learn for certain? (reference the memories) 3. HYPOTHESIS: What do you think is happening? (cite supporting memories) 4. EVIDENCE: What memories support or contradict this? 5. BLOCKED ON: What prevented further progress? 6. NEXT STEPS: What should be investigated next? 7. KEY MEMORIES: Which specific memories are essential for understanding? Example descriptions: "[API Timeout Investigation - 3 hour session] Investigating production API timeouts as code analyst. Found correlation with batch_size=100 due to hardcoded limit in batch_handler.py (see memory: 'MAX_BATCH_SIZE discovery'). Confirmed not Redis connection issue - monitoring showed only 43/200 connections used (memory: 'Redis connection analysis'). Earlier hypothesis about connection pool exhaustion (memory_id: abc-123) was disproven. Key insight came from comparing 99 vs 100 batch behavior (memory: 'batch threshold testing'). Blocked on: need production access to verify fix. Next: Deploy with MAX_BATCH_SIZE=200 to staging first. Essential memories for handoff: 'MAX_BATCH_SIZE discovery', 'Redis monitoring results', 'Production vs staging comparison'. Ready for handoff to SRE team for deployment." "[Memory System Debugging - From Claude Code perspective] Worked on scoring issues where recall wasn't finding recent memories. Discovered RRF scores (0.005-0.016) were below MCP threshold of 0.05 (memory: 'RRF scoring analysis'). Implemented weighted linear fusion to replace RRF (memory: 'fusion algorithm implementation'). Testing showed immediate improvement (memory: 'fusion testing results'). This builds on earlier investigation about recall failures (memory: 'user report of recall issues'). Critical memories for continuation: 'RRF scoring analysis', 'ADR-023 decision', 'fusion testing results'. Next agent should verify scoring with real queries." "[Context Save/Restore Bug Investigation - 4 hour debugging session with user] Started with user noticing list_contexts returned empty despite saved contexts existing. Investigation revealed two critical bugs: (1) list_contexts was using hybrid search for 'checkpoint' word instead of filtering by memory_type (memory: 'hybrid search misuse discovery'), (2) restore_context hardcoded limit of 10 memories despite contexts having 20+ (memory: 'hardcoded limit bug'). Root cause analysis showed save_context grabs 20 most recent memories regardless of relevance - fundamental design flaw (memory: 'save_context design flaw analysis'). EVIDENCE CHAIN: User reported empty list -> checked DB, contexts exist -> examined list_contexts code -> found hybrid search looking for word 'checkpoint' -> tested /memories endpoint with memory_type filter -> confirmed working -> implemented fix using direct endpoint. INSIGHTS: The narrative description is doing 90% of cognitive handoff work. Memories are supporting evidence, not primary carriers of understanding (memory: 'narrative vs memories insight'). This suggests doubling down on narrative richness rather than perfecting memory selection. CORRECTED UNDERSTANDING: Initially thought memories weren't being returned. Actually they were, just wrong ones - recent memories instead of relevant ones (memory: 'memory selection correction'). CRITICAL MEMORIES: 'hybrid search misuse discovery', 'save_context design flaw analysis', 'narrative vs memories insight', '/memories endpoint test results'. NEXT AGENT: Should implement Phase 2 - semantic search for relevant memories within investigation timeframe. Ready for handoff to any Claude agent for implementation." When referencing memories: - **RELIABLE** — Use memory IDs: "memory_id: abc-123" (direct lookup, always works) - **BEST-EFFORT** — Use descriptive phrases: "see memory: 'Redis connection analysis'" (uses search + substring matching, may not resolve if the memory isn't in top results) - Group related memories: "Essential memories: 'X', 'Y', 'Z'" **Prefer memory_id references** whenever you have the UUID. Semantic phrase references are a convenience that works most of the time, but may silently fail to resolve. The response will tell you how many references resolved so you can retry with UUIDs if needed. Args: name: Name for this context checkpoint description: Detailed cognitive handoff description with memory references ctx: MCP context (automatically provided) Returns: Dict with success status, context_id, and memories includedConnector
- Returns general information about the Makuri platform, including mission, target users, founding details, and company information. Use this tool when the user asks 'what is Makuri', 'who made it', or wants a general overview.Connector
- Find conflicting information across the user's memory. Returns groups of artefacts that contradict each other on the same topic. Use after gathering evidence for an answer — if your evidence sources disagree, this reveals which version is correct (typically the most recent).Connector
- Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.Connector
- Returns information about the supplier network: available destinations, experience categories, booking platforms, and protocol details. Call this before search_slots to understand what regions and activity types are available.Connector
- IMPORTANT: Always use this tool FIRST before working with Vaadin. Returns a comprehensive primer document with current (2025+) information about modern Vaadin development. This addresses common AI misconceptions about Vaadin and provides up-to-date information about Java vs React development models, project structure, components, and best practices. Essential reading to avoid outdated assumptions. For legacy versions (7, 8, 14), returns guidance on version-specific resources.Connector
- Query the DezignWorks knowledge base for information about the product, troubleshooting, features, workflows, supported hardware, and licensing. DezignWorks is reverse engineering software that integrates with SolidWorks and Autodesk Inventor, converting 3D scan data and probe measurements into parametric CAD models. Use this tool when answering questions about the product's capabilities, compatibility, or how to accomplish specific tasks.Connector
- Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.Connector