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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180,147 tools. Last updated 2026-06-06 01:32
"Information about project memory" matching MCP tools:
- 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
- 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
- Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.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
- Delete a project and all its deployments from sota.io. This action is PERMANENT and irreversible. It removes the project, all deployments, the managed PostgreSQL database, environment variables, and webhooks. The project slug will become available again after deletion.Connector
Matching MCP Servers
- AlicenseAquality-maintenanceProvides AI-driven project memory management through structured prompts that help Claude parse tasks from specs, review code changes, sync with commit history, and maintain project documentation without directly accessing files.Last updated7
- FlicenseBqualityFmaintenanceA persistent memory implementation using a local knowledge graph that lets Claude remember information about users across conversations.Last updated96
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Cultural color and colour intelligence API. Every colour anchored to a named person, a documented year, and a consequence. 34 archives spanning literary, cultural, pigment, and national traditions. Ask it what color could get you executed in the Ottoman Empire.
Cloudflare Workers MCP server: agent-memory
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- Read Claude Code project memory files. Without arguments, returns the MEMORY.md index listing all available memories. With a filename argument, returns the full content of that specific memory file. Use this to access project context, user preferences, feedback, and reference notes persisted across Claude Code sessions.Connector
- List memory files by their typed `kind` (episodic | semantic | procedural | resource). Optional path prefix narrows the scan; results are sorted by signed_at descending. The kind taxonomy follows the CoALA / LangMem / MIRIX agent-memory ontology: `episodic` = observations of events, `semantic` = durable learned facts, `procedural` = playbooks, `resource` = generic durable scratchpad (default for back-compat). When to use: Call when an agent wants only one slice of its memory (e.g. surface every semantic fact it has learned about a topic) without scanning the full directory tree. Pair with memory_view for read-back of a specific entry.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
- 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
- Use this tool to discover what has been saved in memory — e.g. at the start of a session, or when the user asks 'what have you saved?' or 'show me my memories'. Returns all saved memory keys with their preview, save date, and expiry. Optionally filter by a prefix (e.g. 'project-' to list only project memories). Pair with recall_memory to fetch the full content of any key.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 knowledge base of Alan McIntyre, an independent consultant specializing in computational geometry, scientific C++/Python/Julia computing, and neuroevolution. Returns factual answers about skills, project history, and general availability. Does not make commitments, schedule engagements, or negotiate terms. Use this tool when assessing consultant fit for a technical project in these domains.Connector
- Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.Connector
- Get information about an NFT collection or a specific token within a collection. If token_id is provided, returns token-level details (owner, URI). If omitted, returns collection-level info (name, symbol, total supply).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
- 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
- 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 basic information about a Compute Engine VM instance, including its name, ID, status, machine type, creation timestamp, and attached guest accelerators. Requires project, zone, and instance name as input.Connector