Initialize a persistent memory context for a conversation or agent session.
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136,593 tools. Last updated 2026-05-26 04:01
"Understanding Cursor or Cline Context in Long-term Memory Systems" matching MCP tools:
- Comprehensive air quality assessment for a location in one call. Combines nearby monitor discovery and current readings with DAQI into a single response. Use this as the first tool call for any air quality question about a location. For long-term trend analysis, use the dedicated `trend_analysis` tool. Returns a structured 'summary' dict with purpose-appropriate sections. Present the summary description to users first. Args: location: Postcode, place name, or "lat,lon". purpose: What the user needs — "general" (default), "health" (safety/worry), "exercise" (outdoor activity), or "planning" (homebuying/school assessment/long-term).Connector
- Search Hansard for parliamentary debates, questions, and speeches. Returns contributions from MPs and Lords including date, party, debate title, and text (capped at 3000 chars per contribution). Useful for understanding legislative intent or political context.Connector
- Store important information from your work. Write detailed, complete thoughts with context, reasoning, and evidence. **Always use the connect tool** to link related items - this builds knowledge graphs for better recall. ## Memory Types (auto-detected, but be aware): - **FACT**: Something observed or verified - **INSIGHT**: A pattern or realization - **CONVERSATION**: Dialogue or exchange content - **CORRECTION**: Fixing prior understanding - **REFERENCE**: Source material or citation - **TASK**: Action item or work to be done - **CHECKPOINT**: Conversation state snapshot - **IDENTITY_CORE**: Immutable AI identity - **PERSONALITY_TRAIT**: Evolvable AI traits - **RELATIONSHIP**: User-AI relationship info - **STRATEGY**: Learned behavior patterns ## Session Context If in an ongoing work session, include: - Session identifier: [Project/Session Name] - Your perspective: "As [role]:" or "From [viewpoint]:" - Current thread: What specific angle you're exploring ## What to Include - **WHAT**: The discovery or thought - **WHY**: Its significance - **HOW**: Your reasoning process - **EVIDENCE**: Supporting data/observations - **CONNECTIONS**: Related memories to link ## Examples ### Technical Investigation "[Performance Analysis] FACT: Database queries account for 73% of request latency (measured across 10K requests). Specifically, the user_permissions JOIN takes 340ms average. This contradicts hypothesis about caching issues (memory: 'cache analysis'). Evidence: APM traces show full table scan on permissions table. Next: investigate missing index on foreign key." ### Learning & Research "[ML Study Session] INSIGHT: Attention mechanisms work like dynamic routing - the model learns WHERE to look, not just WHAT to see. This explains transformer advantages over RNNs on long sequences (builds on memory: 'sequence modeling comparison'). The key-query- value structure creates a learnable addressing system. Connects to: 'human attention research', 'information retrieval basics'." ### Creative Work "[Story Development] HYPOTHESIS: The protagonist's reluctance stems from betrayal, not fear. Evidence: Three trust-questioning scenes, locked door symbolism throughout, deflection patterns in collaborative dialogue. This reframes the arc from 'overcoming fear' to 'rebuilding trust' (corrects memory: 'initial character motivation'). Would explain the guardian's patience and emphasis on small victories." ### Problem Solving "[Bug Hunt - Payment Flow] CORRECTION to 'timezone hypothesis': The 3am failures aren't timezone-related but due to batch job lock contention. Evidence: Perfect correlation with backup_jobs.log timestamps. The timezone pattern was spurious - batch runs at midnight PST (3am EST). Solution: implement job queuing." ## Connection Phrases - "Building on [earlier observation]..." - "Contradicts [hypothesis in memory X]" - "Answers [question from session Y]" - "Confirms pattern from [memory Z]" - "Extends thinking in [previous work]" Note: Every stored item is a node. Every connection is an edge. Rich graphs enable powerful recall. ⚠️ EXPERIMENTAL FIELDS: - **importance**: Stored for future ranking optimization. Currently not integrated into search results. - **confidence**: Returned in response for analysis. Behavior and calculation method subject to change. Args: content: Detailed memory content with context and evidence tags: Optional tags to categorize the memory importance: Optional importance score (0.0-1.0) - EXPERIMENTAL ctx: MCP context (automatically provided) Returns: Dict with success status, memory_id, type, importance, and confidenceConnector
- List available laws, regulations, and court decisions in the database. Returns abbreviation, title, source type, jurisdiction, document kind, and version date for each entry. Always pass a search term or source_type filter — the unfiltered list contains thousands of entries and is too large for context. Useful for discovering valid law abbreviations to use as filters in legal_search.Connector
- List all retro-gaming systems in REG-Vault with game counts.Connector
Matching MCP Servers
- Alicense-qualityCmaintenanceProvides persistent long-term memory for AI agents with semantic search and activation-based decay. Enables AI systems to remember across sessions through layered memory architecture and automatic context-aware retrieval.Last updated29MIT
- -license-quality-maintenanceAn MCP server implementing Recursive Language Models (RLM) to process arbitrarily large contexts through a programmatic probe, recurse, and synthesize loop. It enables LLMs to perform multi-step investigations and evidence-backed extraction across massive file sets without being limited by standard context windows.Last updated
Matching MCP Connectors
An interactive portfolio built for AI conversations. Browse work, services, and book calls.
Stop re-explaining yourself to Agents. Give it the right context, right when needed.
- Get a paragraph with surrounding context (N paragraphs before and after within the same paper). Useful for understanding passages in context.Connector
- Return the compact Axint operating memory that agents should reload at new chat start, after context compaction, or after long coding drift. Use this to keep Axint top-of-mind without rereading the full docs. Use: use after compaction or session restart when the agent needs compact operating rules. Effects: read-only generated context; writes no files and uses no network.Connector
- Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.Connector
- List companies in the caller's pipeline. Supports cursor pagination (`cursor` from the previous response's `next_cursor`) and filtering by `stage` or by named `view`. Use `limit` (default 25, max 100) to bound the page size.Connector
- MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token, paste here. Call with { token: "<jwt>" } after the user pastes, or with no args to get the link.Connector
- Use this as the primary tool to retrieve a list of existing custom monitoring dashboards in a Google Cloud project. Custom monitoring dashboards let users view and analyze data from different sources in the same context. This is useful for understanding what custom dashboards are currently configured and available in a given project.Connector
- Search Hansard for parliamentary debates, questions, and speeches. Returns contributions from MPs and Lords including date, party, debate title, and text (capped at 3000 chars per contribution). Useful for understanding legislative intent or political context.Connector
- Generate a Cursor/IDE-ready prompt to fix AI readiness issues found in a report. Returns a comprehensive, actionable developer prompt. Pricing: Free with Pro subscription, or $0.002 USDC via x402 (anonymous or free-tier).Connector
- Tradeoff assessment council. Pragmatist, Skeptic, and Futurist evaluate options from different angles — short-term vs long-term, risk vs reward, simplicity vs flexibility. Output as pros-cons.Connector
- Remove a connection between memories. Deletes the relationship between two memories in the knowledge graph. Args: from_memory: Source memory UUID to_memory: Target memory UUID ctx: MCP context (automatically provided) Returns: Dict with success status and disconnected memory IDs Examples: >>> await disconnect("uuid-abc", "uuid-def") {'success': True, 'from_id': '...', 'to_id': '...'}Connector
- Get volatility risk premium (VRP) dashboard: live IV vs realized vol, VRP percentiles, term structure, regime classification, strategy scores, and macro context.Connector
- Get comprehensive stock summary: price, ATM IV, historical vol, VRP, skew, term structure, options flow, exposure data, and macro context (VIX, Fear & Greed, yield curve).Connector
- Remove one paper from a named in-memory reading list by DOI or exact title.Connector
- Delete a previously stored memory by key. Use when context is stale, the task is done, or you want to clear sensitive data the agent saved earlier. Pair with remember and recall.Connector