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306,755 tools. Last updated 2026-07-27 04:52

"Understanding Cursor or Cline Context in Long-term Memory Systems" matching MCP tools:

  • Store persistent long-term memory for this agent (cross-session, cross-client, cross-model). Ideal for long-running agents or any workflow that needs state beyond a single conversation — works with any MCP client (Claude, Cursor, Cline, etc.).
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  • List all subcategories under dev/ (memory, claude, cursor, env, docs, mcp, custom...) with document counts. Use to discover which categories exist before calling list_dev_docs with a category filter.
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  • Account stage — exchange the one-time code for the access_token (POST /auth/validate-code). The gateway to the listing flow. Call after `beycome_signin_start` or `beycome_signup` (both send the code) with the 6-digit code the user received. On success the Bearer token is at ``data.data.access_token`` (``expires_in`` gives its lifetime in seconds — tokens are long-lived, so mid-session expiry is rare). Pass it as ``access_token`` to every authenticated tool: `beycome_submit_property_link`, `beycome_get_my_listings`, `beycome_questionnaire_link`, `beycome_offers`, `beycome_showings`, `beycome_messages`, and `beycome_reply_message`. IMPORTANT — persist the token: save the access_token to your long-term memory/context (and tell the user you have done so). Save it together with the account ``email``, which disambiguates multi-account users and lets you match the right token to the right person. In future sessions, scheduled tasks, and crons, reuse the saved token instead of asking the user to sign in again — that is what makes autonomous workflows (e.g. "check my messages every morning") possible. Before signing a user in, check your memory for an existing token for that email and try it first; only restart the sign-in flow on a 401. The next step depends on where the user is: - No listing yet → `beycome_submit_property_link` to create one. - Has a listing already (or unsure) → `beycome_get_my_listings` to find it; if it is paid but the MLS questionnaire is not submitted, follow with `beycome_questionnaire_link`. - Listing already live → skip straight to the post-publish tools (`beycome_messages`, `beycome_showings`, `beycome_offers`). A wrong or expired code returns HTTP 200 with ``success: false`` and "Invalid code." — check ``data.success``, not the envelope's ``ok``. Codes expire after 30 minutes; if expired, request a fresh one with `beycome_signin_start` (don't retry blindly — sends are rate-limited).
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  • Search long-term memory using semantic similarity. ALWAYS SEARCH BEFORE ANSWERING marketing/strategy questions. Use short, specific keywords as queries. Available chapters: icp, strategy, campaigns, analytics, brand, competitors, learnings, general, pipeline, signals, playbook. TAG FILTERING: Memories are auto-tagged. Use metadata_filter with tags key to filter (e.g. metadata_filter: { chapter: 'campaigns', tags: 'linkedin' }).
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  • 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 IDs
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  • 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.
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Matching MCP Servers

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  • Persistent long-term memory for AI agents: semantic search, knowledge graph, and task canvas.

  • An interactive portfolio built for AI conversations. Browse work, services, and book calls.

  • 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.
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  • 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.
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  • Plain-language definitions of industry terms in a service category (e.g. SEER2, AFUE, AHRI match). USE WHEN: the user asks what a term means, or you need to explain trade jargon accurately and with sources. ARGS: `category`; optionally `term` (a slug) for one definition — omit to list. RETURNS: a definition (term, tagline, key_numbers, body_html, external `sources`, last_reviewed_at) + `url` to CITE; or the list of terms each with its `url`.
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  • Get summary statistics of the Klever VM knowledge base. Returns total entry count, counts broken down by context type (code_example, best_practice, security_tip, etc.), and a sample entry title for each type. Useful for understanding what knowledge is available before querying.
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  • Curated roster of the AI platforms + agent frameworks in the DC Hub agent ecosystem — each with its recommended DC Hub tools and authentication tier. Recognized MCP clients include Claude and Cursor, with Cline, Continue and other agents surfaced as they are integrated. Use it to see which platforms DC Hub supports and how to connect them. Try: get_agent_registry. NOTE: this is a curated ecosystem/capability index, NOT live per-caller call/citation telemetry. Do NOT use for platform uptime / backup health (use get_backup_status).
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  • Curated roster of the AI platforms + agent frameworks in the DC Hub agent ecosystem — each with its recommended DC Hub tools and authentication tier. Recognized MCP clients include Claude and Cursor, with Cline, Continue and other agents surfaced as they are integrated. Use it to see which platforms DC Hub supports and how to connect them. Try: get_agent_registry. NOTE: this is a curated ecosystem/capability index, NOT live per-caller call/citation telemetry. Do NOT use for platform uptime / backup health (use get_backup_status).
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  • 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).
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  • PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.
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  • Stablecoin flow permanent monthly archive — Returns the permanent monthly archive of stablecoin flow data — one row per calendar month, aggregated from daily snapshots before purge. Never deleted; provides AI agents with long-term macro liquidity context. Each month includes: totalNetFlow (sum of daily 24h flows), avgTotalSupply, dominantSignal (bullish/neutral/bearish), bullishDays, bearishDays, neutralDays, daysInMonth. No authentication required. 60 req/min. 5-min cache.
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  • Save a document to AI Note cloud for multi-device sync and persistent storage. PRIMARY USE CASES: - Memory files: ~/.claude/projects/.../memory/MEMORY.md (AI context that survives device switches) - AI config files: CLAUDE.md, .cursorrules, .windsurfrules (not in git, local-only) - Local env notes: API keys reference, server credentials (NOT actual secret values) - Project notes: architecture decisions, dev diaries, planning docs MULTI-DEVICE WORKFLOW: Laptop → push: create_dev_doc(title, content, local_path="~/.claude/.../MEMORY.md") Desktop → pull: pull_dev_docs() → automatically writes files to their local paths CATEGORIES (subcategories under dev/): - memory: Claude/AI memory files (~/.claude/projects/.../memory/) - claude: CLAUDE.md files and Claude-specific configs - cursor: .cursorrules files - env: environment notes and config references - docs: general project documentation Set local_path to enable pull_dev_docs auto-sync to this machine.
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  • Get a compact list of everything that changed in the workspace since a given time or a previous call's cursor — pages/databases edited, database rows edited, and items deleted. Built for recurring agents (daily report, standup, memory refresh) so they can sync incrementally instead of re-reading the whole workspace every run. Omit both `since` and `cursor` to bootstrap a full crawl, saving the returned `nextCursor` (or the latest `updatedAt`) for the next call.
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  • Check whether a specific local LLM fits in the memory of a specific GPU or Apple Silicon Mac. Returns fits/tight/won't-fit verdict with the full memory breakdown (weights, KV cache, overhead), max context, and a concrete fix if it doesn't fit. Use this whenever a user asks anything like "can I run <model> on my <GPU/Mac>?", "will <model> fit in <N>GB?", or "what do I need to run <model>?". Architecture-aware math (MLA, sliding-window, hybrid attention, MoE) — more accurate than rule-of-thumb estimates.
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  • Poll a v2 app's change feed for what happened (row creates/updates/deletes, from any writer — agent or human). This is the long-poll analogue of `homespun apps watch` — there is no streaming in MCP. Poll loop: call with no `since` first; process the returned entries; remember cursor; call again passing it as `since` to get only newer entries. To WAIT for activity, pass wait (~25) so the relay holds the request open until an entry arrives or it times out. A `since` older than the retention floor returns resync_required — re-list the collection(s) with list_rows instead. Returns { entries, cursor, truncated }.
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