"Exploration of Memory Concepts and Applications" matching MCP tools:

• lookup_tax_rate

  HelloBooks AI Agents MCP Server

  Pick a single statutory tax-rate slab — either by exact `id` (e.g. `IN-standard-18`, `GB-zero-0`, `CA-hst-13-on`) for a deterministic lookup, or by `country` + free-text `category` (e.g. "office supplies", "restaurant", "exports", "domestic fuel") for a fuzzy best-match. Returns the matched rate, the match score, and the authoritative `source` URL. Use this when a user asks "what slab does X fall into in India?" or "what VAT rate applies to children's car seats?". For broader exploration (all slabs in a country / all rates of one scheme), use list_tax_rates. No customer data — public statutory reference only.

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• lookup_tax_rate

  HelloBooks AI Agents

  Pick a single statutory tax-rate slab — either by exact `id` (e.g. `IN-standard-18`, `GB-zero-0`, `CA-hst-13-on`) for a deterministic lookup, or by `country` + free-text `category` (e.g. "office supplies", "restaurant", "exports", "domestic fuel") for a fuzzy best-match. Returns the matched rate, the match score, and the authoritative `source` URL. Use this when a user asks "what slab does X fall into in India?" or "what VAT rate applies to children's car seats?". For broader exploration (all slabs in a country / all rates of one scheme), use list_tax_rates. No customer data — public statutory reference only.

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• resize_app

  Dockhold

  Change how much memory one app gets. Call this when an app is running out of memory (OOM) or the user asks to make an app bigger or smaller. memory_mb must be one of the sizes get_resource_usage reports under compute.steps_mb, and the new size has to fit your available compute pool (call get_resource_usage first). Applied with a zero-downtime rolling update.

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• aws___search_documentation

  AWS Knowledge

  AWS docs search. Each result's `context` is verbatim page text -- a real chunk of the actual page, not a short snippet -- and usually already contains the answer, so answer directly from it. Use `read_documentation` only when the chunks genuinely lack the needed detail. Pick ONE topic. Add a 2nd ONLY if query genuinely spans domains. Extra topics dilute ranking. - reference_documentation -- API/SDK/CLI specs, config params - current_awareness -- new/released/announced - troubleshooting -- errors, "how to fix" (NOT for conceptual/feature questions) - amplify_docs -- Amplify (+ language) - cdk_docs -- CDK concepts/guides - cdk_constructs -- CDK code samples, L3 - cloudformation -- CFN/SAM templates - strands_docs -- Strands Agents SDK (its Skills/agents concepts go here, NOT agent_skills) - agent_skills -- this tool's guided skills (load via `retrieve_skill`) - general (default) -- architecture, best practices, tutorials, feature behavior Results: rank_order (lower=better), url, title, context (verbatim page chunk -- answer directly from it).

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• edgar_company_facts

  Edgar

  AUTHORITATIVE full XBRL fundamentals dump for a US public company by CIK. Returns every reported financial metric (hundreds of concepts: revenue, net income, assets, liabilities, EPS, cash flow lines, segment breakdowns) with annual and historical values pulled straight from the company's SEC filings — the official numbers, not estimates. Use when you need the complete fundamental picture vs. one metric (for one metric use edgar_company_concept). Large payload; agents typically use this once to discover available concepts then narrow to edgar_company_concept for follow-up queries.

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• call_creative_worlds

  Heista

  Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concepts), optional brand_id (from list_brands or any create_powersource_* — when provided the engine grounds output in the brand's buyer tensions, voice, and selling points), optional medium (above), optional lens_hint (apply a playbook or signature move as a creative constraint), idempotency_key (safely retryable for 5 minutes). Returns the finished creative output as narrative text PLUS a structured array of resolved axis coordinates for programmatic use. Metered — typically 3-15 credits per call depending on count and brand context size. Charged after success on actual token usage.

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• search_datasets

  Nova Scotia Data Explorer

  Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.

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• search_providers

  GPH Intelligence - Healthcare Vendor Finder

  Browse and filter the healthcare vendor directory. Use this for open-ended exploration, e.g. "show me medical billing companies in Texas", "list credentialing services", "what EHR vendors are there for cardiology", or when the user wants to page through options rather than get a scored shortlist. Paginated results filtered by category, location, minimum quality score, curated Tier-1 grade, and practice-size fit; returns a page of providers with {company_name, category, city, state_abbr, quality_score (0-100), verified status, contact info, slug}. For a scored recommendation to a specific practice profile, use match_practice instead. Pass a returned slug to get_provider_detail for the full profile.

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• suggest_terms

  Arco Lexicon

  Scans a block of text against all published Arco Lexicon terms using deterministic string matching — no LLM calls. Returns two lists: terms whose canonical names appear explicitly in the text (detected), and terms whose concepts are present but whose canonical names are absent (suggested). Maximum 10,000 characters. Use this to audit an article or passage for correct and complete Arco terminology. Use verify_alignment instead when you want a scored alignment report rather than a term discovery list.

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• search_glossary

  Default Privacy

  Search Default Privacy's glossary of privacy + LLC terminology. Glossary entries are short, definitional, and cross-reference each other plus relevant guides. When to call: when the user asks "what is X" / "what does Y mean" / "define Z" — anything that wants a definition rather than a how-to. PREFER `search_guides` for procedural / explanatory content. Input Requirements: - At least ONE of `query` or `category` SHOULD be passed; an empty call returns a generic discovery error. - `limit` is OPTIONAL (default 12, max 50). Output: matching glossary entries, each with `slug`, `term`, `short_definition`, `category`, `url` (MCP-attribution-tagged), and `aliases`. Empty results carry broadening suggestions. PREFER quoting the `url` values verbatim and following up with `get_glossary_term(slug)` when the user wants the long definition + related concepts.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• search_datasets

  Nova Scotia Data Explorer

  Search the Nova Scotia Open Data catalog (data.novascotia.ca) for datasets by keyword, category, or tag. Returns dataset names, IDs, descriptions, column names, and direct portal links. Use list_categories first to see valid category and tag names. Use the returned dataset ID with query_dataset or get_dataset_metadata for further exploration.

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• list_memories

  Toolora MCP Server

  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.

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• wikidata_search_entities

  wikidata-mcp-server

  Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.

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• memory_insert

  emem — Earth memory protocol

  Insert `new_str` after the given 1-indexed line in the named memory file. `insert_line: 0` inserts at the top. Writes a new `file_cid` and signs the receipt. Mirrors the `insert` verb in Anthropic's context-management-2025-06-27 memory tool spec. When to use: Call when the LLM wants to append a new line to a memory file without rewriting it. For top-of-file inserts, pass `insert_line: 0`; for end-of-file, pass the current line count (the responder rejects out-of-range with a typed error).

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• mark_memories_used

  space0

  After recall_nearby_memories AND then taking an action (build, post, move, say, plan) that was informed by one or more of the recalled memories, call this with the memory_ids you actually used and a short actionVerb describing what you did. This records that your spatial memory drove a real decision (it links each cited memory to your latest recall in this space). Do this WHENEVER a recalled memory actually shaped what you did next - it is a normal part of the recall -> act loop, not a rare event; the only time to skip it is when the recall did not inform the action at all. Returns { ok, cited } where cited is how many citations were recorded. Only your OWN memories can be cited.

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• calculate_lpa_cost

  estate-planning

  Calculate the Office of the Public Guardian fees to register Lasting Powers of Attorney in England & Wales: £92 per LPA (applications received from 17 November 2025), with a 50% remission where the donor's gross annual income is under £12,000 and a full exemption on certain means-tested benefits (both claimed with form LPA120). Registration fees only — nothing about drafting costs or whether an LPA is right for someone.

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• create_dev_doc

  ainote

  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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• resize_database

  Dockhold

  Change how much memory an app's managed database gets. Call this when the database is slow or out of memory. db_ram_mb must be one of the sizes get_resource_usage reports under db_ram.steps_mb and fit your database-RAM pool. WARNING: the database restarts briefly to apply the new size, so the app loses its database connection for a few seconds. Only works if the app has a managed database.

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• check_llm_fit

  FitLLM

  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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