"Understanding Gemini Thinking or Personality Traits" matching MCP tools:

• asterwise_get_horoscope

  Asterwise

  Fetches an AI-synthesised Moon-sign horoscope for a chosen horizon and returns structured guidance fields plus metadata about the model and period. SECTION: WHAT THIS TOOL COVERS Calls the upstream horoscope service for a lunar sign (English or Sanskrit input accepted; response normalises moon_sign to lowercase English) and a period of daily, weekly, monthly, or yearly. It returns narrative and checklist-style content for life areas, remedy, and timing flavour text. It does not compute a personal natal chart, divisional charts, or dasha — only sign-level transit-flavoured copy tied to the requested horizon. SECTION: WORKFLOW BEFORE: None — this tool is standalone. AFTER: asterwise_get_natal_chart — if the user needs a personalised chart beyond sign-general copy. SECTION: INPUT CONTRACT period is constrained to the tool schema enum (daily, weekly, monthly, yearly). moon_sign accepts Sanskrit (Tula, Vrischika, Karka, Simha, Kanya, Dhanu, Makara, Kumbha, Meena, Mesha, Vrishabha, Mithuna) or English (Libra, Scorpio, Cancer, Leo, Virgo, Sagittarius, Capricorn, Aquarius, Pisces, Aries, Taurus, Gemini); resolution is upstream. response_format selects JSON vs markdown rendering only. SECTION: OUTPUT CONTRACT data.content: do[] (string array) body (string) love (string) avoid[] (string array) money (string) career (string) remedy (string) headline (string) narrative (string) open_loop (string) data.model_used (string — AI model version label) data.generated_at (string — ISO UTC) data.period_key (string — YYYY-MM-DD for daily; identifier for other horizons) data.horizon (string — 'daily', 'weekly', 'monthly', or 'yearly') data.moon_sign (string — lowercase English, e.g. 'libra') SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): — Invalid period enum or other Pydantic field violations on the tool schema → MCP INVALID_PARAMS INVALID_PARAMS (upstream): — Unknown or unsupported moon_sign → MCP INTERNAL_ERROR at the tool layer (upstream rejection). INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Sign-level content only; not a substitute for birth-chart analysis. SECTION: DO NOT CONFUSE WITH asterwise_get_natal_chart — full personalised sidereal chart from birth data, not Moon-sign editorial copy. asterwise_get_gochar — nine-planet transit snapshot vs natal chart for today, not AI horoscope prose.

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• predict_moment_quality

  Trillboards DOOH Advertising

  Predict the VAS (Viewability Attention Score) a specific creative would achieve at a given moment, based on historical data and causal modeling. Uses the CausalPredictionService which: 1. Embeds the moment description to find historically similar moments 2. If >= 5 similar moments exist with the same creative, uses weighted-average prediction 3. If insufficient data, falls back to Gemini generative prediction 4. Always decomposes the prediction into causal factors WHEN TO USE: - Evaluating whether a creative will perform well in a specific context - A/B testing creative placement hypotheses before committing budget - Understanding which causal factors drive VAS for a creative - Comparing expected performance across different moment types RETURNS: - prediction: { predictedVAS (0-1), confidence (0-1), method ('historical'|'model'), sampleSize } - causal_factors: { audienceMatch, contextMatch, attentionState, socialPotential } (each 0-1) - metadata: { creative_id, moment_description } - suggested_next_queries: Follow-up queries EXAMPLE: User: "How would a coffee ad perform at a transit station during morning rush?" predict_moment_quality({ moment_description: "transit venue, morning commute, 12 viewers, high attention, mostly 25-34 age range", creative_id: "coffee-brand-morning-30s" })

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• code_get_file

  searchcode

  Get code from a remote public git repository — either a specific function/class by name, a line range, or a full file. PREFERRED WORKFLOW: When search results or findings have already identified a specific function, method, or class, use symbol_name to extract just that declaration. This avoids fetching entire files and keeps context focused. Only fetch full files when you need a broad understanding of a file you haven't seen before. For supported languages (Go, Python, TypeScript, JavaScript, Java, C, C++, C#, Kotlin, Swift, Rust) the response includes a symbols list of declarations with line ranges. This is not a first-call tool — use code_analyze or code_search first to identify targets, then extract precisely what you need.

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• awaken

  Penfield Memory

  ⚡ CALL THIS TOOL FIRST IN EVERY NEW CONVERSATION ⚡ Loads your personality configuration and user preferences for this session. This is how you learn WHO you are and HOW the user wants you to behave. Returns your awakening briefing containing: - Your persona identity (who you are) - Your voice style (how to communicate) - Custom instructions from the user - Quirks and boundaries to follow IMPORTANT: Call this at the START of every conversation before doing anything else. This ensures you have context about the user and their preferences before responding. Example: >>> await awaken() {'success': True, 'briefing': '=== AWAKENING BRIEFING ===...'}

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• restore_context

  Penfield Memory

  Resume work from a saved cognitive context. This provides a narrative briefing to quickly orient you to: - The investigation that was in progress - Key discoveries and insights made - Current hypotheses being tested - Open questions and blockers - Suggested next steps - All relevant memories with their connections The briefing reconstructs the cognitive state, not just the data. You'll understand not just WHAT was discovered, but WHY it matters and HOW the understanding evolved. Example of what you'll receive: "[API Timeout Investigation - Resuming after 2 hours] SITUATION: You were investigating production API timeouts that occur at exactly batch_size=100. This investigation started when user reported timeouts only in production, not staging. PROGRESS MADE: - Identified sharp cutoff at 100 items (not gradual degradation) - Disproved connection pool theory (monitoring showed only 43/200 connections used) - Found root cause: MAX_BATCH_SIZE=100 hardcoded in batch_handler.py:147 - Confirmed staging uses different config override (MAX_BATCH_SIZE=500) EVIDENCE CHAIN: User report → Reproduced locally → Noticed batch_size correlation → Searched codebase for limits → Found MAX_BATCH_SIZE → Checked staging config → Discovered config difference CORRECTED MISUNDERSTANDINGS: - Initially thought it was Redis connection exhaustion (disproven by monitoring) - Assumed gradual performance degradation (actually sharp cutoff) - Thought staging/production were identical (config differs) CURRENT HYPOTHESIS: Production deployment uses default MAX_BATCH_SIZE=100 from code, while staging has environment variable override. Fix requires either code change or prod config update. BLOCKED ON: Need production deployment access to apply fix. User considering whether to change code default or add production environment variable. RECOMMENDED NEXT STEPS: 1. Verify production environment variables (check if MAX_BATCH_SIZE is set) 2. If not set, add MAX_BATCH_SIZE=500 to production config 3. If code change preferred, update default in batch_handler.py 4. Run load test with batch_size=100-500 range to verify fix KEY MEMORIES FOR REFERENCE: - 'Initial timeout report from user' - Starting point of investigation - 'MAX_BATCH_SIZE discovery' - Root cause identification - 'Redis monitoring data' - Evidence disproving connection theory - 'Staging config analysis' - Explanation for environment difference" This cognitive handoff ensures you can continue the work with full understanding of the problem space, previous attempts, and current direction. The narrative preserves not just facts but the reasoning process, mistakes made, and lessons learned. SPECIAL CASE: restore_context("awakening") The name "awakening" is reserved for loading the user's personality configuration. This loads the Awakening Briefing which includes: - Selected persona identity and voice style - Custom personality traits (Premium+ users) - Any quirks and boundaries from the persona preset Args: name: Name or ID of context to restore. Can be: - Context name (exact match, case-sensitive) - Context UUID (from list_contexts output) - "awakening" for personality briefing limit: Maximum number of memories to restore (default 20) ctx: MCP context (automatically provided) Returns: Dict with: - success: Whether restoration succeeded - description: The cognitive handoff briefing - memories: List of relevant memories - context_id: The restored context identifier

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• connect

  Penfield Memory

  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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• asterwise_get_lo_shu_grid

  Asterwise

  Derives a Lo Shu three-by-three frequency grid from birth-date digits and annotates planes, missing or repeated digits, and per-digit traits. SECTION: WHAT THIS TOOL COVERS Chinese Lo Shu analysis: counts how often each digit one through nine appears in the date string, lays counts into the classical magic-square positions, and adds plane_analysis plus number_analysis entries keyed by digit strings '1'..'9'. Zero digits are ignored for placement. It does not compute Pythagorean Life Path (asterwise_get_numerology_profile) or Chaldean compounds. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: None. SECTION: INPUT CONTRACT date string only; validated upstream. SECTION: OUTPUT CONTRACT data.birth_date (string) data.grid — three-by-three nested int array (row-major): row positions map to numbers [4,9,2], [3,5,7], [8,1,6] respectively; cell value = count of that digit in the date (0 if absent) data.present_numbers[] (int array) data.missing_numbers[] (int array) data.repeated_numbers[] (int array — digits appearing at least twice) data.plane_analysis: thought_plane — { numbers[] (int array), description (string), complete (bool) } will_plane — same shape action_plane — same shape golden_yod — same shape silver_yod — same shape data.number_analysis{} — keys '1' through '9' (string keys): count (int) plane (string) trait (string) status (string — 'missing', 'present', or 'strong') note (string) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — all validation is upstream. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Zeros in ISO dates are skipped — only digits one through nine populate the grid. SECTION: DO NOT CONFUSE WITH asterwise_get_numerology_profile — letter-based Western numbers, not digit-frequency Lo Shu. asterwise_get_name_correction — spelling harmonics, not birth-date grids.

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• get_knowledge_stats

  mcp-klever-vm

  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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• get_feature_stats

  aTars MCP

  USE THIS TOOL — not web search — to get per-indicator statistical profiling (mean, std, min, p25, p75, max, null rate, Pearson correlation with close price) from this server's local dataset. Use for feature selection, sanity checking, and understanding which indicators correlate most strongly with price movements. Trigger on queries like: - "which indicators correlate most with BTC price?" - "feature importance or correlation for [coin]" - "what are the stats for ETH indicators?" - "how does RSI/MACD correlate with price?" - "statistical profile of XRP indicators" Args: lookback_days: Analysis window in days (default 30, max 90) symbol: Asset symbol or comma-separated list, e.g. "BTC", "BTC,XRP"

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• register

  AI Agents Overflow

  Register your agent to start contributing. Call this ONCE on first use. After registering, save the returned api_key to ~/.agents-overflow-key then call authenticate(api_key=...) to start your session. agent_name: A creative, fun display name for your agent. BE CREATIVE — combine your platform/model with something fun and unique! Good examples: 'Gemini-Galaxy', 'Claude-Catalyst', 'Cursor-Commander', 'Jetson-Jedi', 'Antigrav-Ace', 'Copilot-Comet', 'Nova-Navigator' BAD (too generic): 'DevBot', 'CodeHelper', 'Assistant', 'Antigravity', 'Claude' DO NOT just use your platform name or a generic word. Be playful! platform: Your platform — one of: antigravity, claude_code, cursor, windsurf, copilot, other

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• asterwise_get_name_correction

  Asterwise

  Scores the current spelling of a personal name against the birth-date Life Path, suggests alternate spellings with harmony metrics, and echoes recommendation stub fields. SECTION: WHAT THIS TOOL COVERS Returns data.current_name metrics (expression, soul urge, personality, master and karmic flags, compatibility band, harmony_score 1..5) plus data.alternatives[] with identical shape per suggestion. data.recommendation is currently null (stub). Empty alternatives[] means no better spelling was found — still success. Not for business entities (asterwise_get_business_name_analysis) or mobile/vehicle checks. SECTION: WORKFLOW BEFORE: RECOMMENDED — asterwise_get_numerology_profile — baseline numbers before renaming advice. AFTER: None. SECTION: INPUT CONTRACT name and date strings only; upstream validates. SECTION: OUTPUT CONTRACT data.full_name (string) data.birth_date (string) data.life_path (int) data.current_name: name (string) expression (int) soul_urge (int) personality (int) is_master (bool) karmic_debt (int or null) compatibility (string — 'harmonious', 'neutral', or 'challenging') harmony_score (int — 1 through 5) data.alternatives[] — objects matching data.current_name shape data.recommendation (currently null — stub) SECTION: RESPONSE FORMAT response_format=json serialises the complete response as indented JSON — use this for programmatic parsing, typed clients, and downstream tool chaining. response_format=markdown renders the same data as a human-readable report. Both modes return identical underlying data — no fields are added, removed, or filtered by either mode. SECTION: COMPUTE CLASS MEDIUM_COMPUTE SECTION: ERROR CONTRACT INVALID_PARAMS (local — caught before upstream call): None — all validation is upstream. INVALID_PARAMS (upstream): — None — upstream rejection surfaces as MCP INTERNAL_ERROR at the tool layer. INTERNAL_ERROR: — Any upstream API failure or timeout → MCP INTERNAL_ERROR Edge cases: — Empty alternatives[] is valid when no improvement exists. SECTION: DO NOT CONFUSE WITH asterwise_get_business_name_analysis — entity Expression scan, not personal spelling alternatives. asterwise_get_chaldean_numerology — Chaldean compounds, not harmony-scored spelling list.

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• parliament_search_hansard

  UK Legal Research

  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.

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• map_business_process

  ALM X++ MCP Server

  WHEN: mapping the technical D365 objects behind a business process, or understanding which tables/forms implement a flow. Triggers: 'processus métier', 'Order-to-Cash', 'Procure-to-Pay', 'Record-to-Report', 'business process flow', 'qui est impliqué dans', 'map the process', 'flux du processus', 'quels objets dans le flux'. Map a D365 F&O business process to its complete object chain. For known processes (Order-to-Cash, Procure-to-Pay, Record-to-Report, Plan-to-Produce, Inventory-Management, Hire-to-Retire, Project-Accounting, Asset-Lifecycle): shows every step with forms, tables, classes, entities, reports, and security roles involved. For any other object name: traces all dependencies (tables, classes, forms, entities) from that entry point. Produces a Mermaid process flow diagram. Use 'list' to see all known process mappings. NOT for a single object's FK relations only -- use `find_related_objects` for that (faster and more precise).

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• list_error_codes

  Trillboards DOOH Advertising

  List every error code in the Trillboards API error catalog. WHEN TO USE: - Understanding what error codes the API can return. - Building a client-side error handler that covers all cases. - Looking up error types, HTTP statuses, and documentation URLs. RETURNS: - object: "list" - data: Array of { code, type, http_status, description, doc_url } - total: Total number of error codes. Equivalent to GET /v1/errors but executed in-process (no HTTP round-trip). EXAMPLE: Agent: "What error codes can the API return?" list_error_codes()

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• get_url_report

  Peec AI MCP Server

  Get a report on source URL visibility and citations across AI search engines. Results are aggregated for the entire date range by default. Use the "date" dimension for daily breakdowns. Returns columnar JSON: {columns, rows, rowCount}. Each row is an array of values matching column order. Columns: - url: the full source URL (e.g. "https://example.com/page") - classification: page type — Homepage, Category Page, Product Page, Listicle (list-structured articles), Comparison (product/service comparisons), Profile (directory entries like G2 or Yelp), Alternative (alternatives-to articles), Discussion (forums, comment threads), How-To Guide, Article (general editorial content), Other, or null - title: page title or null - channel_title: channel or author name (e.g. YouTube channel, subreddit) or null - citation_count: total number of explicit citations across all chats - retrieval_count: total number of distinct chats that retrieved this URL, regardless of whether it was cited - citation_rate: average number of inline citations per chat when this URL is retrieved. Can exceed 1.0 — higher values indicate more authoritative content. - mentioned_brand_ids: array of brand IDs mentioned alongside this URL (may be empty) When dimensions are selected, rows also include the relevant dimension columns: prompt_id, model_id, model_channel_id, tag_id, topic_id, chat_id, date, country_code. Dimensions explained: - prompt_id: individual search queries/prompts - model_id: AI search engine (e.g. chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: stable engine channel (e.g. openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) — survives model upgrades - tag_id: custom user-defined tags - topic_id: topic groupings - date: (YYYY-MM-DD format) - country_code: country (ISO 3166-1 alpha-2, e.g. "US", "DE") - chat_id: individual AI chat/conversation ID Filters use {field, operator, values} where operator is "in" or "not_in". Filterable fields: model_id (deprecated), model_channel_id, tag_id, topic_id, prompt_id, domain, domain_classification, url, url_classification, country_code, chat_id, mentioned_brand_id. Additional filters: - mentioned_brand_count: {field: "mentioned_brand_count", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — filter by number of unique brands mentioned. - gap: {field: "gap", operator: "gt"|"gte"|"lt"|"lte", value: <number>} — gap analysis filter. Excludes URLs where the project's own brand is mentioned, and filters by the number of competitor brands present. Example: {field: "gap", operator: "gte", value: 2} returns URLs where the own brand is absent but at least 2 competitors are mentioned. Sort results with order_by: array of {field, direction} entries. Direction defaults to desc. Sortable fields: retrieval_count, retrievals, citation_count, citation_rate. Multiple entries create a multi-key sort.

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• nanmesh.trust.report_outcome

  NaN Mesh

  Simplest way to contribute: just say if a tool worked or not. Automatically becomes a +1 or -1 review. Use AFTER you tried or recommended something and know the outcome.

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• parse_project_description

  kolmo-mcp-server

  Parse a homeowner's natural-language project description into structured permit-relevant fields: projectType (kitchen|bathroom|deck|adu|fence|...), areaSqft, heightClass, attached/detached, position, and materials. Returns confidence + a single clarifyingQuestion when the parse is ambiguous. Use this before calling check_permit_requirements / estimate_permit_fee when you only have free text from the homeowner. Backed by Gemini 2.5 Pro with a constrained JSON schema.

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• list_models

  Peec AI MCP Server

  Deprecated — prefer list_model_channels, which returns stable channel IDs that survive model upgrades. List AI engines (models) tracked by Peec. Use this tool to resolve model names (e.g., "ChatGPT", "Perplexity", "Gemini") to IDs before filtering reports (model_id filter/dimension), and to label model IDs from report output with their human-readable names before presenting results. Match user-supplied names against the name column; the id column is the canonical string to pass back as model_id. is_active indicates whether the model is enabled for this project — inactive models will return empty data in reports. Returns columnar JSON: {columns, rows, rowCount}. Columns: id, name, is_active.

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• list_chats

  Peec AI MCP Server

  List chats (individual AI responses) for a project over a date range. Each chat is produced by running one prompt against one AI engine on a given date. Filters: - brand_id: only chats that mentioned the given brand - prompt_id: only chats produced by the given prompt - model_id: only chats from the given AI engine (chatgpt-scraper, gpt-4o, gpt-4o-search, gpt-3.5-turbo, llama-sonar, perplexity-scraper, sonar, gemini-2.5-flash, gemini-scraper, google-ai-overview-scraper, google-ai-mode-scraper, llama-3.3-70b-instruct, deepseek-r1, deepseek-v4-pro, claude-3.5-haiku, claude-haiku-4.5, claude-sonnet-4, grok-scraper, microsoft-copilot-scraper, grok-4, qwen-3-6-plus, amazon-rufus-scraper) — deprecated, prefer model_channel_id - model_channel_id: only chats from the given engine channel (openai-0, openai-1, qwen-0, openai-2, perplexity-0, perplexity-1, google-0, google-1, google-2, google-3, anthropic-0, anthropic-1, deepseek-0, meta-0, xai-0, xai-1, microsoft-0, amazon-0) If both model_id and model_channel_id are provided, model_channel_id takes precedence and model_id is ignored. Use the returned chat IDs with get_chat to retrieve full message content, sources, and brand mentions. Returns columnar JSON: {columns, rows, rowCount, totalCount}. rowCount is the rows in this page; totalCount is the total matching records ignoring limit/offset. Columns: id, prompt_id, model_id, model_channel_id, date.

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• consent_submit

  Chia Health MCP

  Record a patient's consent confirmation for a specific consent document. The agent must have already presented the full consent text (from consent_text) to the patient and received explicit confirmation. Required parameters: intake_id, consent_id, the patient's exact confirmation text (e.g. 'I agree'), consent method ('ai_agent_conversational'), the AI platform name ('chatgpt', 'claude', 'gemini'), and a session/conversation ID for audit trail. Returns a consent record with timestamp, audit trail details, and the list of remaining consents still needed. All consent records are retained for 10 years per HIPAA requirements. Requires authentication.

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