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

• get_astrology_signs

  Western Astrology MCP Server by RoxyAPI

  Returns all 12 tropical zodiac signs (Aries, Taurus, Gemini, Cancer, Leo, Virgo, Libra, Scorpio, Sagittarius, Capricorn, Aquarius, Pisces) with essential information: name, symbol, element (fire, earth, air, water), date ranges, and short descriptions. Perfect for zodiac sign lists, horoscope widgets, birth chart calculators, astrology apps, star sign selectors, and zodiac reference tools. Use GET /signs/{id} for complete zodiac sign profiles with personality traits, compatibility, and detailed characteristics.

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• asterwise_get_personality_number

  Asterwise

  Calculates the Personality number from consonants in the full name. All non-vowels (BCDFGHJKLMNPQRSTVWXYZ) contribute — Y is always a consonant. Reduces each name part separately, preserving master numbers 11, 22, 33. SECTION: WHAT THIS TOOL COVERS The Personality number is the outer mask — how others perceive you before they know you well. It governs first impressions, physical presentation, and the social interface between the private self (Soul Urge) and the world. SECTION: WORKFLOW BEFORE: None — standalone. AFTER: asterwise_get_numerology_profile — see all five core numbers together. SECTION: INPUT CONTRACT name — Full legal name as used at birth. Example: 'Arjun Mehta', 'Sofia Rossi' Y is always a consonant — not treated as a vowel. SECTION: OUTPUT CONTRACT data.number (int — Personality number; master numbers 11/22/33 preserved) data.is_master_number (bool) data.karmic_debt_number (int or null) SECTION: RESPONSE FORMAT response_format=json — structured JSON. response_format=markdown — human-readable. Both modes return identical underlying data. SECTION: COMPUTE CLASS FAST_LOOKUP SECTION: ERROR CONTRACT INVALID_PARAMS (local): None. INTERNAL_ERROR: upstream failure → MCP INTERNAL_ERROR SECTION: DO NOT CONFUSE WITH asterwise_get_expression_number — all letters (Expression = Soul Urge + Personality). asterwise_get_soul_urge_number — vowels only.

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• keep_thinking

  Zephex

  Structure multi-step reasoning across iterative tool calls — not a single think tool. Tracks hypotheses, observations, plans, assumptions; detects loops via lastActions and boredLoopDetected. Returns shouldContinue, riskLevel (high/critical blocks continuation), repetitionWarning, reflectionPrompt, approachingLimit. Call when: high-blast-radius edit (auth, billing, schema), debugging after 2+ failed attempts, or task spans 3+ files. DO NOT call when fix is known, single-step typo, repeating without new evidence, or session closed (nextThoughtNeeded:false). Returns projectBrief (stack + memories + key_paths from your repo), stackWarnings, evidenceGate, suggestedRemember on close. Pass area for subsystem scope; projectPath optional (stdio uses cwd). Pass lastActions (2–5 recent tool calls) for loop detection; sessionId to resume; actionReady:true when planning done. Hard cap 10 thoughts/session. Legacy alias: thinking.

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• create_sleepwalker_visibility_run

  Sleepwalker

  Queue a saved AI Visibility run from prompts x platforms or explicit probes. Use this when the user wants reportable probe results, not just prompt ideas. This starts queued work and returns quickly with a run_id; poll get_sleepwalker_visibility_run_status until the run is terminal instead of creating duplicate runs. Platforms accept canonical slugs or common labels: perplexity, openai/ChatGPT, grok, gemini. The response includes credit fields when credits are reserved.

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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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• reflect

  Penfield Memory

  Reflect on recent thoughts and patterns. Analyzes recent activity to identify patterns, topics, and insights. Useful for understanding "what have I been thinking about?" By default, only returns user-created memories (not document chunks). Set include_documents=True to also include chunks from uploaded documents. ⚠️ EXPERIMENTAL: - Importance weighting in results not yet implemented. Importance scores are stored but don't affect ranking. Args: time_window: Time period to analyze ('recent', 'today', 'week', 'month', '1d', '7d', '30d', '90d') include_documents: Whether to include document chunks (default: False, only user memories) start_date: Filter memories created on or after this date (ISO 8601: '2025-01-01' or '2025-01-01T00:00:00Z') end_date: Filter memories created on or before this date (ISO 8601: '2025-01-09' or '2025-01-09T23:59:59Z') ctx: MCP context (automatically provided) Returns: Dict with analysis including top memories, active topics, patterns, insights, and any saved contexts (checkpoints) created in the window. Examples: >>> await reflect("recent") {'success': True, 'memories_analyzed': 50, 'active_topics': [...], 'contexts': [...], ...} >>> await reflect("week", include_documents=True) {'success': True, 'memories_analyzed': 150, ...} # includes document chunks >>> await reflect(start_date="2025-01-01", end_date="2025-01-07") {'success': True, 'memories_analyzed': 25, ...} # memories from first week of January

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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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• get_status_summary

  TensorFeed

  Get the live operational status of every major AI service tracked by TensorFeed (Claude, ChatGPT, Gemini, Perplexity, Cohere, Mistral, HuggingFace, Replicate, Midjourney, etc). Polled every 2 min. Returns operational | degraded | down per service plus the most recent incident.

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• audit_brand_visibility

  Ninar AI

  Run a System of Record adjudication on an entity surfaced by an AI engine (e.g. is 'Banner Life' a valid PMI competitor to Enact?). Uses dual-model consensus (Haiku 4.5 + Gemini Flash, escalating to Sonnet 4.6 + Gemini Pro on disagreement) against a versioned taxonomy. Returns the Why Drawer headline, audit trail, and per-model judgments. Pro plan or higher required.

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• gbif_get_species_classification

  gbif-biodiversity-mcp-server

  Return the complete parent chain for a taxon — from kingdom (or domain) down to the taxon itself — as an ordered array. Each entry has its rank, canonical name, and taxon key. The array is returned root-first (kingdom → phylum → class → … → parent of given taxon). Useful for building taxonomic trees or understanding placement without navigating the backbone level-by-level.

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• vector_quantize

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Simulate int8 or int4 quantization of float32 embedding vectors. Reduces storage by 4x (int8) or 8x (int4). Returns quantized values, scale factor, and precision loss (MSE). Useful for understanding vector DB compression trade-offs.

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• post_human_design_variables

  Human Design MCP Server by RoxyAPI

  Calculate the four Human Design Variable arrows for a birth moment: Determination and Environment on the design side, Perspective and Motivation on the personality side. Each arrow returns its Color, Tone, and Base numbers from the hexagram-line substructure, the left or right direction set by the Tone, and the sourced Color and direction labels. This is the advanced Rave Variables and Primary Health System layer beneath Type, Strategy, Authority, and Profile. Color, Tone, and Base shift with tiny differences in birth time, so each arrow carries a confidence flag that turns false near a Color or Tone boundary, and a precise birth time is essential. Built for Human Design apps offering PHS, diet, environment, and Rave Psychology readings.

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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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• 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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• browser_take_screenshot

  DialogBrain

  Capture a PNG screenshot of the page or a specific element. Returns base64-encoded image bytes AND a file_id (persisted in DialogBrain files storage). Pass file_id straight to messages.send(attachment_file_ids=[file_id]) — do NOT call files.upload again. Use sparingly — favor browser.snapshot for structured DOM understanding.

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• substrate_breakthroughs

  Zambo AI Tools

  Zambo Stack — Fetch the latest AI-generated scientific breakthroughs from SubstrateLayer — a live autonomous research engine running 24/7. 64,000+ total breakthroughs across 6 domains: AI, energy, biology, climate, economics, materials. Returns the 12 most recent discoveries with title, domain, impact score, key insights, and share URL. Free, no auth. Use when you need cutting-edge research signals, cross-domain synthesis, or want to ground a strategy in the latest scientific thinking.

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• list-models

  Roundtable

  List available AI models grouped by thinking level (low/medium/high). Shows default models, credit costs, capabilities for each tier. Use this before consult to understand model options.

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• get_match_telemetry

  Silicon Pantheon

  Read-only. Return server-tracked match statistics for both teams: total tokens consumed, per-turn thinking time, number of tool calls, and turn count. Available during and after a match. Use this for post-game analysis or mid-game cost monitoring. For game-state history (what moves were made) use get_history instead.

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• discover

  XFMS — Model Source

  Show which quality dimensions matter for a stated purpose, WITHOUT ranking any models. Returns the inferred weights and the discovery-walk trace. Useful for understanding how XFMS interprets the purpose before committing to a pick.

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• estimate_llm_cost

  IA-QA — 130+ QA & Dev Tools for AI Agents

  Estimate the API cost in USD for a given model and token counts. Supports all major 2024–2026 models: GPT-4o, GPT-4.1, o3, o4-mini, Claude Opus 4, Claude Sonnet 4/4.5, Gemini 2.5 Pro/Flash, DeepSeek V3/R1, Grok 3, and legacy models.

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