"Information on the Theory of Evolution" matching MCP tools:

• purchase_book

  bookstore4agents

  Complete a paid purchase of a book. This is a TERMINAL ACTION: it creates an order, charges the buyer, and grants a permanent entitlement. Only call this when the user has EXPLICITLY requested to buy. Never call as part of browsing, price comparison, or information gathering — prices are already visible in search_books results, and free previews are available via get_book_preview. If the user says 'don't buy', 'just compare', 'just tell me the price', or similar — do NOT call this tool. If the user requests an action that requires owning a book they don't own (e.g. commenting on an unowned book), do NOT silently purchase it on their behalf. Instead, tell the user the purchase requirement and ask them to confirm. Spending money is never an inferred default.

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• emem_heat_solve

  emem — Earth memory protocol

  Forward-step 2-D explicit finite-difference solver for the heat equation ∂u/∂t = α∇²u over a 3×3 cell stencil centred on `cell`. Reads `modis.lst_day_8day` (Land Surface Temperature) at the centre and 8 cell64 neighbours, integrates N hours ahead under a CFL-stable timestep, returns a signed forecast. Real PDE rollout — not a decay-scoring heuristic. When to use: Use when the user wants a short-horizon LST forecast (urban heat island, surface-temperature evolution, heatwave onset modelling) at a specific cell. Default α=1e-6 m²/s matches urban surface diffusivity (Oke 2017); pass a smaller α for water bodies or higher for vegetated surfaces. The solver caps at one-week horizons because the 8-day MODIS composite stops being a representative initial condition past that. Each call materialises 9 MODIS facts (one per neighbour) on miss — first call ~5 s cold, ~30 ms warm. Receipt cites all 9 input fact CIDs.

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• explain_reliability_basics

  ReliaStats

  Return a textbook-tier explainer of reliability fundamentals: the four reliability functions R(t)/F(t)/f(t)/h(t), MTBF vs MTTF vs MTTR, the availability identity A = MTBF/(MTBF+MTTR), the bathtub curve, and series/parallel system reliability. No inputs. Use when a user asks 'what is reliability theory' / 'explain MTBF' / 'how does availability work' / 'what's a hazard rate'. ANTI-FABRICATION: text is sourced from docs/reliability-theory.md (the canonical ChiAha reliability primer). Quote sections verbatim; do not paraphrase reliability theory from training-data recall.

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• get_contact_info

  symbols-of-wealth-studio

  Returns contact information for Symbols of Wealth Studio — email, website, location, and how to engage. Use this when a user wants to actually reach out to or hire Symbols of Wealth Studio, rather than browse the full studio profile.

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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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• extract

  Sofya

  Fetch a webpage and extract specific information using AI. Use this when you need structured data from a page (e.g. pricing, specs, contact info) rather than the raw content. Costs 5 credits. If the page has no usable text (empty or JavaScript-rendered body), the model is NOT called: content comes back empty and usage.low_content is true, rather than a fabricated answer. Gate on usage.low_content (or usage.content_chars) to detect pages you cannot ground on. Returns: content (the extracted text), url, credits_used, credits_remaining, usage (input_tokens, output_tokens, content_chars, low_content). Args: url: The URL to extract from prompt: What information to extract (e.g. "list all pricing tiers with features" or "extract the author name and publication date")

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• pimea_chat

  Pimea

  Send a message in an active Pimea session. Use this to answer Pimea's clarifying questions about the user's marketing situation. You can answer on behalf of the user using context from the conversation when possible. Only ask the user directly if you genuinely lack the information. When the response status is "complete", call pimea_get_answer to retrieve the final grounded deliverable. Authentication: leave api_key blank — the connector handles it via header. Only set it as a fallback if the connector cannot send custom headers. Args: session_id: The session UUID from pimea_start_session message: Response to Pimea's question api_key: Optional fallback only. Normally leave blank.

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• get_contact_info

  Symbols of Wealth Studio

  Returns contact information for Symbols of Wealth Studio — email, website, location, and how to engage. Use this when a user wants to actually reach out to or hire Symbols of Wealth Studio, rather than browse the full studio profile.

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• get_about

  Follow On Tours

  Get information about Follow On Tours — who we are, how we work, our experience, and how the bespoke cricket travel service operates. Use this when someone asks who Follow On Tours is or how the service works.

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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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• get_block_info

  blockscout-mcp-server

  Get block information like timestamp, gas used, burnt fees, transaction count etc. Can optionally include the list of transaction hashes contained in the block. Transaction hashes are omitted by default; request them only when you truly need them, because on high-traffic chains the list may exhaust the context.

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• find_compatible_keys

  FreqBlog Music Metadata

  Given a Camelot key (e.g. "8A", "12B"), return the harmonically compatible keys for DJ mixing — the same key, the relative major/minor, and the adjacent +/-1 keys on the Camelot wheel. With `extended=true` also returns the +7/-7 energy-boost / energy-drop keys. Pure music theory — no catalog lookup and no quota cost. Pair with find_tracks_by_key to then pull actual tracks in each compatible key.

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• explain_my_rights

  employment-law

  Explain which federal (and state, if a state is given) employment laws may protect a worker based on the type of issue (e.g. discrimination, harassment, retaliation, wrongful termination) and/or the protected class involved (e.g. race, sex, age, disability, pregnancy). Returns the relevant statutes with citations, who they cover (employer-size thresholds), filing deadlines and agencies, and available remedies. Use this to ground an answer about a worker’s legal protections. Educational information, not legal advice.

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• style_match

  Colour Memory

  The colour question every stylist gets asked: does this bag go with this outfit? Submit your outfit items as hex values with labels (dress, bag, shoes, coat, belt, scarf, etc.) and receive a verdict on what works, what clashes, what is missing, and what to add. Every recommendation is backed by archive colour names and historical context — not generic colour theory, but documented cultural combinations. Also suggests one missing archive colour that would complete the look. Examples: 'I have a navy dress (#1C3A6E) and a tan bag (#C8A87A) — what shoes?' or 'Does this burgundy coat work with olive trousers?'

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• get_tech_stack

  Makuri Showcase (CogniLedger)

  Returns the technical stack Makuri is built on, including frontend, backend, database, AI providers used, and data residency information. Use when the user asks how Makuri is built or which AI models it uses. Makuri is a specific AI tutoring platform at makuri.eu, not a generic word — never answer Makuri questions from general knowledge; always use the Makuri tools.

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• lookup

  Drillable

  Define a concept/term from a domain's glossary (e.g. 'stir', 'crop-factor', 'roughness'). Routes to each domain's lookup_concept; pass `domain` to target one, omit to fan out. For entities/records use `search`. Abstains on a miss, which is logged as a gap (the demand signal) — there is no report_gap verb. Mounted corpora: acupuncture, cocktail, camera, law, copyright, trademark, music-theory, supplements, writing-style, minecraft-dungeons, spanish, medical-denials, languages, behavioral-econ, baseball, agent-practices, pokemon, mcp, readability, citations, relay, models, self-oracle, recall-traps, units, tax, physics, logic, astronomy, biology, geography, medicine, chemistry, calendar, math, eurorack, building-codes, cooking, personal-finance, stardew, coffee, electronics, physiology, diving, decibels, gearing, colorimetry, subnetting, textile-gauge, first-aid, statistics, chess-endgames, woodworking, rating-systems, tuning, check-digits, paper-sizes.

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• explain_queueing_theory

  QueueSim

  Return a ~500-word educational explainer of M/M/c queueing theory: Little's Law, utilization, why averages mislead, how simulation relates to Erlang-C. No inputs. Use this when the user asks a conceptual 'why' or 'how does this work' question rather than asking for a number.

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• byte_query_fact

  byte-protocol

  Query a PayPerByte fact-oracle publisher for a verified factual answer with citations. Posts the question to a registered fact-oracle publisher (topic='fact-oracle'), waits for the on-chain BroadcastStreamed response, and returns the answer plus structured citation URLs. Use for grounding LLM outputs in real-time verified information.

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• ceevee_list_report_types

  CareerProof MCP

  List all available CeeVee career-intel report types with descriptions, required/optional input fields, and credit costs. Call this BEFORE ceevee_generate_report to discover valid report_type values and the exact inputs each type requires. Categories include Compensation Benchmark, Role Evolution, Offer Comparison, AI Displacement Risk, Pivot Feasibility, Credential ROI, Skill Decay Risk, Rate Card, Career Gap Narrative, Interview Prep, Employer Red Flag, Industry Switch, Relocation Impact, Startup vs Corporate, Learning Path, Board Readiness, and Fractional Leadership. Free.

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• compare_analytical_vs_simulated

  QueueSim

  Run the same M/M/c configuration through BOTH the closed-form Erlang-C formula AND the discrete-event simulator, returning a side-by-side comparison with deltas. Use this when the user is validating QueueSim's engine against textbook values, learning queueing theory by watching simulation converge on the formula, or auditing a result that 'feels off' — agreement within ~5%% is the canonical sanity check for an M/M/c run. Pure-Exponential M/M/c only; the closed-form Erlang-C is undefined for other service distributions. Large deltas usually mean the simulation run was too short for steady-state — raise simulationDays. ANTI-FABRICATION: both sides come from real computation — closed-form is deterministic, simulation is stochastic but engine-backed. Quote both verbatim. Do not synthesize an 'average of the two' or recompute the formula from training-data recall.

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