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261,972 tools. Last updated 2026-07-05 15:06

"Techniques for Enhancing Memory and Augmenting Cognitive Thinking" matching MCP tools:

  • Look up a MITRE ATLAS case study — a documented real-world AI/ML attack incident. Each case study links a sequence of ATLAS techniques (techniques_used) to the incident. Default response is SLIM (description truncated to 240 chars); pass include='full' for the verbose narrative. Use this after atlas_technique_search to find which incidents have exercised a given technique. Drill into the full techniques_used array via bulk_atlas_technique_lookup in a single call (next_calls emits exactly that hint). Returns 404 when the id is not in the synced catalog. Free: 30/hr, Pro: 500/hr. Returns {case_study_id, name, description, techniques_used, next_calls}.
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  • List the canonical trait vocabulary: 30 trait codes grouped by category (Adaptive Capacity, Cognitive Style, Interpersonal Orientation, Drive Architecture, Integrity & Trust) with a one-line semantic per code, plus the valid discovery contexts and the EU AI Act Art 5(1)(d) workforce gating rules. Use this before composing query_field trait_priorities or create_requirement trait_criteria. Static reference data. Free L0, no authentication required.
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  • Get the full AI analysis for a single exploit by its platform ID. Returns classification (working_poc, trojan, suspicious, scanner, stub, writeup), attack type, complexity, reliability, confidence score, authentication requirements, target software, a summary of what the exploit does, prerequisites, MITRE ATT&CK techniques, deception indicators for trojans, and the standalone backdoor-review verdict with operator-risk notes when available. Use this to check if an exploit is safe before reviewing its code. Example: exploit_id=61514 returns a TROJAN warning with deception indicators.
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  • Look up a MITRE ATLAS technique — the AI/ML adversarial attack catalog. ATLAS catalogues TTPs targeting machine learning systems: prompt injection, model evasion, training data poisoning, model theft, etc. Roughly 80% of ATLAS techniques are AI/ML-specific (no ATT&CK bridge); 20% mirror an enterprise ATT&CK technique via attack_reference_id — use that to pivot to D3FEND defenses (d3fend_defense_for_attack) and CVE search. Sub-techniques inherit `tactics` from the parent (inherited_tactics=true flag) when ATLAS upstream leaves them empty. Use this tool when the user asks about AI/ML threats, LLM red-teaming, or adversarial ML; for multiple techniques in one call (e.g. drilling into a case study's techniques_used), prefer bulk_atlas_technique_lookup. Returns 404 when the id is not in the synced ATLAS catalog. Free: 30/hr, Pro: 500/hr. Returns {technique_id, name, description, tactics, inherited_tactics, maturity (demonstrated|feasible|realized), attack_reference_id, attack_reference_url, subtechnique_of, created_date, modified_date, next_calls}.
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  • Search the MITRE D3FEND catalog of defensive techniques by keyword, tactic, or targeted artifact. Default response is SLIM (drops `uri` from each row — saves ~60 chars/row, ~30% on popular drills); pass include='full' for the verbose record. Pass exclude_id when chaining from d3fend_defense_lookup to skip self in sibling-artifact searches. Use to discover defenses applicable to a given threat model — e.g. 'what defenses harden access tokens?' (tactic=Harden + artifact='Access Token'). Drill into d3fend_defense_lookup with any returned defense_id for the ATT&CK technique mappings. Free: 30/hr, Pro: 500/hr. Returns {query, total, results [{defense_id, label, uri (only when include=full), parent_label, tactic, artifact}], next_calls}.
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  • Bulk ATLAS technique lookup — retrieve full records for up to 50 techniques in a single request instead of N separate atlas_technique_lookup calls. Designed as the natural follow-up to atlas_case_study_lookup, whose techniques_used array can be passed directly. Each item is the same shape as atlas_technique_lookup, including parent-tactics inheritance for sub-techniques (inherited_tactics=true flag) and per-item next_calls (D3FEND bridge when attack_reference_id present, sibling-technique search by tactic, parent lookup for sub-techniques). Free: 30/hr (1 per item), Pro: 500/hr. Returns {results [{technique_id, status (ok|not_found|invalid_format), technique, error}], total, successful, failed, partial, summary}.
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Matching MCP Servers

Matching MCP Connectors

  • Find relevant Smart‑Thinking memories fast. Fetch full entries by ID to get complete context. Spee…

  • Cultural color and colour intelligence API. Every colour anchored to a named person, a documented year, and a consequence. 34 archives spanning literary, cultural, pigment, and national traditions. Ask it what color could get you executed in the Ottoman Empire.

  • Given a passage of text (essay, note, message, snippet, transcript), returns ~5 humans whose intellectual fingerprint matches it — recurring themes, mental models, archetypal stance, blind spots. Use when the principal asks for sparring partners, intellectual peers, "who else is wrestling with this," "who thinks like X," or "find me writers similar to this passage." Each result returns a name, three-word archetype, one-line summary, dominant themes, and a profile URL the principal can visit. The match runs over Voyage 3.5-lite text embeddings reranked by a proprietary 12-dimensional cognitive-style vector — so results align by *how* a mind reasons, not just topical overlap.
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  • Given a rack (the module ids the user owns), return which canonical patch techniques the rack can realize, and which it is one module away from. The set-level companion to find_role_realizations: where that answers "which module fills role R in technique T?", this answers the rack owner's actual question — "given everything I own, what can I actually do, and what am I close to?". This is the right tool the moment a user gives you their modules and asks an open "what can I do / what can this rack do / what am I missing?" question — instead of guessing techniques from training priors or calling find_role_realizations technique-by-technique by hand. It runs the affordance match across the whole technique catalog for you. Returns two buckets: - reachable: every required role has a rack module that fills it. Each carries an `assignment` (role → module). `requires_shared_module: true` flags a technique only reachable by reusing one module for two roles — verify those roles can share one instance. - near_misses: all-but-one role fillable; `missing_roles` names the unfilled role(s) and the `required_affordances` you'd need. This is the acquisition signal — "you can already do X; you're one <affordance> module away from Y". Args: - rack (string[], required): module ids, e.g. ["make-noise/maths", "mutable-instruments/plaits"]. Max 64. Ids that match no module are returned in `unresolved` (with did-you-mean), not silently dropped. - limit (number): max techniques per bucket. Default 25, max 100. Stateless-rack contract: the server keeps no memory of your rack between calls — pass the COMPLETE current rack every call. A partial rack silently narrows what's reported reachable, so if a module id doesn't resolve, surface the `unresolved` did-you-mean to the user rather than proceeding on the incomplete set. Scope: reachability is role-PRESENCE based. It does NOT verify per-role instance counts (cardinality) — a technique needing two independent envelopes is judged reachable if you have one envelope source. The distinct-instance question (can one module fill two roles?) is surfaced as `requires_shared_module`, not silently assumed. For the editorial detail on a specific technique (canonical instance, counter-canonical notes, full realization list), call list_techniques; for one role's candidates, find_role_realizations. To go the other way — which of your modules are redundant / safe to sell — call rack_redundancy.
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  • Return canonical synthesis / patching techniques with role-keyed module realizations drawn from the corpus. Use this when the user asks "how do I do X?" with X being a recognisable technique (low-pass-gate plucks, pinged-filter percussion, parallel multiband processing, complex-oscillator FM, karplus-strong pluck, clocked-delay feedback, modal-resonator excitation, wavefolder harmonics, envelope-follower ducking, Maths-style function-generator omnibus). It's also the right tool when the user has a module and asks "what's this good for?" — pass filter.module_id to retrieve every technique that references the module via its role_realizations. Each technique declares role_definitions (the roles the technique uses, each with required and optional affordances) and role_realizations (concrete modules that fill each role, with the affordances they provide). The model substitutes modules from the user's rack into roles by affordance match — DO NOT treat the realization list as exhaustive or as a recipe. Args: - filter (optional): { capability?, module_id?, text? } - capability: kebab-case capability id (see search_modules _meta.taxonomy). Returns techniques whose required *or* optional capability list includes this id. - module_id: "<manufacturer>/<module-slug>". Returns techniques that have a role_realization referencing this module. - text: free-text phrase. Substring-matches against technique id/label/description AND a curated alias table (technique_aliases) — that's the right surface when a user types evocative prose like "stuttering delay", "plucked string", "source of uncertainty" that doesn't grep against any kebab-case id. Two-way alias match: long alias ("source of uncertainty") matches short query ("uncertainty"), and vice versa. - When multiple filters supplied, AND-intersects. - Omit filter entirely to list all techniques. Returns: { "techniques": [ { "id": "low-pass-gate-pluck", "label": "Low-Pass Gate Pluck", "description": "Send a short envelope...", "required_capabilities": ["lowpass-gate"], "optional_capabilities": ["envelope-generator", "function-generator"], "role_definitions": [ { "role_id": "lpg", "description": "The vactrol-based or vactrol-emulating element. Strictly required...", "required_affordances": ["lowpass-gate"], "optional_affordances": [] }, ... ], "role_realizations": [ { "role_id": "lpg", "module_id": "make-noise/optomix", "affordances_provided": ["lowpass-gate"], "notes": "Two-channel vactrol-based LPG..." }, ... ], "canonical_instance": { "rationale": "...", "lineage": [ { "position": 1, "label": "Buchla 292 (1970)", "module_id": null, "notes": "..." }, { "position": 2, "label": "Tiptop Audio Buchla 292t", "module_id": "tiptop-audio/buchla-292t" }, ... ] }, "counter_canonical_notes": [ { "claim_pushed_back_against": "Optomix is the canonical pairing with Plaits...", "evidence": "The corpus catalogs 19 LPG-capable modules..." } ], "coverage": [ { "role_id": "voice", "realizations_count": 3 }, { "role_id": "lpg", "realizations_count": 19 }, { "role_id": "env", "realizations_count": 6 }, { "role_id": "clock", "realizations_count": 2 } ] } ], "_meta": { "filter": {...}, "feedback_hint"?: string } } How to use role data: - role_realizations are CURATORIAL SAMPLES, not exhaustive lists. The coverage[].realizations_count tells you how many are documented; other modules may fill the same role. - To find modules in the user's rack that can fill a role, use find_role_realizations(technique_id, role_id, available_modules). - canonical_instance is opt-in and sparse. Most techniques don't have one; that absence is information. When present, it documents a documented historical lineage (e.g., Buchla 292 → 292t → MMG → Optomix for low-pass-gate-pluck) — NOT a prescription. - counter_canonical_notes push back on likely training-data priors. When the user invokes a canonical-sounding claim that has a counter_canonical_note, surface the pushback. Errors: - "Module not found: <id>" if filter.module_id is supplied and unknown. - Empty techniques[] with a feedback_hint when filters produce no matches — call report_gap if the user expected coverage.
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  • [ChatGPT Connector compat] Fetch memory by ID. Exists to satisfy ChatGPT Deep Research's required `search`/`fetch` tool contract. Native MCP clients should fetch via `recall` + memory_id, or use the API's GET /memories/{id} endpoint directly. Returns a single memory with citation support (id, title, url, text fields). Args: id: Memory UUID to fetch ctx: MCP context Returns: Dict with id, title, url, text, metadata fields
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  • Create an alert rule to monitor CPU, memory, or disk usage. When the metric crosses the threshold, a notification is sent via email and/or webhook. Max 10 rules per site. Requires: API key with write scope. Args: slug: Site identifier metric: "cpu", "memory", or "disk" (percentage-based) threshold: Threshold value 0-100 (e.g. 90 for 90%) operator: "gt" (greater than) or "lt" (less than). Default: "gt" severity: "warning" or "critical". Default: "warning" cooldown_minutes: Min minutes between repeated alerts. Default: 30 notify_email: Send email notification. Default: true notify_webhook: Optional webhook URL for POST notifications Returns: {"id": "uuid", "metric": "disk", "threshold": 90, ...}
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  • Capture a Texas homeowner's interest in rooftop solar and route to a licensed installer — use when the user owns (or is buying) a Texas home and mentions solar panels, solar quotes, solar savings, or reducing their bill through solar. Use when the user says 'I just bought a house in Austin and want solar quotes', 'how much could solar save on my Houston electric bill', or 'connect me with a solar installer for my new home'. Returns a lead ID and confirms next steps; Utilify routes the lead to installer partners (SunPower, Sunrun, Palmetto, and independent TX installers). Caveats: (1) only call when the user has explicitly opted in and confirmed homeownership — this is not for renters, and Utilify may earn a referral fee. (2) Texas-only — for non-TX addresses, decline and explain. (3) Don't double-call for the same address in one conversation; one lead per opt-in. If the user has only expressed mild curiosity ('I'm thinking about solar someday'), answer the question first and only call this tool once they confirm 'yes, connect me'.
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  • Save a cognitive checkpoint for handoff to another agent or your future self. The `description` is the primary cognitive payload — its narrative is what lets another agent resume the work. The server also runs hybrid search on the description and attaches the most relevant memories to the checkpoint. Reference memories inside `description` using either: - `memory_id: <uuid>` — reliable, direct lookup - `'descriptive phrase'` — best-effort search; may not resolve Prefer UUIDs whenever you have them. The response reports `references_resolved` + `unresolved_references` so you can retry. For the full hygiene guide (what to include, how to organize, when to checkpoint, example shapes), invoke the `checkpoint_protocol` MCP prompt. Args: name: Unique identifier for this checkpoint (used by restore_context). description: Narrative handoff with optional memory references. ctx: MCP context (automatically provided). Returns: Dict with success status, context_id, memories_included, and (when references were extracted) references_resolved + unresolved_references.
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  • 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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  • Return the dossier projection for a city, in the requested cognitive lens. Defaults to the synthesis projection (the multidimensional view that holds all lenses in superposition and names the dialectics). Pass a single-lens value to get the focused cognitive position — useful when the agent is acting on behalf of a user with a specific stake (developer underwriting, investor thesis, broker client argument, attorney precedent search, resident orientation, civic-leader regional coordination).
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  • Read the full body and metadata for one Pathrule memory. Use this after pathrule_get_context, pathrule_goto, or pathrule_list_memories returns a memory_id. This reads cloud data only and does not inspect the user's local filesystem.
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  • Look up a MITRE ATT&CK technique by ID or keyword for authorized penetration testing and security research. Returns the full technique record: name, associated tactics, description, detection opportunities (log sources, behavioral indicators), real-world procedure examples from public reporting, recommended mitigations, and related sub-techniques. The detection and mitigation sections make this equally useful for defenders building detection coverage. Accepts exact IDs (T1190, T1059.001) or keyword search (e.g., "sql injection", "pass the hash", "web shell upload").
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  • Given a profile of the authorized test target (technology stack, exposed services, authentication type, OS), return a ranked list of ATT&CK techniques and OWASP test cases most relevant to that profile — not a generic dump of all techniques. Ranking factors: platform match, service match, auth type exposure, technique prevalence. Each result includes why it is relevant to this specific profile, the detection opportunity, and the recommended mitigation. Use when starting an authorized engagement to prioritize the testing scope; pair with pentest_guide to get the full methodology for each top-ranked vector.
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  • ALWAYS call this first when a user connects or asks what this is. Returns a short orientation for StudioMeyer Academy — a free 6-level 'Memory-First AI Operator' curriculum (Levels 1-3 fundamentals, 4-6 memory/MCP/multi-agent), plus playbooks and build recipes. Read it back to the user in their language and offer to start at their level.
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  • Returns NeuroRank's public, aggregate cognitive-combine statistics across all completed combine runs: total runs, estimated trials, game titles and countries represented, median run age, and test-retest reliability. Read-only, no authentication, aggregate (non-personal) data only.
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