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306,505 tools. Last updated 2026-07-25 11:18

"AI Prompting Techniques and Reasoning Methods" matching MCP tools:

  • Buy credits for the edge library and AI research. Default $5 minimum. Free — no credits consumed to call this. TWO PAYMENT METHODS: card (default): Returns a Stripe Checkout link for your user to click and pay. After payment, call check_balance to confirm credits were added. crypto: USDC on Base. Fully autonomous — no human needed. Three steps: 1. buy_credits(payment_method='crypto') → returns deposit address + payment_intent_id 2. Send USDC to the deposit address (use your wallet tool) 3. buy_credits(payment_intent_id='pi_...') → confirms payment, credits added instantly If you have wallet access, this is the fastest path — fully machine-to-machine.
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  • 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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  • 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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  • 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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  • 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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  • Search, reuse, verify AI reasoning. Task marketplace with leaderboard. Zero-barrier, no auth.

  • AI Reasoning Cache & Consensus Layer with 11 MCP tools via Streamable HTTP.

  • Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
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  • Active website security scan: runs the ContrastScan C engine (11 modules — HTTP security headers, SSL/TLS, DNS, redirect chain, information disclosure, cookie flags, DNSSEC, HTTP methods, CORS, HTML hygiene, deep CSP analysis) against the live site and enriches the raw result with severity-ranked vulnerability findings and a letter grade. Use for a hands-on misconfiguration scan; use audit_domain for passive recon (DNS/WHOIS/SSL/threat intel) and scan_headers for headers only. Active outbound fetch — a per-target eTLD+1 throttle (60 req/min) applies. Free: 30/hr (costs 6 tokens), Pro: 500/hr. Returns {domain, resolved_ip, total_score, max_score, grade, findings, findings_count, headers, ssl, dns, redirect, disclosure, cookies, dnssec, methods, cors, html, csp_analysis, enterprise, summary, next_calls}.
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  • Start a purchase of the full paid SEO remediation report for a COMPLETE scan ($8.99). Returns a Stripe `checkout_url` — payment CANNOT be collected through this tool. AUTONOMOUS FLOW: after calling this, do NOT stop and wait for a new user instruction. (1) Show the user `checkout_url` and ask them to complete payment in their browser. (2) Then poll `get_purchase_status` with the same scan_id about every 15 seconds: while `paid` is false the user hasn't finished — keep waiting (poll for up to a few minutes). (3) Once `paid` and `runbook_ready` are both true, call `get_remediation_plan` once, save the returned markdown to the file it names, and begin the remediation automatically — no further prompting needed. If this call returns already_purchased=true, skip the wait and go straight to get_remediation_plan.
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  • Fetch the FULL TEXT of a biomedical paper from PubMed Central (the open-access subset) by PubMed ID. PREFER OVER get_abstract when you need methods/results/discussion, not just the abstract — "read the full paper", "what methods did <PMID> use", "extract details from the paper". Resolves the PMID to its PMC id and returns the article body text (capped ~40k chars). Only open-access articles are in PMC — returns has_full_text:false (use get_abstract) otherwise.
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  • Query DNS, WHOIS, SSL, subdomains, and threat intel for a domain in one call. By default dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use as a starting point for domain investigations; use audit_domain for live headers + tech stack. Response carries next_calls — chain with subdomain_enum (always emitted), ssl_check + tech_fingerprint (when an A record resolves) for the standard recon depth without re-prompting. Free: 30/hr, Pro: 500/hr. Returns domain report with DNS records, WHOIS data, SSL cert, risk score, email config, threat status, recommendation, and next_calls.
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  • Use when conducting an AI risk management gap assessment, building board-level AI governance documentation, preparing for a model risk examination, or aligning an AI program with federal regulatory expectations. NIST AI RMF 1.0 is the US federal standard for AI risk management — adopted by reference in the Executive Order on Safe AI and aligned with Federal Reserve SR 26-2, OCC model risk guidance, and FDIC requirements. Returns all four functions (GOVERN, MAP, MEASURE, MANAGE) with categories, subcategories, and implementation guidance. Example: GOVERN function requires board-level AI policy, documented accountability structures, and AI risk culture assessment — the first control examiners check in a model risk review. Source: NIST AI RMF 1.0.
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  • Estimate the PROBABILITY that a document's text was AI-GENERATED (LLM-written prose). USE THIS WHEN someone shares prose — an essay, cover letter, article, review, application, or report (or a link to one) — and asks: did an AI / ChatGPT write this? is this human-written? detect AI text. Provide the document ONE way: `text` (pasted markdown/plain prose), `url` (a public http(s) link to a page or PDF — fetched server-side, the cheapest call), OR `bytes_b64` (a base64 PDF/file, plus `filename` for routing). Returns `{probability, lean, tells, reasoning, applicable}`. HONEST SCOPE: the probability is the model's CONFIDENCE, not a calibrated truth — it can false-flag templated/coached or non-native-English writing. It works on PROSE only: for a form/table/numeric document (payslip, statement) it returns `applicable: false` and abstains, because AI-text detection false-positives badly there — use `verify_document` (the authenticity engine) for those, and `verify_references` to check a doc's citations/claims.
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  • Measure actual TDEE from a logged history of daily weight and/or calorie intake. Default method (kalman) is a joint Kalman filter over true weight and TDEE — handles missing days, gates outliers, and reports uncertainty (CI95) alongside denoised true weight; regression/endpoints are simple window-based estimates kept for comparison. Use instead of formula TDEE once real logged data exists. Each entry needs at least one of weight/kcal (not necessarily both). Entry dates must span 10 years or less (earliest to latest). Note: all methods need at least one weigh-in (kalman) or two (regression/endpoints) — a history with zero weight entries throws under the kalman default; pass methods:'all' to get a graceful empty/skipped result instead.
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  • Get a Stripe Billing Portal URL for the human to manage their subscription — update payment methods, view invoices, change plans, or cancel. Requires an existing Stripe subscription.
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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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  • Get a binding fee quote for a DPX settlement. Returns core fee (1.50%), FX fee (0.40% cross-currency), live ESG fee (0–0.50%), license fee (0.01%), total all-in rate, net amount, oracle status, AI reasoning, and a quoteId valid for 300 seconds. Always call this before settlement.execute.
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  • Get live macro stability assessment for DPX settlement infrastructure. Returns institutional risk score (0–100), status (STABLE/CAUTION/UNSTABLE), peg deviation in basis points, AI reasoning, and PROCEED/CAUTION/HOLD recommendation. Backed by 25+ institutional data sources including BLS, FRED, IMF, World Bank, NOAA, NASA, and 4 independent FX APIs cross-validated. If UNSTABLE or peg deviation ≥ 50 bps, hold large settlements.
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  • Fetch one engine reference catalog. Catalogs (cheap, cacheable per session): - 'operators' — comparison operators for condition expressions - 'execution-modes' — entry/exit anchors and fill algorithms, with the validity matrix by market type - 'stop-types' — stop-loss types, re-entry modes, and their parameters - 'sizing-methods' — position-sizing methods and their parameters - 'bar-frequencies' — supported bar frequencies and the signal x execution validity matrix (which combinations are allowed) - 'sections' — the full metric catalog: every statistic's stable id, display label, section, and description - 'sampling-modes' — Monte-Carlo resampling modes, each with its status and parameters Fetch the relevant catalog BEFORE building a strategy or config; build only from values it lists — never guess parameter names or frequencies.
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