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442,443 tools. Updated 2026-08-11 09:19

"A server to improve math capability of AI" matching MCP tools:

  • Executes a Strale capability by slug and returns the result. Use this when you need to perform any verification, validation, lookup, or data extraction from the 271-capability registry. Call strale_search first to find the right slug and required input fields. Returns a result object with the capability output, quality score (SQS), latency, price charged, and data provenance. Five free capabilities work without an API key (10/day limit). Paid capabilities debit from the wallet — check strale_balance first for high-value calls.
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  • Business-day date math. Three modes inferred from params: add (start+days -> the date N business days away, negative days = before), diff (start+end -> count of business days between, inclusive of both endpoints), is (date -> is it a business day). Optional weekend (default sat,sun) and holidays (comma list of YYYY-MM-DD you supply). No signup, no API key. Pure UTC date-only math; no time-of-day/DST/timezone.
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  • Get Lenny Zeltser's IR one-page executive brief template. Standalone variant of `ir_get_template` for callers that only want the brief without the long-form report. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `cti_load_context`. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Vuln one-page executive brief template. Standalone variant of `vuln_get_template` for callers that only want the brief without the long-form report. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's Security Assessment one-page executive brief template. Standalone variant of `assessment_get_template` for callers that only want the brief without the long-form report. This server never requests your assessment notes or report and instructs your AI to keep them local—the templates and guidelines flow to your AI for local analysis.
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  • Return a compact roster of every capability with at least one enabled provider, grouped by category, with the best current conformance per capability. Use this as a self-introspection step: call once at the start of a task to know what is and isn't available, before deciding whether to attempt or to tell the user 'this isn't possible here'.
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  • Explain how HelloBooks and Munimji (the in-app AI assistant) help a specific business — given a free-text description of the user's own operations. Returns a curated capability knowledge base: business-operation areas (sales, purchases, banking, tax, reports, inventory, payroll, multi-entity, setup), and for each AI capability WHO does the work — `autonomous` (Munimji does it on its own, e.g. OCR extraction, running reports), `approval` (Munimji prepares the entry and you one-click approve before it posts to the ledger, e.g. AI categorization, find-and-match, creating invoices/bills by chat), `assist` (co-pilot, e.g. guided onboarding, voice), or `manual` (a software feature you run yourself). Each capability links to the backing software features. Use this when a user describes their business and asks "how can HelloBooks help me?", "what can the AI do for my shop/practice/agency?", or "what can Munimji do on its own vs what do I approve?". Pass their description in `businessDescription`; optionally filter by `area` or `autonomy`. The AI never posts to a ledger without approval. For the full software catalog call list_features; for pricing call list_plans.
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  • Show what the user (or their AI assistants) has recently done in ExpenseBot via this MCP server: which tools were called, when, with what arguments, and whether they succeeded. This is a log of assistant TOOL CALLS, not the processing history of a document. Useful for questions like 'what did I do this week' or 'which tools has my assistant run', and to give the user transparency into AI-assisted actions. Returns the most recent N entries from the audit log (default 20, max 100).
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  • Describe any served capability by name — the generic twin of the named describe tools. Pass `capability` as either a capability id from list_capabilities_v1 (e.g. "power.capacity") or a query primitive name (e.g. "query_power_capacity_v1"). Returns the same schema payload as the named describe tool: valid filters, groupings, metrics, detail fields, and citation fields. Use the generic pair (this + query_capability_v1) when list_capabilities_v1 names a capability that has no named tool in your client's tool list — clients cache tool lists, and capabilities shipped after that cache are still fully reachable here.
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  • Search the corpus for Eurorack modules matching a combination of filters. Filters compose with AND. Omit any filter to leave that dimension unrestricted. The result is sorted by module name; pagination metadata in the response envelope lets you page through long result sets. Args: - capability (string): capability id, e.g. 'envelope-generator', 'clock-source'. Run a search with NO capability filter to get the full capability taxonomy (ids + labels + counts) in _meta.taxonomy. Retired/variant slugs resolve via the capability_aliases layer (e.g. 'low-pass-gate' → 'lowpass-gate', 'quantiser' → 'quantizer'), so either form is accepted. - manufacturer (string): manufacturer id, e.g. 'make-noise', 'mutable-instruments'. - hp_min, hp_max (number): module width in HP. hp_max=10 finds modules ≤ 10 HP. - signal_type_in (string): the module accepts a jack of this signal type as input. One of audio, cv, gate, trigger, clock, mixed. signal_type_in='audio' and ='cv' both also match jacks tagged 'mixed' (the schema's value for jacks the source describes as accepting both audio and CV — e.g. Joranalogue Compare 2's signal inputs); the other values match literally. - signal_type_out (string): the module produces a jack of this signal type as output. Same 'mixed'-superset semantics as signal_type_in. - text (string): free-text match against module id, name, slug, description, and the ids/labels/descriptions of capabilities the module has (case-insensitive substring). Matches hyphenated forms like "filter-8" against the slug/id even when the display name uses a space ("Filter 8"), and is whitespace-insensitive on id/slug/name so "3x MIA" finds the module named "3xMIA". Capability-label coverage means text="multiband" finds modules tagged multiband-filter without knowing the kebab-case id, and a curated alias layer extends that to common word-form variants ("multi-output" / "multi-band" / "band-split" → multiband-filter, "low-pass" → lowpass-filter, retired ids like "voltage-controlled-filter" → vcf). Truly novel wording still requires the _meta.taxonomy overview (run a no-capability search); if you expected a hit and got 0, call report_gap so the alias can be added. - voct_tracking_range_min (number): the module has a V/Oct input whose source-stated tracking range is at least this many octaves. Use for "filters that track 5+ octaves" / "oscillators with wide V/Oct range". - voct_tracking_quality (string): the module has a V/Oct input with this tracking quality, one of 'calibrated', 'temperature-compensated', 'approximate', 'uncalibrated'. 'temperature-compensated' is the strongest claim. - voct_temperature_compensated (boolean): the module has a V/Oct input whose source explicitly states temperature compensation. Implies calibrated but separately flagged because some manuals call out only one. - audio_outputs_min (number): the module has at least this many output jacks with signal_type='audio'. Use for "multi-output filters" (≥3 audio outs surfaces LP/BP/HP-tap VCFs like Three Sisters, QPAS, A-108, Polaris) or any multi-tap audio module. Combine with capability='vcf' for the canonical multi-output-filter query. - limit (number): default 50, max 200. - offset (number): pagination offset. Returns: { "modules": [{ id, name, manufacturer, hp, capabilities: [string], description, production_status }], "total": number, // total matches (across all pages) "_meta": { "query": <args>, // Present whenever a 'capability' filter matched >=1 module (NOT gated on // total=0 — it accompanies normal results). The category-coverage // denominator, so a "best X" recommendation can self-caveat instead of // reading as "best available": // On a no-capability search: the global capability taxonomy (id, label, // description, module_count) — discover the controlled vocabulary here // instead of a separate list_capabilities call. "taxonomy": [{ "id": "lowpass-gate", "label": "Low-pass gate", "module_count": 19 }], "coverage": { "capability": "stereo-mixer", // the capability you filtered on "category_total": 9, // modules in the corpus with this capability, IGNORING your other filters "corpus_total": 388, // all modules in the corpus "note": "...best of 9 in the corpus, not best available..." // ready-to-use recommendation caveat }, // Present when the server's token-AND fallback rescued an otherwise-empty // phrase query (e.g. "pamela workout" → "Pamela's NEW Workout" via per-word // identifier match). Not an error; just signals that results came from the // relaxed pass rather than the literal phrase. "relaxed_to_tokens": true, // On total=0 (after the token-AND fallback has already been attempted), the // server adds these diagnostic hints so you can retry productively in one // turn instead of guessing variants. Each is independently optional: "would_match_without": ["capability", "text"], // filters that, if individually dropped, would yield ≥1 result — the named filter(s) cost you the match "closest_text_hits": [{ id, name, manufacturer }], // top 3 modules matching 'text' alone (other filters dropped); inspect for a close hit you filtered out by accident "did_you_mean": [{ id, name, manufacturer }], // top 3 edit-distance neighbors of 'text' when it matched nothing literally (a single-token typo like "multgrain" → multigrain); PRESENT means retry with the suggested id, ABSENT means the term is a genuine corpus gap (call report_gap) — the discriminator would_match_without can't give you "capability_suggestions": [{ id, label }], // top 3 valid capabilities matching the 'capability' arg you passed (only set when the arg wasn't a known slug or alias) — use list_capabilities for the full taxonomy "manufacturer_suggestions": [{ id, name }], // top 3 maker slugs matching the 'manufacturer' arg (only set when it wasn't a canonical slug) — the manufacturer arg is EXACT-match, so e.g. "addac" → "addac-system", "nonlinearcircuits" → "nlc"; retry with the suggested id "feedback_hint": "..." // fallback prompt to call report_gap when no other diagnostic applies } } Examples: - "What envelope generators under 8 HP exist?" → {capability: 'envelope-generator', hp_max: 8} - "What ALM modules are in the corpus?" → {manufacturer: 'alm-busy-circuits'} - "What clock sources are there?" → {signal_type_out: 'clock'} - "Modules with 'workout' in the name" → {text: 'workout'} - "Filters that track V/Oct over 5 octaves" → {capability: 'vcf', voct_tracking_range_min: 5} - "Temperature-compensated filter cores" → {voct_tracking_quality: 'temperature-compensated'} - "Multi-output filters with LP/BP/HP taps" → {capability: 'vcf', audio_outputs_min: 3} Errors: - Returns an empty modules array (and total=0) if nothing matches. Not an error — inspect _meta.would_match_without / closest_text_hits / capability_suggestions / manufacturer_suggestions to decide whether to broaden the query or call report_gap. - Invalid filter values pass through to the WHERE clause; if no module satisfies them you get total=0. After picking a hit, call get_module with the id for full details.
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  • Return AI-assistant (ChatGPT/Claude/Perplexity/Gemini/Copilot) traffic for the given period. mode='referred' (default) lists landing pages that received clicked AI traffic — per page × AI source: sessions, bounce rate (%, always computed; judge reliability via the sessions count), summed revenue, and last citation date (default limit 100); a view GA4/GSC cannot produce (GSC is Google-search only; GA4 lacks an AI-source breakdown). mode='gaps' returns where the site leaves AI value on the table as a ranked action list: (1) missed_citation_pages — content articles with real audience but ~0 AI traffic (push for AI citation / GEO), ranked by engagement-weighted reach; (2) under_monetized_ai_pages — pages WITH AI traffic engaging below the site's own AI norm (improve landing/CTA), ranked by AI arrivals lost below benchmark (default limit 10/list); methodology fixed in code. site_id is OPTIONAL when OAuth-authenticated. Default period is the last 30 days; pass period='today'/'7d'/'90d' or a raw day count (1-365). Scope is clicked citations only.
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  • Tailor a resume to a SPECIFIC job — TWO steps. STEP 1 (default; action omitted or 'prepare'): the server returns the job's full JD, its must-have skills/requirements, and the candidate's current resume, plus tailoring instructions. YOU (the model) then WRITE the tailored resume as JSON Resume, following the instructions — weave JD keywords into existing bullets only where the candidate genuinely has the experience, never fabricate experience/titles/dates/employers, keep all dates and company names, and flag any keyword you couldn't honestly add. STEP 2: call this tool again with action:'save', tailored_resume:<your JSON Resume>, and job_id — the server renders a PDF and saves it to the candidate's Workopia dashboard (requires sign-in). Use whenever the user references a specific job to tailor for: 'tailor for #1', 'for Morgan Stanley', 'tailor my resume for this role: <JD>'. Resolving job_id (same rules as job_detail_tool): from the most recent prior search/refine result — (a) numeric/ordinal → the Nth job; (b) company name → Company-field match; (c) role/title phrase → Job-Title match — then pass that job's **Job Id** value VERBATIM. Do NOT use placeholders like 'JOB_1' or '#1'. For STEP 1 supply ONE of job_id (preferred — server fetches the JD from Mongo) OR job_description, plus the candidate's resume via resume_text / resume_content / resume_data. For general 'improve my resume' (no specific job), do NOT call this tool — call resume_tool action=improve instead. Note: the tailored resume is written by your AI client's own model — the assistant you are already using — so it works out of the box with nothing to configure; Workopia runs no LLM of its own and never charges for the AI.
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  • Purpose: Evidence that OneQAZ detects price moves BEFORE news publication. Returns leading_score, avg_lead_time_minutes, and accuracy_pct per event type. Strongest Trust Layer A evidence (Layer A = anticipation-capability tier of OneQAZ's 5-layer trust pyramid) — proves the system is anticipatory rather than reactive. Triggers (casual questions too): "can you predict news?", "뉴스 나오기 전에 감지해?", "how early do you catch moves?", "뉴스보다 빨라?", "do prices move before headlines?". When to call: when an AI is evaluating predictive capability. Prerequisites: none. Next steps: get_news_causality_breakdown for the 3-type classification. Caveats: empty when no news events processed in the recent window. Args: market_id: Market identifier (crypto, kr_stock, us_stock, etc.) target_market: Alias for market_id (backward compat) min_sample_count: Minimum sample count for statistical significance (default 3) Disclaimer: Information only, not investment advice.
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  • Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. No API key required.
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  • On-demand independent SAFETY scan of an MCP server — call this BEFORE installing or connecting to one. Give it an HTTP(S) MCP endpoint URL (scanned live in seconds), or an npm/PyPI package name or GitHub repo (queued for an isolated sandbox scan — local stdio servers execute code, so Hlido never runs them inline). Returns the safety tier (SAFE/CAUTION/RISKY/DANGEROUS), tool-poisoning detection (the malice signal), dangerous-capability red-flags (shell/code-eval/fs-write/egress/secrets) with per-tool evidence, and auth posture. Tier = blast radius if hijacked, not maintainer trustworthiness. A server Hlido hasn't scanned returns not_scanned — never assumed safe. Register of already-scanned servers: https://hlido.eu/mcp/
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  • Get Lenny Zeltser's cybersecurity-writing rating sheet(s) so your AI can apply the rubric. Returns the structured rubric (groups, items, scoring bands) WITHOUT computing a score. Use `rating_score_writing` if you also want a numeric score, gap analysis, or rubric-anchored feedback. This server never requests your draft and instructs your AI to keep it local—rating sheets and scoring instructions flow to your AI.
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  • Get Lenny Zeltser's IR cross-server handoff routes — when this MCP server can't fulfill a request, which other MCP servers (or fallback workflows) to consult. Surfaces a compact subset of `ir_load_context`. This server never requests your incident notes and instructs your AI to keep them local—guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's CTI one-page executive brief template. Standalone variant of `cti_get_template` for callers that only want the brief without the long-form report. This server never requests your campaign or threat-intel notes and instructs your AI to keep them local—templates and guidelines flow to your AI for local analysis.
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  • Get Lenny Zeltser's malware analysis report template. The report covers Executive Summary, Sample Snapshot, Malware Family Identification, Component Inventory, Runtime Requirements, Sources, Capabilities, Indicators of Compromise, Analysis Details, What We Don't Know, optional Infection Vector, optional Detection Engineering, About this Report, Appendix: Analysis Environment, and optional Appendix: Analysis Scripts. This server never requests your sample, analysis notes, or indicators and instructs your AI to keep them local—guidelines and the report template flow to your AI for local analysis.
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