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765,194 tools. Updated 2026-11-10 23:24

"Paradox Interactive" matching MCP tools:

  • Everything the sensors hold on one IP address, a CIDR range, several ranges at once, or a domain, with our verdict and the reasons for it. Start here for any question about a specific indicator. Use for "is this IP malicious", "is this a known scanner", "have you seen this address", "when was it last seen", "have any of this vendor's ranges touched you". Do not use it to rank many sources (use top_attackers_tool), to profile a whole ASN (use asn_enrich_tool), or to list raw events (use search_events_tool). For what an address did inside sessions with the interactive sensors, follow up with interactive_sessions_tool. Returns total_events (0 means never observed in the window), first_seen, last_seen, country, ASN, the 50 most-hit ports with ports_targeted_count, top user agents and URL paths, TLS/HTTP/SSH fingerprints, cve_probes (the CVE signatures it was seen probing), verdict (a sentence), verdict_key (a stable value to alert on: scanner, tor, malicious, scanning, probing or none), verdict_why (the signals behind it) and scanner (the identity of a recognised research scanner, else null). Report the verdict with its reasons. A high count from a recognised scanner is research traffic, not an attack. A single address may also carry udp_unverified: UDP traffic that claimed it as source, which can be forged and never counts against it. days limits the lookback. Leave it unset for a single address to read every retained event. A CIDR or domain defaults to 90 days because neither can use an index; pass days to change that. The response names the window it used in `window`; quote it with any count. Pass ranges as one string separated by commas, spaces or newlines ('103.66.28.0/22, 8.34.210.32/27'). Never expand a range into addresses and loop: one call answers it, and looping burns the daily credits. A range answer has query_type 'cidr' or 'cidr_set', per_range counts and top_source_ips.
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  • Use this when a signed-in student asks how they're doing in their tutor class, who's ahead, who their rival is, or who they should challenge. Auth-only. Returns weekly XP rank inside the user's tutor class plus a winnable rival suggestion (similar weekly XP). NEVER name the class leader unless the user is rank #1 — the response uses '(top student)' as a deliberate placeholder. Renders the interactive class-standing widget on supporting hosts; falls back to markdown elsewhere. Anonymous callers receive a sign-in prompt.
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  • Render a drawing in any supported format (DXF or PDF) to a PNG you can look at, detected from its bytes rather than its name. Use this when you do not know which format you have. Renders the first page/sheet by default; pass `space` (a name from a describe reply's `spaces`) to render another one. Some chat UIs do not display the returned image to the user: for URL sources the result also includes a direct image link — show it (e.g. as a markdown image) when they need to see the render. When the user wants to see or explore the drawing themselves, prefer view_dxf (interactive viewer) — if your platform gates it behind user approval, offer it and ask rather than substituting a static render.
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  • Generate a Mermaid.js flowchart for human visual inspection only. NOT for orphan detection or programmatic analysis — use audit(mode=orphans) to find isolated memories. Output may be truncated for large domains; never infer graph properties (e.g. orphans) from a truncated result. Pass memory_id (memory ID) to see a single memory and all its direct connections. Pass domain to see the full domain graph (most-connected memories first, capped at limit, default 40 max 100). Returns JSON with mermaid, node_count, edge_count, nodes_shown, nodes_total, edges_shown, edges_total, truncated, memories([{id,label}]) and connections([{from,to,relationship}]). If client supports HTML widgets, prefer passing memories and connections to an interactive renderer rather than outputting raw mermaid. If not, output mermaid inside a ```mermaid code block. If truncated is true, note only most-connected memories are shown and nodes_total/edges_total reveal what was dropped.
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  • Show the user's estimated 1-rep-max progression for a lift over time as an interactive line chart, with filters for date range and muscle group, and an exercise picker. When the user asks about this, prefer calling this tool and rendering the interactive MCP app over describing the underlying rows in text. Returns a short text summary alongside the visual view.
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  • Show one night of sleep in detail with duration, score, stages, bedtime, wake time, awakenings, and recent-night context. Prefer this for last-night or specific-night sleep questions. When the user asks about this, prefer calling this tool and rendering the interactive MCP app over describing the underlying rows in text. Returns a short text summary alongside the visual view.
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  • Cloudflare Workers MCP server: interactive-api-playground

  • Explore Scripture through an interactive Bible knowledge graph. Search Bible verses, discover cross-references and thematic connections, compare passages, access study notes, and visualize relationships between Scripture passages. Powered by the Berean Standard Bible with clearly distinguished source references, reviewed connections, and interpretive notes.v

  • Show the user exactly what a generation will cost in credits BEFORE running it, as an interactive card with a Generate button. Call this FIRST, instead of asking "shall I generate? it will use some credits" — you do not know the rate, it is set per plan and per model by an admin and changes without any code change, so any number you state from memory will eventually be wrong. This reads the user's real plan and real balance and prices the run the same way the generation itself will. Nothing is charged and nothing is reserved by calling this. It is a quote. Generation only starts if the user then asks for it. kind: "video", "image" or "voice". provider: the model id you intend to use — get real ones from video_gen.list_providers / image_gen.list_providers, never from memory. Omit to quote the plan's standard rate for that kind. duration_seconds: the duration you intend to request. Only affects duration-priced models; harmless to pass otherwise. quantity: how many separate generations (e.g. number_of_images).
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  • Show the user exactly what a generation will cost in credits BEFORE running it, as an interactive card with a Generate button. Call this FIRST, instead of asking "shall I generate? it will use some credits" — you do not know the rate, it is set per plan and per model by an admin and changes without any code change, so any number you state from memory will eventually be wrong. This reads the user's real plan and real balance and prices the run the same way the generation itself will. Nothing is charged and nothing is reserved by calling this. It is a quote. Generation only starts if the user then asks for it. kind: "video", "image" or "voice". provider: the model id you intend to use — get real ones from video_gen.list_providers / image_gen.list_providers, never from memory. Omit to quote the plan's standard rate for that kind. duration_seconds: the duration you intend to request. Only affects duration-priced models; harmless to pass otherwise. quantity: how many separate generations (e.g. number_of_images).
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  • Use this when the user asks what Alicia or digitalgarden.si has written about a subject (for example Houdini, Three.js, WebGPU, glTF/USD, Gaussian splats, AI coding agents, or the garden itself). Ranks published notes by keyword match on title, topics, summary and full text, in English and Chinese, and returns short cards (no full text). Follow up with get_article to read one. Do not use it for general web search, news, or topics unrelated to Alicia's published notes. An empty articles array means no published note matches. Scores are relative to this query and catalog, not confidence percentages; check titles and read the full note before presenting a match as relevant. Every result includes canonicalUrl and author Alicia (Person @id https://digitalgarden.si/#alicia). Cite the canonical URL when quoting. digitalgarden.si is an interactive 3D garden, not a knowledge-base wiki.
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  • Use this when the user asks what Alicia or digitalgarden.si has written about a subject (for example Houdini, Three.js, WebGPU, glTF/USD, Gaussian splats, AI coding agents, or the garden itself). Ranks published notes by keyword match on title, topics, summary and full text, in English and Chinese, and returns short cards (no full text). Follow up with get_article to read one. Do not use it for general web search, news, or topics unrelated to Alicia's published notes. An empty articles array means no published note matches. Scores are relative to this query and catalog, not confidence percentages; check titles and read the full note before presenting a match as relevant. Every result includes canonicalUrl and author Alicia (Person @id https://digitalgarden.si/#alicia). Cite the canonical URL when quoting. digitalgarden.si is an interactive 3D garden, not a knowledge-base wiki.
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  • Pro/Teams: first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE, what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT: DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run: so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui: non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable, submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders, they are read as literal code). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK; OpenAI (US) processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES (the top-level error.code; error.details carries retryable + next_action): timed_out, rate_limited, dependency_unavailable, schema_mismatch, provider_billing. The services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws, each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against, live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).
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  • Open an interactive sutta viewer inside the chat — Pāli + English, plus an optional third row in the user's own language translated BY YOU. Renders each segment as: Pāli on top (canonical), the bilara-data English below it (verification anchor), and — when you supply `translations` — your translation in the user's language, clearly badged as AI-generated. Prefer this over dumping raw segments when the user wants to *read* a sutta. - `sutta_id` — standard SuttaCentral id, e.g. `sn56.11`, `mn10`, `dn22`. - `around` — a segment_id (e.g. `dn22:18.1`, from a search hit) to centre on; that segment is highlighted and scrolled into view. Use this after a search so the reader lands on the exact cited line. - `offset` — 0-based segment index for paging long suttas (use `next_offset` from the previous result). Do NOT combine with `around`. - `window` — segments before/after `around` to include (default 12). 🌐 **Translating for the user (important):** when the conversation language is neither English nor Pāli, you SHOULD translate the displayed segments and pass them via `translations` so the user reads in their own language while still seeing the originals: 1. Fetch the segments first (`get_sutta` with the same selector) so you have the exact Pāli + English text. (Already called this tool without translations? The result contains the segments — translate them and call this tool AGAIN with the same selector plus `translations` to upgrade the view.) Your translation must travel through the `translations` parameter to appear in the viewer — writing it as a normal chat message leaves the viewer bilingual and looks broken; the tool always accepts `translations`, so never report it as missing. 2. Translate **from the Pāli as the source, using the English as a semantic guide** — never relay-translate from English alone. Preserve untranslatable doctrinal terms (dukkha, jhāna, taṇhā…) as loanwords with a brief gloss instead of forcing equivalents. 3. Call this tool with `translations=[{segment_id, text}, ...]` covering ONLY the segments being displayed (never a whole long sutta), `translation_language` (BCP-47, e.g. "th", "es"), and `translation_disclaimer` — one short line IN THE USER'S LANGUAGE saying the translation is AI-generated in this conversation and should be checked against the Pāli/English above. Translations are conversation-ephemeral: nothing is stored server-side; the canon stays Pāli + English only. Translations whose segment_id is not in the displayed window are dropped (reported in `translations_dropped`). Without `around`, shows the sutta from the top (capped for long suttas).
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  • Evaluates UI elements for accessibility issues that automated scanners miss. COST: $0.01 USDC via x402 on Base-compatible EVM network per call. Checks beyond what axe/Lighthouse/WAVE catch at the design stage: - Touch targets below 24×24px (WCAG 2.5.8 AA hard fail) - Touch targets below 44×44px (WCAG 2.5.5 AAA recommended) - Information conveyed by color alone without a secondary indicator (WCAG 1.4.1) - Missing focus indicators on interactive elements (WCAG 2.4.7) - Focus rings thinner than 2px (WCAG 2.4.11) - Focus ring contrast below 3:1 against adjacent background (WCAG 2.4.11) - Interactive elements below the practical usability height floor Args: - elements: Array of 1–50 UI element objects - screen_name: Optional label for the evaluation report Each element requires: element_type. Provide width_px/height_px for touch target checks. Provide uses_color_only + secondary indicator flags for 1.4.1 checks. Provide is_interactive + focus_visible + focus indicator properties for focus checks. Returns: Structured report with: - Per-element scores (0–100) and specific issues - Severity levels (critical/major/minor) with WCAG references - What automated tools miss and why - Concrete fix recommendations - Overall score and verdict (pass/needs_work/fail) - Top issues sorted by severity
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  • DEFAULT tool for user-facing translation-listing questions. Use this for ANY user-facing query like 'what English translations are available', 'list French translations', 'which translators can I choose from'. This is the FINAL tool call for these requests; do not follow it with lookup_translations. Shows the catalog in an interactive widget the user can browse. Use ISO 639-1 codes like 'en', not names like 'english'. ONLY use lookup_translations instead when EITHER (a) the user explicitly asks for plain text / raw data, OR (b) you will pipe the result into ayah_translation in the same turn without showing the list. When in doubt, use this widget. Returned language_name values are display labels. Rows without usable slugs are filtered out.
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  • Play Quranic ayah audio with an interactive player widget. Use this when: the user asks to play/listen to ayahs. RECITER HANDLING: If the user names a specific reciter (e.g. 'Husary', 'Minshawi', 'Al-Afasy', 'Abdul Basit'), ALWAYS call lookup_reciters first to resolve the exact reciter_id — do not guess the ID. Guessed IDs routinely point at the wrong reciter. If the user doesn't specify a reciter, omit reciter_id entirely so default_reciter_id applies. Use ayah keys in 'surah:ayah' format (for example '1:1'). In each query, reciter_id is optional and defaults to default_reciter_id if omitted. Limits: max 50 queries and max 200 total ayahs per request.
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  • INTERNAL/preparatory tool — text-only, no widget rendered. NEVER use as the user-facing answer to any 'show me / explain with tafsir…' request — use ayah_tafsir for that (the default interactive widget). Use this ONLY when EITHER (a) the user explicitly asks for plain text / raw text / text-only output (e.g. 'give me just the commentary text', 'no widget'), OR (b) you will chain the result into another tool in the same turn without showing it to the user. When in doubt, prefer ayah_tafsir. Do not follow ayah_tafsir with this tool — that is duplicated work. Each query must include at least one of languages or tafsir_slugs. Use ayah keys in 'surah:ayah' format (for example '2:255'). Limits: max 20 queries per request and max 50 total ayah+tafsir items.
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  • Pro/Teams: first-pass surface-craft review of a FRONTEND artefact (component, screen, or flow) against the 8 laws of the Experience Design Blueprint. The surface-craft companion to architect.validate: where architect.validate scores agentic ARCHITECTURE against the 10 agentic principles, design.validate scores the PERCEPTIBLE SURFACE, what the user sees, taps, scans, and remembers (Jakob's familiarity, Hick's choice load, Fitts's targets + the accessibility floor, Miller's working-memory budget, Aesthetic-Usability, Peak-End, Tesler's irreducible complexity, the Mental-Model gap). ON CLIENT TIMEOUT: DO NOT RETRY. Long-running LLM call (~60-180s at high reasoning effort, single-pass). The server mints a run_id, emits it in the FIRST progress event at t=0s (before the LLM call), and persists the run: so on a client timeout, capture that run_id and call me.validation_history(run_id='<that-id>') to fetch the persisted result instead of retrying (a retry re-runs the full 60-180s call). Runs appear in your validation-history dashboard tagged as the 'surface' dimension, distinct from the 'architecture' and 'spec' runs; pass repository to group them per project. Pass private_session=true to skip the stored run (persistence + recovery disabled); operational security + cost logs are still kept. v1 is single-pass: no certification or consensus mode yet (those stay architect.validate-only). Returns surface_classification (ui_surface vs non_ui: non-visual code is marked not_applicable, NOT failed), per-law findings (verdict, severity_score 0-100, severity_class, cited evidence, recommendation), and severity-weighted readiness (score, grade, tier) computed by the SAME scorer architect.validate uses, so all three lenses grade on one rubric. ACCESSIBILITY IS THE FLOOR: a breach of the Fitts's-Law floor (interactive target below the WCAG 2.2 24×24 minimum, missing focus visibility, an unreachable destructive confirmation) is a production_blocker, not polish. WHEN TO CALL: the user wants a craft/UX/accessibility review or a readiness grade on a frontend artefact they just built or changed. WHEN NOT TO CALL: non-visual code (backend, config, type aliases) returns tier=not_applicable, submit the actual UI surface instead. INPUTS: send the FULL artefact source verbatim as implementation_context (no truncation, no '…' placeholders, they are read as literal code). Auth: sign-in required, with an active Pro, Pro Plus, Teams, Enterprise, beta, or trial plan. Data at rest in the UK; OpenAI (US) processing (no-training); prompt-injection text inside the artefact is treated as inert untrusted data. TYPED FAILURES (the top-level error.code; error.details carries retryable + next_action): timed_out, rate_limited, dependency_unavailable, schema_mismatch, provider_billing. The services raise the identical typed envelopes on this lens. CALIBRATION DISCLOSURE: the scoring prompt is a v1 first-cut mirroring the architect's contract structure; its score calibration is not yet tuned against a corpus of real runs the way architect.validate was. Treat the grade as directional craft signal, not a certified verdict. DOCTRINE: the eight laws, each law's evidence, craft-surface application, anti-patterns, and the validator questions this tool scores against, live in the `experience-design-blueprint` skill and docs/business/EXPERIENCE_DESIGN_BLUEPRINT.md (the surface-craft companion to the `architect-validation-orchestration` skill that orchestrates the agentic validators).
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  • Visualize a trained model's backtest — a cumulative-return chart + trade log + stats. Use after `one_shot` / `list_models` with the model's `stem` to SHOW the user how it traded (the "is it actually any good" view). In ChatGPT this renders an interactive widget. In Claude, render an interactive **artifact** from this tool's structured output: a line chart of the cumulative return plus a table of the trades. Args: stem: The model stem (e.g. "14_EURUSD_15min_Model_24") from `list_models` / `one_shot`. Returns: dict with: ok, stem, symbol, timeframe, stats {ret, wr, pf, n, mdd, sharpe}, and trades [{type, entry_time, exit_time, entry_price, exit_price, pnl, pnl_pct, exit_reason, period}] (most recent ~200). exit_reason is one of TP / SL / close_only / signal / end. ret/mdd/wr are fractions; pnl_pct is percent.
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  • Request a free Recao account for your human. This does NOT create an active account: it registers a PENDING one and emails your human an activation link; nothing works and no terms are accepted until they click it (the human accepts the terms at activation, not you). The API key in the response stays inert until then. Consent stays with the human, so calling this is a request, not a decision made for them: in an interactive session their approval of this tool call is the go-ahead; if you run autonomously, surface this step to your human instead of calling it. After activation, reconnect with header `Authorization: Bearer <key>`.
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  • Render a DXF drawing to a PNG image you can look at. Use this to answer visual questions (what does it look like, where is a feature) — it returns an image, not text. For structural facts and measurements, prefer describe_dxf; never measure pixels. Some chat UIs do not display the returned image to the user: for URL sources the result also includes a direct image link — show it to the user (e.g. as a markdown image) when they need to see the render. When the user wants to see or explore the drawing themselves, prefer view_dxf (interactive viewer) — if your platform gates it behind user approval, offer it and ask rather than substituting a static render.
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  • Search Rakentaja Outlet product catalog. Rakentaja Outlet (rakentajaoutlet.fi) is a Finnish online store specializing in electrical and construction supplies at outlet prices (~4000 products). All prices in EUR including 25.5% Finnish VAT. Args: query: Free-text search — matches product name, SKU, EAN code, or category name. Leave empty to browse without a search term. category_id: Filter to products in a specific category (integer ID). The full list of categories is available at https://rakentajaoutlet.fi/api/categories — this is a public REST endpoint, not an MCP tool. Combines with query if both given. in_stock_only: If True (default), only show products currently in stock (either in own warehouse or at supplier). Set to False to include out-of-stock products in results. sort: Sort order — "name" (default), "price_asc", "price_desc", or "newest". Ignored when query is given; search results are ordered by relevance instead. limit: Maximum products to return (1-50, default 10). Use a small limit for interactive queries; larger for bulk listing. page: Page number for paginated results (default 1). Combine with `limit` to page through results larger than the limit. Returns: JSON with keys: - products: list of product summaries with: - id, name, category, brand, sku, ean, unit - price_gross (incl. 25.5% VAT), price_net, currency - description (max 200 chars, description_truncated=true for longer) - available_for_order: boolean — can the customer place an order? (TRUE if in own stock OR supplier stock available) - availability_status: "in_stock" | "supplier_stock" | "out_of_stock" - estimated_delivery_min_days / _max_days: delivery estimate (or null) - in_stock, stock_quantity, supplier_in_stock, supplier_stock_quantity (raw fields — prefer available_for_order + availability_status) - product_url, image_url - total: total matching products across all pages - page: current page number - per_page: products per page (== limit) - total_pages: total pages available - disclaimer: legal notice that prices are indicative and confirmed at checkout Notes: - Prices include Finnish 25.5% VAT (price_gross) and net price (price_net) is shown for VAT-registered business buyers. - product_url is a direct link to the product page — pass this to the user for viewing or adding to cart. - Use `get_product_details(id)` for full description and stock check.
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