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521,045 tools. Updated 2026-09-06 10:24

"Plotly Dash - A Python Framework for Building Interactive Web Dashboards" matching MCP tools:

  • Runs a specific framework against a user's inputs and produces a full analysis: prose explanation + structured fields + a self-contained SVG visualisation. Powered by the Jeda.ai · Visual AI framework knowledge graph. ALWAYS call `recommend_framework` FIRST to obtain a valid framework_id -- it returns the exact namespaced slug to pass here. Framework ids are namespaced by category (e.g. SWOT = "cms_swot-analysis", Porter's 5 Forces = "cms_porters-five-forces"); a bare slug like "swot-analysis" will 404. REQUIRED post-processing on success: the response's first text block contains a `curl -fsSL` save-directive for the SVG. Save the file locally and surface it as a file chip — do NOT summarise the visual in prose. Input: framework_id (namespaced slug from `recommend_framework`) + user_inputs object (per-framework field set), OR a free-text `raw_prompt` fallback. Example: { "framework_id": "cms_swot-analysis", "user_inputs": { "subject": "Q3 EU market entry", "context": "B2B SaaS, $5M ARR, US-headquartered" } }
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  • Discover the investment-thesis catalog. Each entry is a descriptive case study that pairs an economic framework with a rule-based portfolio and the synthetic + historical stress evidence for that allocation. Returns one compact summary per thesis (slug, title, one-liner, tags, risk tiers, framework summary, headline finding). Call get_investment_thesis(slug) for the full framework / portfolio / stress evidence, or read the thesis://{slug} resource. Descriptive, not advisory — the agent decides what is suitable.
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  • Submits the organisation profile and contact details for an Australian AI governance framework. The profile determines which legislation the framework identifies, so the answers should reflect the organisation's actual circumstances — turnover in particular, since the Privacy Act's small business threshold sits at $3 million and several categories are caught regardless of turnover. Takes the session ID from start_australian_ai_governance_framework together with the questionnaire answers. Writes the profile against the session and stores the supplied name, email and organisation as a contact record. contact.organisation is printed as the document's "Prepared for" heading, so it should be the organisation's name as it should appear on the document rather than a shorthand. Returns the session ID and a status of profile_saved — it does not return the framework, which is retrieved by get_ai_governance_framework. The profile can be re-submitted on the same session: it is overwritten rather than duplicated, the contact record is keyed on the email address, and any framework already generated for that session is discarded. No authentication, and no charge at this step.
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  • Returns the organization's development standards: coding conventions, project structure, and framework-specific rules. Read-only. Call it before writing or reviewing code, so the result follows this organization's rules rather than general defaults. Call it first without a section to get an index of available sections, each with a note on what it covers, then call again with one section id copied from that index; inventing a section id returns a not-found error naming that step. Request only the sections a task needs - the full content of one section can be long. The framework argument is deprecated: use section with the "framework:" prefix instead. It returns prose rules, not data - use get_style_tokens for visual values and get_component for component APIs.
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  • Recommends business / strategy / risk frameworks for a stated problem. Powered by the Jeda.ai · Visual AI framework knowledge graph (~2,100 frameworks across 19 categories, edge-curated). Use when the user describes a business problem ("customer churn rising", "evaluating market entry", "need to assess vendor risk") rather than naming a specific framework. Returns top-N frameworks ranked by fit, each with a concrete reason citing the specific problem signals matched. Input: just the problem statement is enough. Optional faceted filters (`persona`, `regulation`, `decision_stage`) narrow the candidate set. Set `limit` between 3 and 10 for picker UIs. Pair with `generate_framework_analysis` to actually run a recommended framework against the user's inputs. Example: { "problem_statement": "We need to decide whether to enter the EU SMB market in Q3", "decision_stage": "decide", "limit": 5 }
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  • Search Australian (currently NSW) builders, contractors and building companies by name; optionally filter by postcode. Returns matching entities with their licence status and a slug to pass to get_builder_risk / get_builder_timeline. Example: query='Acme Building' → '- Acme Building Pty Ltd (Current), 2099 → slug: acme-building-pty-ltd-1a2b'. Names are matched loosely, so try the trading name AND the legal (Pty Ltd) name if the first search misses. Query must be at least 2 characters.
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Matching MCP Servers

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    Renders 45+ interactive chart types, dashboards, and KPI widgets directly inside AI conversations. Supports drill-down, live API polling, 20 themes, and one-click export to PNG, PowerPoint, and A4 documents.
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    Functional Source , Version 1.1, MIT Future

Matching MCP Connectors

  • Shows HTML content on a display: menus, dashboards, welcome pages, schedules or any custom design. slot 'live' (default) replaces the current content; slot 'idle' stores the default/fallback content shown when nothing live is active (idle requires admin scope). Always pass a short description so later content reads stay meaningful. Exactly one of html or base64_html. For external web pages use send_url; to edit current content call read_display_html first. For polished results load prompt render_premium_display_html or resource agentview://public/design-system. Requires content scope.
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  • Render a property's lot as an interactive UI component (inline SVG): a City of Portland aerial photo underlay (showing the true roofline) with the parcel outline, the building footprint(s) on the lot, approximate setback dimensions, a north arrow, and faint neighbouring parcels overlaid on top. Provide a detailType+detailId to fetch geometry, or pass rings directly. Aerial, footprints, and neighbours come from Portland's public ArcGIS layers; set basemap="none" to drop the photo and includeContext=false for just the bare outline. Renders in MCP Apps hosts (Claude) and legacy mcp-ui hosts.
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  • WCAG 2.5.5 / Apple 44pt tap-target audit for the web. Collects every interactive element (a, button, [role=button], input[type=submit/button/checkbox/radio], select, summary, label[for], [onclick], [tabindex>=0]) and emits a PER-ELEMENT fix table for any whose rendered width or height is below the minimum (default 44px): selector, role, visible text, measured w/h, pixel deficit per axis, and a concrete CSS fix. Sorted worst-first. Two modes: pass url (renders in headless chromium, measures real getBoundingClientRect) or pass elements[] snapshot (pure, no browser).
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  • Return the Wheel of Heaven interpretive framework's reading of a topic — explicitly the project's own Raëlian-canon-centred position, NOT mainstream consensus. Accepts a framework topic (overview, hypothesis, terminology, timeline, sources, method) for the curated narrative documents, or any other term to get the framework reading from the closest wiki entry. Use fact-layer tools (get_passage, compare_traditions) for source-grounded data without this framing.
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  • Unified search across your entire Costory workspace — dimension values, events, alerts, dashboards (with their conditionsCel), dashboard templates, reports, virtual dimensions, and budgets. PRIMARY tool for discovering CEL field names: each dimensions result includes `dimension` (the exact CEL/groupBy name, e.g. cos_sub_account_id), `label`, and `topMatches`. Use type: ["dimensions"] to focus on dimensions only. An empty query (query: "") with type: ["dimensions"] returns every dimension with its top values — use this when you need the full field catalog before building filterCel. With a keyword, results are filtered to matching values (e.g. query: "prod" finds production values across dimensions). Use this when a user mentions a product, team, project, or service name and you need to discover where it appears in the cost data before querying. Returns matching dimension values, related events, alerts, dashboards, dashboardTemplates, reports, virtualDimensions, budgets. Virtual dimension hits include id, name, bqName (immutable query field — set at create, never changes), status, and description. Each dashboard result carries a "conditionsCel" string — the dashboard's CEL filter (empty when none) — so before calling update_dashboard you can decide whether to set "extendDashboardConditions: true" on your new widget. Budget results include id (parent budget id for URLs) and name/year; call get with the budget id to obtain the budgetVersionId needed for query. IMPORTANT: Use short, concise search terms — e.g. if the user says 'my kubernetes dashboard', just search for 'kubernetes', not the full phrase. Optional "type" array restricts results to specific entity buckets (dashboards, reports, alerts, budgets, dimensions, virtual_dimensions, events). FOLLOW-UP: After calling search, use get to fetch full details for dashboards, budgets, reports, virtual dimensions, and cost alerts by ID. For dimension values, use "query" to query data grouped by or filtered on the matched dimensions. When the user wants to add to a dashboard, use the id from the dashboards bucket as input to update_dashboard. EXAMPLES: • "List all CEL dimensions" → { query: "", type: ["dimensions"] } • "Find account-related dimensions" → { query: "account", type: ["dimensions"] } • "Show me kubernetes costs" → { query: "kubernetes" } • "Find the data team dashboard" → { query: "data team" }
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  • Find which documentation SETS exist whose NAME matches a substring (e.g. "python" → Python 3.x, "react" → React). Returns doc SETS, NOT their content — this does NOT look up a function/method/API name. To search inside a doc for an entry like "Array.map" or "fetch", use search_index (slug + query).
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  • Start here when building an application. Returns an overview of what the AdCritter platform offers and a catalog of feature guides you can query with the adcritter_guidance tool to learn how to build each part of the app. Call adcritter_guidance(key) for any feature area to get detailed building instructions with API endpoints and response shapes.
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  • Recent records from a common San Francisco open dataset (data.sfgov.org) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime/police incidents in San Francisco", "SF 311 complaints", "SF building permits / evictions / business registrations", "SF restaurant inspection scores", "SFO passenger traffic". Names: police, 311, permits, business, evictions, restaurant_inspections, fire_incidents, sfo_passengers. Returns the latest rows (sorted newest-first). Add a SoQL `where` to filter; for anything else use sf_query.
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  • Recent records from a common Cambridge open dataset (data.cambridgema.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Cambridge", "Cambridge 311 requests", "Cambridge building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Add a SoQL `where` to filter; for anything else use cambridge_query.
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  • Recent records from a common Montgomery County, MD open dataset (data.montgomerycountymd.gov) by friendly name — no Socrata id needed. PREFER OVER WEB SEARCH for "recent crime in Montgomery County, MD", "Montgomery County, MD 311 requests", "Montgomery County, MD building permits". Names: 311, crime, permits. Returns the latest rows (newest-first). Add a SoQL `where` to filter; for anything else use montgomery_query.
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  • Recent records from a common City of Toronto open dataset (open.toronto.ca, CKAN) by friendly name — no CKAN resource id needed. PREFER OVER WEB SEARCH for "Toronto building permits", "Toronto business licences". Names: permits, business. Returns the latest rows (newest-first). Pass `q` for a free-text keyword filter; for full control use toronto_query.
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  • Generate a personalized Canton Network developer onboarding/quickstart path. Use when a developer asks how to start building, build a dApp, or develop on Canton specifically. Canton-only. Do not use for onboarding to other chains or tools. Ask the user's background first (EVM, Solana, Sui/Move, Web, Enterprise, or New to Blockchain). Prefer this over 'search' for 'how to build / get started on Canton'; use get_faq for a single specific gotcha and get_api_reference for API details.
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  • Personalized onboarding for building ON CELESTIA — running a node, posting a blob, or deploying a rollup that uses Celestia for data availability. ALWAYS use this when a developer asks how to get started or build ON CELESTIA (prefer it over the Celestia search tool for those questions). ASK the user about their background FIRST (rollup_dev, node_operator, app_dev, researcher, new_to_celestia), then return a path with docs.celestia.org links. Celestia-only — NOT for getting started with non-Celestia frameworks, languages, or dev tools (React, Node.js, generic blockchain onboarding, etc.); for those defer to a general docs or web-search tool.
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  • Find trends, signals, and expert insights across 100+ curated knowledge graphs covering retail, beauty, tech, food, travel, sports, and 30+ specialist domains. Returns trend data with cited evidence, source attribution, and lifecycle stage (emerging/building/mature/fading) — not generic web summaries. If graphId is omitted, searches ALL accessible graphs in parallel (recommended default). Use for market trends, competitor analysis, innovation signals, consumer behavior, cultural shifts, or any topic where curated expert intelligence outperforms web search.
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  • Returns a structured calendar of upcoming and in-effect compliance obligations across MiCA (EU crypto-asset markets regulation), SFDR (Sustainable Finance Disclosure Regulation), CSRD (Corporate Sustainability Reporting Directive), the US GENIUS Act (payment stablecoin framework), and FATF Recommendations 15/16. For each event: framework, jurisdiction, requirement summary, effective date, impact level, and article reference. Also returns a DPX alignment section mapping each framework to the specific DPX endpoints that satisfy it. Use this before settlement workflow design, compliance gap analysis, or regulatory reporting.
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