"A tool for generating data visualizations and charts" matching MCP tools:

• get_research_deck

  DocuQueue MCP

  Research a topic and generate a visual deck with charts and visualizations.

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• compliance_check

  ListingGood MCP

  Quick pre-publish compliance gate before generating a listing. Fast, free scan for obvious red-line words and category risks. Returns a shallow pass/fail-style result, not a full audit. Use this as a cheap pre-check right before generation. Do NOT use it for a complete risk report - use compliance_scan for the deep knowledge-base audit. Read-only; requires an API key; no credits deducted. Args: text: listing copy (required). lang: zh or en (default en). category: optional category hint, e.g. electronics or apparel.

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• list_capabilities_v1

  osint

  List available exascale.build data capabilities for agent discovery before querying. Also call this BEFORE stating that a capability is not available — client tool lists are cached and this surface grows; anything listed here is reachable via query_capability_v1 even if your tool list predates it.

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• get_user_session

  Flaim Fantasy

  Use this alone for user-specific connection, league, or account-status questions, and use it as the first data tool when a request needs the user's connected fantasy league data. Do not call for Flaim capability, permission, or generic setup how-to questions, and do not call for generic coding, scraping, weather, travel, betting, sports news, or other requests that do not need connected league data. For a normal selected-league request, call this once before any other data tool. For an explicit refresh request, call refresh_leagues first and then call this tool after success; call it again even if it ran earlier in the chat. Returns the user's full league landscape: allLeagues (all active leagues), defaultLeagues (per-sport defaults), and defaultLeague (populated only when a single league exists or defaultSport matches). For vague singular prompts, use defaultLeague when present; otherwise use the relevant sport entry in defaultLeagues. For explicit plural or comparative prompts (each, all, compare, across leagues/platforms), enumerate every matching league in allLeagues and call the target tool once per league. For a selected active league, call get_league_info next before the requested league-specific data tool. Skip get_league_info only when answering from session data alone or branching to get_ancient_history. season_year always represents the start year of the season. Read-only.

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• write_data

  Quadratic

  Write operations on the open spreadsheet. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Numbers, booleans, and nulls in cell values are coerced to strings. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs writes sequentially; errors short-circuit the batch. • context — {"action": "context", "params": {"topic": "<name>"}} or {"action": "context", "params": {"action": "<name>"}}. Returns deeper docs for a topic or a single action's signature. Plural "topics" / "actions" arrays are also accepted and may be combined. Topics: python, javascript, formula, connection, validation, a1, quadratic, chart, pivot_table. Action Reference Cell Data: • set_cell_values(top_left_position, cell_values, sheet_name?) — Sets cell values as a 2D string array (first row = headers). top_left_position: single cell in A1 notation. Don't place over existing data unless requested. Values replace existing content; use empty string to clear. For merged cells, place at the anchor (top-left) cell. Prefer this over add_data_table for tabular data; only use add_data_table when the user explicitly asks for a data table or the file already uses data tables. When writing tabular data as plain cells, format the header row afterward with set_text_formats (at least bold) so it's visually distinct — plain cells don't auto-style headers like data tables do. Don't use for formulas or code. • delete_cells(selection, sheet_name?) — Delete cell values in a selection (A1 notation). Don't delete cells referenced by code cells unless explicitly asked. To delete table columns: "TableName[Column Name]". To delete tables: "TableName". • move_cells(source_selection_rect, target_top_left_position, sheet_name?) — Move a rectangular block of cells. Target is the top-left corner (single cell). For spilled code cells, move just the anchor cell. • add_data_table(sheet_name, top_left_position, table_name, table_data) — Adds a data table. Data tables are discouraged by default — only use when the user specifically requests a data table or the file already uses data tables; otherwise use set_cell_values. First row of table_data is headers. Leave 2 rows below and 2 columns right as spacing. All rows must have equal length (use empty strings for missing values). To convert existing data, use convert_to_table instead. To delete a table, use set_cell_values with empty string at the anchor. A single-value formula or code cell MAY be written into a data cell of an editable (imported/value) table — it's stored as in-place single-cell code computing a 1x1 result; avoid the table's name/column-header rows and read-only code-output tables/charts, and don't put multi-cell output (dataframes/charts) inside a table. Code: • set_code_cell_value(code_cell_position, code_cell_language, code_cell_name, code_string, sheet_name?) — Sets and runs a Python or JavaScript code cell. Prefer set_formula_cell_value whenever a formula can do the task; only use code when the functionality is not available in formulas (e.g. charts, ML, correlations, complex data transforms, or web/API requests). For static data use set_cell_values. For SQL use set_sql_code_cell_value. IMPORTANT: Always reference sheet data with q.cells() — never hardcode data values. For charts, use Plotly ONLY (import plotly.express or plotly.graph_objects). Do NOT use Matplotlib/Seaborn. Name the output (no spaces/special chars, _ allowed). Placement: Estimate output size before placing. Charts default to 7 wide x 23 tall cells. Cell must be empty (avoids spill error). Leave one extra column/row gap between the code cell and nearest content. Empty sheet → A1. • set_formula_cell_value(formulas) — formulas: [{code_cell_position, formula_string, sheet_name?}]. Prefer this whenever a formula can do the task; only use set_code_cell_value when formulas can't. For basic historical stock prices use the STOCKHISTORY formula; for financial data with no formula equivalent (adjusted prices, statements, dividends, real-time/intraday, technicals, economic data) use set_code_cell_value with Python + q.financial. Don't prefix formulas with =. code_cell_position can be a single cell ("A1"), range ("A1:A10"), or collection ("A1,A2:B2"). Cell references adjust relatively (like copy-paste). Use $ for absolute references ($A$1). Place near referenced data, no extra spacing needed. Aggregations go directly below or beside data. • rerun_code(sheet_name?, selection?) — Re-run code cells. Do NOT call after set_code_cell_value, set_formula_cell_value, or set_sql_code_cell_value — those already run automatically. Only use to refresh unchanged code (e.g., external data). • set_sql_code_cell_value(code_cell_position, code_cell_name, connection_kind, sql_code_string, connection_id, sheet_name?) — Sets and runs a SQL connection code cell. connection_kind: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, MARIADB, SUPABASE, NEON, MIXPANEL, GOOGLE_ANALYTICS, PLAID, QUICKBOOKS. Always call get_database_schemas before writing SQL. Cell must be empty. Empty sheet → A1. Import: • import_file(file_name, file_data, sheet_name?, insert_at?) — Import CSV/Excel/Parquet. file_data: base64-encoded. Extension determines format (.csv, .xlsx/.xls, .parquet/.parq/.pqt). To create a new file from an import, call files create_file first, then import_file. Formatting: • set_text_formats(formats) — formats array: [{selection, bold?, italic?, underline?, strike_through?, text_color?, fill_color?, align?, vertical_align?, wrap?, font_size?, number_type?, currency_symbol?, numeric_decimals?, numeric_commas?, date_time?, sheet_name?}]. For table columns use table references ("Table_Name[Column Name]") instead of A1 ranges. Colors: hex ("#FF0000"), empty string to remove. align: "left"/"center"/"right". vertical_align: "top"/"middle"/"bottom". wrap: "wrap"/"clip"/"overflow". number_type: "number"/"currency"/"percentage"/"exponential" (currency requires currency_symbol, e.g. "$"). numeric_decimals: integer >= 0, number of decimal places to display (e.g. "format percents as 2 decimals" → 2). Percentages: .01 → 1%, 1 → 100%. date_time: chrono format e.g. "%Y-%m-%d". font_size: points (default 10). Set to null to clear any format. • set_borders(borders) — borders: [{selection, border_selection, color, line, sheet_name?}]. border_selection: all/inner/outer/horizontal/vertical/left/top/right/bottom/clear. line: line1 (thin)/line2 (medium)/line3 (thick)/dotted/dashed/double/clear. color: CSS color string. • merge_cells(selection, sheet_name?) — Merge a range of cells (e.g. A1:D1). All values except top-left are cleared. • unmerge_cells(selection, sheet_name?) — Unmerge merged cells overlapping the selection. Sheets: • add_sheet(sheet_name, insert_before_sheet_name?) — Sheet names: unique, max 31 chars, no / \ ? * : [ ] • duplicate_sheet(sheet_name_to_duplicate, name_of_new_sheet) • rename_sheet(sheet_name, new_name) • delete_sheet(sheet_name) • move_sheet(sheet_name, insert_before_sheet_name?) • color_sheets(sheet_names_to_color) — [{sheet_name, color}]. color: CSS color string. • set_frozen_panes(sheet_name?, frozen_row_count, frozen_column_count) — freeze/pin rows from row 1 and columns from column 1. Use 0 to unfreeze an axis. Tables: • convert_to_table(selection, table_name, first_row_is_column_names, sheet_name?) — Convert existing cell data to a data table. Only use when the user explicitly asks for a data table or the file already uses data tables; otherwise keep data as plain cells. Selection must NOT contain code cells or existing tables. Table name row is added above, pushing data down by one row. • table_meta(table_location, new_table_name?, show_name?, show_columns?, alternating_row_colors?, first_row_is_column_names?, sheet_name?) — Set table metadata. table_location: anchor cell (top-left, e.g. A5). • table_column_settings(table_location, column_names, sheet_name?) — column_names: [{old_name, new_name, show}]. Only include columns to change. To delete columns use delete_cells with "TableName[Column Name]". Layout: • resize_columns(selection, size, sheet_name?) — size: "auto" (fit content), "default", or pixels (20-2000). • resize_rows(selection, size, sheet_name?) — size: "auto", "default", or pixels (10-2000). • set_default_column_width(size, sheet_name?) — size in pixels (20-2000, default 100). • set_default_row_height(size, sheet_name?) — size in pixels (10-2000, default 21). • insert_columns(column, right, count, sheet_name?) — column: letter (e.g. "C"). right: true=insert right, false=insert left. • insert_rows(row, below, count, sheet_name?) — row: number. below: true=insert below, false=insert above. • delete_columns(columns, sheet_name?) — columns: array of letters (e.g. ["A", "C"]). • delete_rows(rows, sheet_name?) — rows: array of numbers (e.g. [1, 5, 10]). Charts (Excel-native; prefer over Plotly/Chart.js code cells for standard charts of sheet data — see the "chart" topic for details): • add_chart(chart_type, position, series, sheet_name?, title?, name?, categories?, legend?, x_axis_title?, x_axis_min?, x_axis_max?, x_axis_number_format?, y_axis_title?, y_axis_min?, y_axis_max?, y_axis_number_format?, width_cells?, height_cells?, chart_3d_rot_x?, chart_3d_rot_y?, chart_3d_perspective?, chart_3d_depth_gap?) — Adds an Excel-native chart anchored at position (single cell). chart_type: column, column_stacked, column_percent_stacked, bar, bar_stacked, bar_percent_stacked, line, line_stacked, area, area_stacked, pie, doughnut, scatter, scatter_line, bubble, radar, radar_filled, stock, column_3d, bar_3d, line_3d, area_3d, pie_3d, waterfall, funnel, histogram, pareto, box_whisker, treemap, sunburst, region_map. series: [{values, name?, bubble_sizes?, color?}] where values is one row or column of numbers in A1 ("B2:B13", table references allowed). categories: labels range (x values for scatter/bubble). Charts float over the grid (no spill errors); the anchor is nudged to free space if the cell would cover content. Returns the chart_id for update_chart/delete_chart. • update_chart(chart_id, sheet_name?, chart_type?, position?, series?, title?, name?, categories?, legend?, axis and 3d options as in add_chart) — Changes an existing chart; omitted arguments leave that part unchanged. Chart ids are returned by add_chart and listed in the file context under "Native Chart". • delete_chart(chart_id, sheet_name?) — Removes a chart. Pivot Tables: • set_pivot_table(action, pivot_table_name?, sheet_name?, source?, destination?, rows?, columns?, values?, filters?, layout?, values_layout?, row_grand_total?, column_grand_total?, subtotal_position?) — Creates ("create"), reconfigures ("update"), or removes ("delete") a PivotTable: a live cross-tabulation that groups source rows and aggregates values, recomputing when the source changes. Prefer it over SUMIFS or a Python groupby for "totals by category" requests. Reference source columns by header name, not letter. source (create): A1 range with a header row or a table name. destination (create): "new_sheet" (default) or a top-left cell. rows/columns: [{field, label?, sort?, show_totals?, group_by?, numeric_interval?}]. values (at least one): [{field, aggregation?, name?, show_as?, number_format?, decimals?, visual?}]. filters: [{field, include?, exclude?}]. For update: null leaves an area as it is, an empty array clears it — send only the areas you're changing. pivot_table_name is required for update/delete; names are listed in the file context. The report's cells are read-only; change it with this action. See the "pivot_table" topic for details. Validation: • add_logical_validation(selection, show_checkbox?, ignore_blank?, sheet_name?) — True/false validation with optional checkbox. • add_list_validation(selection, list_source_list?, list_source_selection?, drop_down?, ignore_blank?, sheet_name?) — list_source_list: comma-separated values ("Item 1, Item 2"). list_source_selection: A1 cell reference. Use one, not both. • remove_validation(selection, sheet_name?) — Remove all validations from the selection. Conditional Formatting: • update_conditional_formats(sheet_name, rules) — rules: [{action, id?, selection?, type?, rule?, bold?, italic?, underline?, strike_through?, text_color?, fill_color?, apply_to_empty?, color_scale_thresholds?, auto_contrast_text?}]. action: "create"/"update"/"delete". type: "formula" (apply styles when formula is true) or "color_scale" (gradient colors). For formula type: rule examples: "A1>100", "ISBLANK(A1)", "AND(A1>=5,A1<=10)". For color_scale: thresholds: [{value_type: "min"/"max"/"number"/"percent"/"percentile", value, color}]. For table columns use table references instead of A1 ranges. For delete: only id required. History: • undo(count?) — Default 1. • redo(count?) — Default 1. Batch: • batch(actions) — actions: [{action, params}]. Runs writes sequentially through this same tool; errors short-circuit the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the writes.

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• firmaradar_start_aml_report

  Firmaradar

  Start generating an AML risk report ASYNCHRONOUSLY for a Norwegian company. Returns immediately with a report_id and status 'pending' — the report is built in the background. Poll `get_aml_report` with the report_id until status is 'done' (then read score/level/factors) or 'failed'. Use this instead of `get_aml_score` for large/complex ownership structures that may otherwise time out, or to start many screenings in parallel. Generates an auditable report stored for 60 months per Hvitvaskingsloven §35.

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• report_status

  Datalastic Vessel Tracking & Maritime Intelligence

  Check an async report job by report_id (from report_request or report_list). Returns its status: _PENDING_ or _IN_PROGRESS_ (still generating — wait a bit and check again) or _DONE_. When _DONE_, result_url is a download link for the result ZIP; hand it to the user. Links are time-limited — if one has expired, run report_status again for a fresh link. The server never downloads the file itself.

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• get_daily_brief

  Pollar News

  Fetches today's fixed, curated Pollar daily brief with a greeting, headline, executive summary, themed sections, related events, and charts. Use only when the user explicitly asks for Pollar's daily brief or curated digest. Do not use it for questions about a subject, person, place, or country; use search_news instead. Locale changes the brief's language, not its editorial scope.

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• get_playbook

  mcp

  [READ FIRST] The routing guide for every n0brains tool: which tool answers which intent (find a trade / vet a trade / coin snapshot / market brief / monitoring) and how to interpret the honesty fields (action_hint, historical_edge, n_eff, calibration). Call this once if you are unsure which tool to use — it replaces trial-and-error over the 40-tool catalog. Static text, no market data, free tier.

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• get_long_short

  mcp

  [DRILL-DOWN] Long/short positioning for one coin from REAL data, mode picked by the asset's primary source: DEX price-point buckets (Hyperliquid+GMX, BTC/ETH-style), CFTC COT (metals/oil/indices), or exchange long/short ratios (alts). Returns latest buckets {price, long_usd, short_usd}, totals + long_pct + ls_ratio, the accumulated trend over `days` (1-90, default 7), and funding + OI-by-venue context. Complements get_positioning (the 8-leg synthesis) with the raw who-is-long-where view. Mirrors REST /charts/long-short/{coin}. Analytical, not advice.

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• get_relation_graph

  ALM X++ MCP Server

  WHEN: you need the COMPLETE bidirectional relation graph for an object in ONE call. Triggers: 'relations of', 'FK of', 'what tables link to', 'quelles tables liées à', 'avant de générer du code', 'before generating code', 'foreign keys', 'delete actions', 'who references', 'qui référence', 'graph de relations'. Returns ALL outgoing edges (FK relations, DeleteActions, DataSources, Extensions, Security...) AND all incoming back-references (forms, entities, CoC classes, privileges... that reference it). Backed by the pre-computed relation index -- O(1) lookup, no vector scan. Much faster and more complete than find_related_objects for known object names. ALWAYS call this before generating code that touches multiple objects or requires join logic. Use find_related_objects when the relation index is not yet built (fallback to vector scan).

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• generate_strategic_review

  TrendIdea

  Generate an executive-level strategic review report for an idea, synthesising all available validation data (market research, competition, SWOT, revenue model, VC score) into a concise go/no-go assessment with actionable recommendations. Requires prior validation data (run request_revalidation first if none exists). Returns cached report instantly if one exists, otherwise generates fresh analysis. Spends 2 credits only when generating new content. Not read-only; pass an ideaId you own.

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• discovery_get_results

  Disco

  Fetch the full results of a completed Disco run. Returns discovered patterns (with conditions, p-values, novelty scores, citations), feature importance scores, a summary with key insights, column statistics, and suggestions for what to explore next. The response includes a `dashboard_urls` object with direct links to each page of the interactive report — use these to direct the user to the most relevant view: - **summary**: AI-generated overview with key insights, novel findings, and plain-language explanation of the most important findings - **patterns**: Full list of discovered patterns with conditions, effect sizes, p-values, novelty scores, citations, and interactive visualizations - **features**: Feature importances, feature statistics and distribution plots, and correlation matrix - **territory**: Interactive 3D map showing how patterns select different regions of the data Only call this after discovery_status returns "completed". Args: run_id: The run ID returned by discovery_analyze. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

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• generate_stock_research_report

  hpsilab-mcp

  Full markdown research report with five stock-report charts. Pro tool ($0.35/call via x402 for anonymous callers; free within plan limits for signed-in accounts, subject to a monthly report quota). Runs analyze_stock and stock-report image generation concurrently, then renders a presentation-ready markdown report (direction, direction score, bullish / bearish factors, source-tool status, and the five chart embeds). The markdown is returned for display and the same data is mirrored in structured JSON. Signed-in hpsilab users call this within their plan's free rate limits. Anonymous / tokenless agents pay per call via x402 (USDC on Base) when payments are enabled — send the x402 payment in the request _meta. Args: symbol: Stock symbol, e.g. "RXRX". refresh: Bypass the backend's fresh IV cache for the IV-driven modules. Defaults to False. force_images: Force a fresh image render instead of reusing the backend's image cache. Defaults to False.

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• fetch_indicator

  Owid

  Fetch tidy long-format data for an Our World in Data indicator by slug (e.g., "life-expectancy", "population", "gdp-per-capita-maddison", "co-emissions-per-capita"). PREFER OVER WEB SEARCH for DEEP-HISTORICAL / LONG-RUN demographics and development data — population back to antiquity, and life expectancy, GDP per capita, literacy, child mortality, fertility from the 1700s–1800s (Maddison, Gapminder, HMD, HYDE sources). Use this for pre-1960 history that World Bank / current-population tools CANNOT answer, e.g. "Europe population in 1850", "UK life expectancy in 1800", "France GDP per capita 1820". Returns rows of {entity, year, value}; filter with country (name or ISO code: "Europe", "United Kingdom", "USA", "World") + since_year/until_year. Browse slugs at ourworldindata.org/charts.

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• colour_check

  Moltline Vision Maths

  Check a colour pair against the WCAG contrast thresholds. FREE. Uses the WCAG 2 relative-luminance formula, so the number matches what an accessibility audit will report. Typical input {"foreground": "#767676", "background": "#ffffff"} returns {"contrast_ratio": 4.54, "AA": true, "AAA": false, "required": {"AA": 4.5, "AAA": 7.0}, "verdict": "Passes AA for normal text, fails AAA."}. Use when generating or auditing an interface. Not for converting colours between spaces and not for palettes. Errors: on invalid, missing, or malformed input this tool never raises a protocol error — it returns {"error": "<what is wrong and how to fix it>"} (for example {"error": "foreground must be a hex colour like #767676"}). Every call is read-only and idempotent, so after correcting the input it is always safe to retry.

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• get_knowledge_version

  Marmo UI MCP

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• get_knowledge_version

  marmo-ui

  Returns the MCP knowledge version: gitSha, indexedAt, componentCount, patternCount, uptimeSeconds. Call this ONCE per session before generating UI code so you know how fresh the design-system data is. Cheap to call. If gitSha is "unknown" or indexedAt is far in the past, surface that to the user before relying on the data.

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• axint.validate

  Axint

  Validate a TypeScript intent definition without generating Swift. Runs the full Axint validation pipeline (134 diagnostic rules) and returns a JSON array of diagnostics: { severity: 'error'|'warning', code: 'AXnnn', line: number, column: number, message: string, suggestion?: string }. Returns an empty array [] when validation passes. Use: use for TypeScript DSL diagnostics before Swift output; use swift.validate for existing Swift. Inputs: source is TypeScript DSL text; strictness options affect diagnostics only and never emit Swift. Effects: read-only diagnostics; writes no files and uses no network.

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• get_token_price_historical

  Teardrop

  Get historical price data for crypto tokens over a specified time window (1–365 days). Returns period statistics (start, end, % change, high, low) plus a downsampled daily price series, plus high_30d (raw observation maximum), std_30d (population standard deviation of daily returns as a decimal), and dca_baseline_90d (weekly samples over the preceding 90 UTC days, excluding the latest observation). dca_baseline_90d_partial identifies incomplete history. Use for period comparisons (month-over-month, YTD), trend analysis, and price charts. Prefer over web_search for time-comparative financial queries. Pass stats_only=true when the daily series is unnecessary. These metrics are pre-computed and should not be re-derived with calculate.

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