"MySQL" matching MCP tools:

• read_dataA

  QuadraticA

  Read-only queries on the open spreadsheet. No data is modified. Safe to auto-approve. Call as {"action": "<name>", "params": {...}} — per-action params are listed in the Action Reference below. Special actions (not shown in the action enum): • batch — {"action": "batch", "params": {"actions": [{"action": "<name>", "params": {...}}, ...]}}. Runs reads in parallel; individual failures are reported per-entry without short-circuiting. • 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 • get_cell_data(selection, page?, sheet_name?) — Returns cell values for a selection in A1 notation. Supports comma-separated ranges to fetch multiple areas in ONE call, including across different sheets. Examples: "A1:B10, D1:E10", "TableName, OtherTable", "'Sheet1'!A1:B10, 'Sheet2'!C1:D10". Table names are globally unique so they work without sheet prefixes. For cell ranges on other sheets use 'SheetName'!Range. Only use when you need the full dataset (aggregations, lookups, analysis). The file summary already includes sample rows. Results may be paginated — use page (0-based) for additional pages. • has_cell_data(selection, sheet_name?) — Check if any cells in a selection have data. Returns true if ANY cell contains data. Use before creating/moving tables or code to avoid spill errors. All ranges MUST be on the same sheet. • get_code_cell_value(code_cell_position?, code_cell_name?, sheet_name?) — Get full code from an existing Python, JavaScript, or connection code cell. Do NOT use for formula cells — formulas are already in get_cell_data results and the file summary. • get_text_formats(selection, page?, sheet_name?) — Get text formatting info. Use table column references for tables ("Table_Name[Column Name]"). Results may be paginated. • get_validations(sheet_name?) — Get all validations in a sheet. • get_conditional_formats(sheet_name) — Get all conditional formatting rules. Use to check existing rules before creating/updating/deleting. • text_search(query, case_sensitive?, whole_cell?, search_code?, regex?, sheet_name?) — Search for text in cell outputs. Supports regex when enabled (e.g., "\d+", "^hello", "foo|bar"). Searches cell outputs only, not code. Booleans default false. • get_sheet_info() — List all sheets and names. • get_spreadsheet_context(sheet_name?, include_errors?) — Full context snapshot of the file. • read_data(selection, sheet_name?, max_rows?) — Read cell data as compact CSV. Auto-tiers: returns all rows for small/medium data (<5000 rows), head+tail preview for large data. Preferred over get_cell_data for most reads. • outline(sheet_name?) — Structural map of the file: sheets, bounds, tables, code cells, charts, connections, errors. Use to understand file layout before reading data. • dependencies(position, sheet_name?, direction?) — Trace cell dependencies. direction: "forward" (what this cell reads), "reverse" (what depends on this cell), or "both" (default). • export_pdf(options?) — Export the file as a PDF with Excel-parity print semantics. Returns {mime_type, size_bytes, data_base64}. options is a camelCase object: {sheetIds?: [id], fileName?, pageSetup?: {paperSize ("letter"|"legal"|"tabloid"|"a3"|"a4"|"a5"|...), orientation ("portrait"|"landscape"), margins {left,right,top,bottom,header,footer} (inches), scaling ({type:"zoom",percent} or {type:"fitTo",width?,height?}), pageOrder ("downThenOver"|"overThenDown"), centerHorizontally?, centerVertically?, printGridlines?, printHeadings?, header/footer {odd:{left,center,right}, even?, first?} with Excel codes (&P page, &N total, &D date, &T time, &F file, &A sheet, &B bold)}, sheetOptions?: {"<sheetId>": {pageSetup?, printArea? ("A1:F20"), repeatRows? ([1,2]), repeatCols?, rowBreaks?, colBreaks?}}}. Omit options for sensible defaults (letter portrait, 100% zoom, all sheets). • list_connections(team_uuid?) — List all database connections in a team (PostgreSQL, MySQL, MS SQL, Snowflake, BigQuery, Mixpanel, Google Analytics, Plaid, etc.). Returns each connection's uuid, name, and type. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Call this BEFORE get_database_schemas or set_sql_code_cell_value to discover the connection_ids and connection types you need. • get_database_schemas(connection_ids, connection_type, team_uuid) — Get table/column schemas for database connections. Always call before writing SQL. Get connection_ids from list_connections. connection_type: POSTGRES, MYSQL, MSSQL, SNOWFLAKE, BIGQUERY, COCKROACHDB, etc. • list_agent_connections(team_uuid?) — List the team's ready Agent Connections (third-party REST API bindings). Returns each connection's uuid, name, service, base URL, auth pattern, and `{{SECRET_NAME}}` references to use in fetch code. team_uuid is optional — if omitted, the user's only team is used; multi-team users must pass it. Reference secrets via `{{SECRET_NAME}}` in Python/JavaScript fetch code; the connection proxy substitutes team secret values at request time. • inspect_agent_connection(connection_id, team_uuid?) — Get the full schema (resources, endpoints, fields, docs URLs) and plan for one ready Agent Connection by uuid (from list_agent_connections). Call BEFORE writing fetch code against a connection so you don't guess at endpoints. team_uuid is optional with the same single-team fallback as list_agent_connections. Batch: • batch(actions) — actions: [{action, params}]. Runs reads in parallel through this same tool; per-entry failures are reported in the result without short-circuiting the batch. `action` may be any name from this reference. Nested `context` items are allowed and returned alongside the reads.

  ConnectorNo auth

• write_dataA

  QuadraticA

  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(top_left_position, table_name, table_data, sheet_name?) — 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.

  Connector

  Destructive

  No auth

• run_sqlA

  FixterA

  Execute a read-only QuerySQL SELECT against the observability data. QuerySQL is standard SQL (MySQL-compatible syntax, backtick-quoted identifiers) with automatic tenant isolation. Write normal SQL — most standard features work: WHERE, GROUP BY, HAVING, ORDER BY, LIMIT, DISTINCT, CASE WHEN, LIKE, ILIKE, BETWEEN, IN, !=, <>, IS NULL, IS NOT NULL, NOT, OR, AND, subqueries, derived tables, JOINs, aliases, COALESCE, IF. Also =~ 'pattern' (case-insensitive match, * wildcard); = / != with a *-wildcard string value behave as ILIKE / NOT ILIKE. Free-text search: matches('text') in WHERE searches the message, all attributes, and service case-insensitively (substring match; trace/span ids by exact match), e.g. SELECT * FROM logs WHERE matches('connection refused'). Call describe_schema first to discover available fields and dynamic attributes for your data. Sources: logs, spans, metrics. Dynamic attributes are queryable directly by name, dots included: http.request.method. Resource attributes need the resource. prefix: resource.service.name (logs and spans only; metrics does not expose resource attributes). Missing attributes read as NULL. Common fields per source: logs: timestamp, service, level, message, trace_id, span_id, parent_span_id, source_instance_id, log_id spans: timestamp, service, name, kind, status_code, status_message, trace_id, span_id, parent_span_id, source_instance_id, duration_ms metrics: metric_name, service, source_instance_id, timestamp, value Custom functions: count(), count(DISTINCT field), countIf(condition), countIf(DISTINCT field, condition), sum(field), avg(field), min(field), max(field), p50(field), p95(field), p99(field), contains(field, 'text') (case-insensitive substring match), error_rate() (percentage, 0-100), request_count(), error_burn_rate(budget), latency_burn_rate(field, threshold, budget), bucket(field, 'interval'), now(), regexp_extract(field, 'pattern' [, group]), lag(field) OVER (PARTITION BY ... ORDER BY ...). bucket(timestamp, '5m') groups by time. Intervals: <number><unit> with unit m, h, or d (e.g. 1m, 5m, 30m, 1h, 6h, 1d). For a query that selects a single aliased bucket, groups by it alone, orders by it, and has no LIMIT, interior gaps between the first and last returned bucket are zero-filled in the response (numeric columns 0, others null). Buckets outside the data range are not invented; other query shapes still return only non-empty buckets. DISTINCT is a modifier on the counting aggregates: count(DISTINCT field) counts distinct values, countIf(DISTINCT field, condition) counts the distinct values of the rows matching the condition. DISTINCT inside any other aggregate (sum, avg, p95, ...) is rejected with an error rather than ignored. regexp_extract returns the first regex match (or capture group if specified). Returns null on no match. Example: regexp_extract(message, 'status=(\d+)', 1). Burn-rate rules (declared SLO): error_burn_rate(budget) is the error share divided by your budget (0.001 = 99.9% SLO); latency_burn_rate(duration_ms, 500, 0.03) is the share of requests over 500ms divided by a 3% budget. Alert when the result exceeds a burn multiple (e.g. GT 6 over a 60-minute window). Metrics aggregation: a metric row carries one reading in its value column, so aggregate it with the ordinary functions — avg(value) for a gauge, sum(value) only where each row is already a delta. There is no rate() or value() function: a cumulative counter's rate cannot be written as one aggregate, because an aggregate cannot wrap the window function the per-point delta needs. Spell it as a subquery instead: SELECT sum(delta) / 300 AS value FROM (SELECT value - lag(value) OVER (PARTITION BY service, source_instance_id, metric_name ORDER BY timestamp) AS delta FROM metrics WHERE metric_name = 'http.server.request.count') AS deltas WHERE delta >= 0 Replace 300 with your own window in seconds and the metric name with yours. The derived table has to be aliased (AS deltas) or the outer select has no source to resolve delta against. delta >= 0 drops counter restarts. The shape is correct only where the metric carries one series per service, source_instance_id and metric_name: when attributes split it into several series, lag() steps between interleaved series and the summed rate is silently wrong. That case needs the attribute set in the PARTITION BY, which run_sql cannot express today, so pin the query to a single series in its WHERE, or use a metric alert rule, which partitions per series. This reads the metrics table directly, which does not expose temporality, so it assumes the metric is cumulative; for a delta-temporality metric sum(value) over the window is already the answer. list_metrics reports which is which. Limitations: - Read-only SELECT only (no INSERT/UPDATE/DELETE/UNION). - No CROSS JOIN (use explicit JOIN ... ON). - No SYMMETRIC BETWEEN (order the bounds and use plain BETWEEN). - JOINs require qualified field references (e.g. l.service, s.name). - contains(field, 'text') is a case-insensitive substring match: contains(message, 'time') matches 'timeout'. regexp_matches(field, 'pattern') is also substring, but CASE-SENSITIVE — 'GET' will not match 'get'. Prefix the pattern with (?i) to opt in to case-insensitive matching, e.g. regexp_matches(message, '(?i)get'). matches('text') searches message, attributes, and service together. Prefer purpose-built tools when they fit: use correlate when you have a trace id (returns spans, logs, and metric exemplars in one call), get_trace for the span tree alone, and aggregate_spans to find where errors or latency are concentrated before drilling in. Use run_sql for ad-hoc analysis that the other tools don't cover. Examples: SELECT service, count(*) FROM logs WHERE level = 'ERROR' GROUP BY service SELECT service, p95(duration_ms) FROM spans GROUP BY service SELECT bucket(timestamp, '5m') AS t, count(*) FROM logs GROUP BY t ORDER BY t SELECT http_method, count(*) FROM logs GROUP BY http_method SELECT http.response.status_code, count(*) FROM logs GROUP BY http.response.status_code SELECT s.name, l.message FROM spans s JOIN logs l ON s.trace_id = l.trace_id SELECT service FROM logs WHERE service IN (SELECT DISTINCT service FROM spans) SELECT error_burn_rate(0.001) AS value FROM spans WHERE service = 'my-svc' Time-typed columns (timestamp, bucket(...)) come back as ISO-8601 UTC strings. The response carries only rows, queryStats, and error — no link back to the Fixter UI. For a linkable log search, use the logs tool instead.

  ConnectorAPI key

• install_appA

  BorealHostA

  Install an app template on a VPS/Cloud site. Starts a background installation. Poll get_app_status() for progress. Requires: API key with write scope. VPS or Cloud plan only. Args: slug: Site identifier template: App template slug. Available: django, laravel, nextjs, nodejs, nuxtjs, rails, static app_name: Short name for the app (2-50 chars, lowercase alphanumeric + hyphens). Used as subdomain: {app_name}.{site_domain} db_type: Database type. "none", "mysql", or "postgresql" (depends on template) domain: Custom domain override (default: {app_name}.{site_domain}) display_name: Human-friendly name (default: derived from app_name) Returns: {"id": "uuid", "app_name": "myapp", "status": "installing", "message": "Installation started. Poll for progress."} Errors: FORBIDDEN: Plan does not support apps (shared plans) VALIDATION_ERROR: Invalid template, app_name, or duplicate name

  ConnectorNo auth

• upgrade_to_clusterA

  mcpA

  THE WAY TO MAKE AN EXISTING APP HIGHLY AVAILABLE. If the user asks for high availability, redundancy, no single point of failure, surviving a host or VM failure, or 'stay up if a machine dies', this is the tool: point it at their running VM and pass high_availability:true. It needs no rebuild and no redeploy, and the URL does not change. Turns a single running instance into a horizontally-autoscaling cluster: snapshots the instance and puts your SOURCE VM behind a load balancer as its ALWAYS-ON BASELINE MEMBER, then an autoscaling group adds EXTRA members (booted from that snapshot) on CPU load and scales them back down to zero. FLAVOR — LEAD WITH A MEMORY-OPTIMIZED SMALL-DISK FLAVOR (m1.mem16/m1.mem32) for the cluster, and ideally have the SOURCE VM on one too: clustering snapshots the source VM's FULL disk, so DISK size (not RAM) drives how long the cluster takes to build and to add each member under load — a 40 GB mem flavor is several times faster to snapshot and scale than a 160 GB standard flavor (m1.xlarge) and costs less for the same RAM. If the source VM is on a big-disk flavor, RECOMMEND migrating the app onto a mem-flavor VM first (a root disk cannot be shrunk in place), even though it is extra work — it pays off on every scale-out, and a clustered app is stateless anyway (state in the managed DB) so the lean disk fits. Expect a DRASTICALLY better clustering experience on a mem flavor: far faster cluster-create, snapshots, and every scale-out. The user can override with any flavor. At rest ONLY your source VM serves — there is NO idle extra VM to pay for (the source VM is the cluster's minimum, so the floor is 0 extra members). Use it to LOAD-SCALE a stateless app tier while managed services hold state: it becomes highly-available UNDER LOAD (multiple members behind the LB), but at rest a SINGLE source VM serves — and that source VM is a plain VM, not an autoscaled member, so it is NOT auto-replaced if it fails while idle (only the autoscaled extra members are ASG-managed and self-healed). If you need always-on redundancy, keep the app under enough load to hold >=1 extra member, or use a separate always-on setup. BILLED — at rest it costs just your source VM (which you already run) plus the load balancer; under load it adds up to max_size EXTRA members at the member flavor (flavor_id), billed only while they run. In guided mode show the cost that way (now: source VM already running + the LB; under load: up to max_size x the member flavor) and get the user's explicit go first. redu automatically repoints the extra members from the old single-VM URL to the load-balancer URL across app config. It REFUSES a STATEFUL VM with 409 cluster_needs_stateless unless confirm_stateless:true. To have redu FIX a stateful VM for you instead of refusing, pass auto_restructure:true — for a single_vm Postgres it fully-automatically provisions a managed DB + migrates the data + repoints the members; for a compose-stack DB it provisions the matching managed DB (set restructure_engine, e.g. 'mysql'/'mariadb' for WordPress) and returns migration commands to run from the app VM. WordPress/WooCommerce is not generic autoscaling: managed DB alone is not enough because wp-content/uploads is file state. Use app_profile:'wordpress'/'woocommerce', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true so all members mount the same uploads filesystem; otherwise the backend refuses with 409 cluster_needs_media_space. PUT THE CLUSTER ON THE SAME private network as the managed DB and media space. HA: cluster members are spread across DIFFERENT physical hosts automatically, and an autoscaled member that is destroyed is REBUILT AUTOMATICALLY in 1.5 to 5 minutes depending on how it failed with no action from you (the always-on source/hero VM is a plain VM and is NOT covered by that). CRITICAL for members: the app must start on EVERY boot (systemd unit or container restart policy) - if it only starts from a first-boot cloud-init script, a rebooted or resized member comes back with no app, silently never rejoins the load balancer, and the cluster quietly loses capacity with nothing reporting an error. Pass startup_command if the app does not already auto-start on boot, and have it bind its port only once it is genuinely ready to serve (the health check can only see whether the port is open). SEQUENCING - this catches people: the snapshot is taken IMMEDIATELY, and every member boots from it, so the source VM's app must already be RUNNING before you call this. Clustering a freshly-created VM whose cloud-init has not finished captures an image with no enabled service, and all members then come up ACTIVE while failing the load-balancer health check forever - a cluster that looks built and serves nothing. Verify the app answers on its port first (get_ssh_command, or just fetch the VM's URL). The snapshot upload can take several minutes; poll list_clusters until CREATE_COMPLETE.

  ConnectorNo auth

• create_relational_databaseA

  mcpA

  Provisions a managed MySQL (or MariaDB) database on a dedicated VM on your private network — the relational-database resource (use this instead of create_database when the app needs MySQL/MariaDB, e.g. WordPress, NextCloud, Matomo, many PHP/LAMP apps). Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' with db_engine mysql/mariadb so plan_deploy includes and wires the DB automatically. It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP (port 3306), not a public address. Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_relational_databases until status='ready', then the connection details (private_ip, port 3306, db_name, db_user) are populated. MySQL is created with mysql_native_password auth so older clients/apps connect cleanly. (ClickHouse is a separate resource — use create_clickhouse / list_clickhouse_databases.)

  ConnectorNo auth

• deploy_composeA

  mcpA

  Deploys a MULTI-CONTAINER app — a repo that ships docker-compose.yml / compose.yaml — onto ONE VM via podman-compose, and exposes one or more services at redu.cloud URLs. Use this instead of deploy_app when the repo is a compose stack. Same prereqs + source modes as deploy_app; always run plan_deploy first. PORT is the HOST port for the exposed service. DB: 'compose' uses the stack's own db container; 'managed' provisions a separate managed Postgres/MySQL/MariaDB VM and appends connection env. For WordPress/WooCommerce cluster intent, do not leave the compose db service/local uploads as state: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu writes an override file that points the WordPress service at managed DB env and mounts the media space into /var/www/html/wp-content/uploads. Poll get_deployment until ready.

  ConnectorNo auth

• query_sqlA

  ThinAir DataA

  Execute a read-only SQL query against the target connection. ONLY SELECT / WITH / EXPLAIN permitted. Write dialect-appropriate SQL for the connection's engine — use PostgreSQL syntax for postgres connections (`SELECT NOW()`, `LIMIT`, `ILIKE`), T-SQL for mssql (`SELECT GETDATE()`, `TOP N`, `LIKE`), MySQL for mysql (`SELECT NOW()`, `LIMIT`). Response meta includes `connection` + `dialect` so you know which syntax worked; reuse that dialect in follow-up calls. Default LIMIT 100 unless the user asks for all rows.

  ConnectorOAuth

• create_userA

  mcpA

  Create a database user for a Cloud SQL instance. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * When you use the `create_user` tool, specify the type of user: `CLOUD_IAM_USER`, `CLOUD_IAM_SERVICE_ACCOUNT`, or `BUILT_IN`. * By default the newly created user is assigned the `cloudsqlsuperuser` role, unless you specify other database roles explicitly in the request. * You can use a newly created user with the `execute_sql` tool if the user is a currently logged in IAM user. The `execute_sql` tool executes the SQL statements using the privileges of the database user logged in using IAM database authentication. The `create_user` tool has the following limitations: * To create a built-in user with password, use the `password_secret_version` field to provide password using the Google Cloud Secret Manager. The value of `password_secret_version` should be the resource name of the secret version, like `projects/12345/locations/us-central1/secrets/my-password-secret/versions/1` or `projects/12345/locations/us-central1/secrets/my-password-secret/versions/latest`. The caller needs to have `secretmanager.secretVersions.access` permission on the secret version. * The `create_user` tool doesn't support creating a user for SQL Server. To create an IAM user in PostgreSQL: * The database username must be the IAM user's email address and all lowercase. For example, to create user for PostgreSQL IAM user `example-user@example.com`, you can use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance":"test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user@example.com`. To create an IAM service account in PostgreSQL: * The database username must be created without the `.gserviceaccount.com` suffix even though the full email address for the account is`service-account-name@project-id.iam.gserviceaccount.com`. For example, to create an IAM service account for PostgreSQL you can use the following request format: ``` { "name": "test@test-project.iam", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `test@test-project.iam`. To create an IAM user or IAM service account in MySQL: * When Cloud SQL for MySQL stores a username, it truncates the @ and the domain name from the user or service account's email address. For example, `example-user@example.com` becomes `example-user`. * For this reason, you can't add two IAM users or service accounts with the same username but different domain names to the same Cloud SQL instance. * For example, to create user for the MySQL IAM user `example-user@example.com`, use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user`. * For example, to create the MySQL IAM service account `service-account-name@project-id.iam.gserviceaccount.com`, use the following request: ``` { "name": "service-account-name@project-id.iam.gserviceaccount.com", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `service-account-name`.

  ConnectorNo auth

• update_userA

  mcpA

  Update a database user for a Cloud SQL instance. A common use case for the `update_user` is to grant a user the `cloudsqlsuperuser` role, which can provide a user with many required permissions. This tool only supports updating users to assign database roles. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * Before calling the `update_user` tool, always check the existing configuration of the user such as the user type with `list_users` tool. * As a special case for MySQL, if the `list_users` tool returns a full email address for the `iamEmail` field, for example `{name=test-account, iamEmail=test-account@project-id.iam.gserviceaccount.com}`, then in your `update_user` request, use the full email address in the `iamEmail` field in the `name` field of your toolrequest. For example, `name=test-account@project-id.iam.gserviceaccount.com`. Key parameters for updating user roles: * `database_roles`: A list of database roles to be assigned to the user. * `revokeExistingRoles`: A boolean field (default: false) that controls how existing roles are handled. How role updates work: 1. **If `revokeExistingRoles` is true:** * Any existing roles granted to the user but NOT in the provided `database_roles` list will be REVOKED. * Revoking only applies to non-system roles. System roles like `cloudsqliamuser` etc won't be revoked. * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * If `database_roles` is empty, then ALL existing non-system roles are revoked. 2. **If `revokeExistingRoles` is false (default):** * Any roles in the `database_roles` list that the user does NOT already have will be GRANTED. * Existing roles NOT in the `database_roles` list are KEPT. * If `database_roles` is empty, then there is no change to the user's roles. Examples: * Existing Roles: `[roleA, roleB]` * Request: `database_roles: [roleB, roleC], revokeExistingRoles: true` * Result: Revokes `roleA`, Grants `roleC`. User roles become `[roleB, roleC]`. * Request: `database_roles: [roleB, roleC], revokeExistingRoles: false` * Result: Grants `roleC`. User roles become `[roleA, roleB, roleC]`. * Request: `database_roles: [], revokeExistingRoles: true` * Result: Revokes `roleA`, Revokes `roleB`. User roles become `[]`. * Request: `database_roles: [], revokeExistingRoles: false` * Result: No change. User roles remain `[roleA, roleB]`.

  ConnectorNo auth

• deploy_appA

  mcpA

  Deploys an app to a VM and exposes it at a public https://<name>-<id>.redu.cloud URL. The container is built ON the VM. PREREQS — run check_deploy_prerequisites first for network_id + keypair_name, then plan_deploy for cost approval. Source can be git repo or prepare_upload source_token. PORT must be the real app listen port. To wire a DB, pass database:'managed' (dedicated managed datastore VM on the same private network, reused on same-name redeploy) or database:'single_vm' for Postgres on the app VM. Choose db_engine ('postgres' default; 'mysql'/'mariadb' for WordPress/Matomo/LAMP, managed only). For WordPress/WooCommerce cluster intent, do not use generic stateless deploy: pass app_profile, cluster_target:true, database:'managed', db_engine:'mariadb' or 'mysql', cluster_media_mode:'media_space', and either media_space_id or create_media_space:true. Redu mounts the media space into wp-content/uploads and refuses unsafe local uploads. Build+provision takes minutes; poll list_deployments/get_deployment.

  ConnectorNo auth

• transpile_sqlA

  netcafe-devkitA

  Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.

  ConnectorNo auth

• list_connectionsA

  ThinAir DataA

  List every database connection registered for your tenant: name, id, dbType (postgres / mysql / mssql), createdAt. Flags duplicate names — only the first-added connection of a duplicate name is reachable by name. Returns nothing sensitive (no DSN, no credentials).

  ConnectorOAuth

• create_diagramA

  FlowgrafA

  Create a NEW architecture diagram from a graph that YOU author, and get back a shareable, editable canvas URL plus a rendered SVG and Mermaid. You produce only the SEMANTICS — nodes, the groups (VPC/cluster/...) they live in, and the directed edges between them. You do NOT lay anything out: never send x/y/position/pinned. A deterministic layout engine computes all geometry and an icon layer picks the pictures from each node's kind. kind.catalog is one of aws | gcp | azure | k8s | saas | generic, each with rich per-catalog kind.types (e.g. aws:lambda, gcp:bigquery, azure:cosmos_db, k8s:deployment, saas:kafka): - "aws" (api_gateway, lambda, s3, rds, dynamodb, sqs, bedrock, kinesis, fargate, eventbridge, aurora, ...). - "gcp" (compute_engine, gke, cloud_run, cloud_sql, spanner, firestore, bigquery, pubsub, dataflow, vertex_ai, ...). - "azure" (virtual_machine, aks, app_service, functions, blob_storage, sql_database, cosmos_db, service_bus, event_hubs, key_vault, ...). - "k8s" (pod, deployment, statefulset, daemonset, job, cronjob, service, ingress, configmap, secret, hpa, ...). - "saas" for hosted third-parties (redis, postgresql, mysql, mongodb, kafka, stripe, twilio, auth0, github, cloudflare, ...). - "generic" primitive when nothing branded fits: service, database, cache, queue, user, external_system, storage, gateway, function, note. - "generic" FLOWCHART kinds for processes/flowcharts: process, decision, terminator, data, document, subprocess. edge.kind is one of: request, response, async_event, data_flow, dependency, network, generic. WORKED EXAMPLE — a user hitting an API in a VPC that talks to Postgres: { "title": "Web API", "domain": "cloud_architecture", "graph": { "groups": [{ "id": "g_vpc", "label": "VPC", "type": "vpc" }], "nodes": [ { "id": "n_user", "label": "User", "kind": { "catalog": "generic", "type": "user" } }, { "id": "n_api", "label": "API", "kind": { "catalog": "aws", "type": "api_gateway" }, "parentId": "g_vpc" }, { "id": "n_db", "label": "Postgres", "kind": { "catalog": "aws", "type": "rds" }, "parentId": "g_vpc" } ], "edges": [ { "id": "e1", "source": "n_user", "target": "n_api", "kind": "request" }, { "id": "e2", "source": "n_api", "target": "n_db", "kind": "data_flow" } ] } } Returns { diagramId, url, svg, mermaid, version }. Give the user the url — opening it shows the same diagram on an editable canvas (anonymous; it's theirs to claim by signing in). To change the diagram afterwards, use get_diagram then edit_diagram.

  ConnectorNo auth

• add_connectionA

  ThinAir DataA

  Get a secure one-time link to register a new database connection with ThinAir Data (postgres, mysql, or mssql). This tool does NOT take a connection string as input — you'll open the returned link and paste the connection string into a secure web form; it is never sent through chat. The response includes `connection_string_format` and `auth_note` for the chosen dialect — surface both to the user verbatim. IMPORTANT for mssql: Azure SQL uses Microsoft Entra, so the connection string is HOST/DATABASE only (no credentials) and the tenant/client/secret go in the form's separate fields — never construct or suggest an `mssql://CLIENT_ID:CLIENT_SECRET@host` string (client secrets break URL parsing).

  ConnectorOAuth

• cross_db_queryA

  ThinAir DataA

  ⚠️ SQL MUST BE VALID IN EVERY DIALECT YOU TARGET — stick to ANSI-ish SELECT syntax when mixing pg/mysql/mssql. `SELECT TOP 10` (mssql) or `LIMIT` (others) will fail on the wrong side. Run the same query across 2-4 connections in parallel; returns per-connection rows + errors for diffing. Canonical use cases: regional compare (`['mssql-reporting-us', 'mssql-reporting-eu']`), cross-dialect sync check (`['prod-postgres-fleet', 'prod-mysql-app']`), 3-env drift, 4-region compare. Resolve every connection name via `list_connections` first; tool fails per-connection on unknown names. ARCHITECT-tier cap: 4 connections; https://www.thinair.co/ for unlimited. [ARCHITECT tier]

  ConnectorOAuth

• data_queryA

  termalin-webA

  Run a database query on one of your servers — passwordless. It executes the engine's own client on the host over Termalin's keyless tunnel, using the database's local trust (Postgres peer auth via `sudo -u postgres`, MySQL/MariaDB unix-socket via `sudo mysql`, redis-cli, mongosh, sqlite3) — so no database password is needed or stored anywhere. Read-only by default: only SELECT/SHOW-style statements run unless allowWrites is set (full-access keys only). SQL engines return CSV/TSV with a header. For MongoDB pass a shell expression, e.g. db.products.find({}).limit(20).toArray().

  Connector

  Destructive

  No auth

• list_relational_databasesA

  mcpA

  Lists your managed MySQL/MariaDB databases (the relational-database resource). Each row carries its engine ('mysql'|'mariadb'); once status is 'ready' it has the private-network connection details (private_ip, port 3306, db_name, db_user).

  ConnectorNo auth

• delete_relational_databaseA

  mcpA

  Deletes a managed MySQL/MariaDB database and its underlying VM. Pass the numeric id from list_relational_databases. This cannot be undone.

  Connector

  Destructive

  No auth

• set_managed_backupsA

  mcpA

  Turns nightly AUTOMATED (scheduled) backups ON or OFF for a MANAGED data service — the toggle that create_backup (a one-off volume snapshot) is NOT. Once enabled, redu's nightly job backs the service up on its own and prunes to the retention window; see them with list_backups and recover with the service's restore. Works for managed Postgres/MySQL/MariaDB/Redis/Qdrant/ClickHouse and can be flipped ANY time after provisioning, not only at create. Requires a card (automated backups are a paid feature; no-card trials cannot enable them). Pass the service type + its numeric id, enabled, and optional retention (days).

  ConnectorNo auth

• list_lease_targetsA

  seekrit — secrets for agentsA

  List the registered temporary-access targets — the databases (Postgres, MySQL, …) against which short-lived credentials can be minted on demand instead of storing a standing password. Use it to see what can be leased before minting one (create_pg_lease / create_mysql_lease run on the LOCAL crypto plane), and see list_leases for what has actually been issued. Returns [{ id, orgId, provider, name, config, createdAt }], where `provider` names the kind of target (postgres, mysql, …) and `config` is non-secret connection settings.

  ConnectorNo auth