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650,238 tools. Updated 2026-10-13 00:32
"Postgres" matching MCP tools:
- Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.ConnectorNo auth
- USE for one person's 90-year Long Range Journey theme card: birth_date + as_of_date (completed civil years; no wall clock). NOT Magian Age Spread board (get_age_spread / get_age_plate); NOT get_date sky/calendars. RETURNS compact cosmic birth card + long_range {age_civil, age_no (mod 90), age_aliases, theme_card, spread_offset, planetary_period}. One theme card only — never 90 ages. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.ConnectorNo auth
- USE for one person's 90-year Long Range Journey theme card: birth_date + as_of_date (completed civil years; no wall clock). NOT Magian Age Spread board (get_age_spread / get_age_plate); NOT get_date sky/calendars. RETURNS compact cosmic birth card + long_range {age_civil, age_no (mod 90), age_aliases, theme_card, spread_offset, planetary_period}. One theme card only — never 90 ages. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.ConnectorNo auth
- 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 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.ConnectorDestructiveNo auth
Matching MCP Servers
- FlicenseNot gradedqualityDmaintenanceEnables querying and inspecting a PostgreSQL database, including executing SQL queries, listing schemas and tables, and describing table columns.-
- AlicenseAqualityDmaintenanceA Postgres MCP server with index tuning, explain plans, health checks, and safe SQL execution for AI agents.9MIT
Matching MCP Connectors
Check a Postgres URL shape. Credentials discarded.
Postgres + Grafana Stack Generator
- List the SQL databases (D1 or Neon Postgres) on my account, including which owned site (if any) each is attached to. Call this BEFORE db_query/db_schema-style work to discover a databaseId — those live on a per-database MCP server reached via GET /api/v1/databases/{id} (see llms.txt), which this id feeds.ConnectorNo auth
- Run a READ-ONLY SQL query against the project's Postgres database (SELECT, EXPLAIN, etc.) — the Floot-managed database, or an external Postgres the user connected to the project. Writes are rejected — use execute_sql for those. Returns JSON: `{rows, rowCount, command, truncated?}` (or `{results: [...]}` for multi-statement queries). Pass `database` only if the project has more than one.ConnectorOAuth
- USE for one person's Magian Age Spread board seat: birth_date + as_of_date (completed civil years; no wall clock). NOT the 90-year Long Range Journey theme card; NOT get_date sky/calendars; NOT two-person scoring. RETURNS compact cosmic birth card + age_spread {age_civil, age_no (mod 90), age_aliases, age_spread_pos, planet_row, planet_col, is_fixed_card}. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.ConnectorNo auth
- USE for one person's Magian Age Spread board seat: birth_date + as_of_date (completed civil years; no wall clock). NOT the 90-year Long Range Journey theme card; NOT get_date sky/calendars; NOT two-person scoring. RETURNS compact cosmic birth card + age_spread {age_civil, age_no (mod 90), age_aliases, age_spread_pos, planet_row, planet_col, is_fixed_card}. Feb-29 follows Postgres age(). Error as_of_before_birth when as_of_date < birth_date.ConnectorNo auth
- Price a list of cloud components at real list prices. No account or signup. Use this when the user can name the pieces: "two m5.large servers, a Postgres database and a load balancer in Mumbai". If they cannot name the pieces, use `describe_workload` instead, which asks in units a person actually has. `items` is a list of `{"component": "<id>", "params": {...}}`. Get the ids and each component's parameters from `list_cost_building_blocks` and use them verbatim: a name you invent will be rejected, which is deliberate. `region` is an AWS region id such as `ap-south-1`. The reply is a RANGE, not a number: `monthly_total` has low, expected and high, because several inputs are genuinely uncertain and a single figure would hide that. `swing_factor` names the one input that most widens the range, which is the most useful sentence in the reply: it tells the user what to go and measure. `concentration` ranks the lines by share of the bill. `assumptions` lists every value that was defaulted rather than given, and `traps` lists costs this architecture usually incurs that the caller did not ask for, such as public IPv4 addresses or database storage billed apart from the database. `gaps` lists anything that could not be priced. It is never silently treated as zero. If a line is missing, the total is missing that line, and the gap says so. These are AWS on-demand list prices before any discount, credit or committed-use agreement, and they exclude tax. Treat the result as an upper bound on a real bill and never as a quote.ConnectorNo auth
- Export a generated dataset as a file. Returns a `download_url` the person can open (or you can fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link rather than pasting file contents into the chat. Args: dataset_id: from a prior generate_dataset call. format: data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql. code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy, typescript, jsonschema, expectations, django, openapi, mockapi, demo. `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) — the way to seed a real database: run the returned SQL through your own database connection, since this server never holds a database credential itself. dialect: for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake. inline: also return the file itself as `base64` (only for files under a few MB). Use it when you must write the file yourself and cannot fetch a URL. Returns: filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when `inline` and small enough.ConnectorNo auth
- Search and filter developer tools by category, features, and constraints. Returns every matching tool as a compact row of decision facts, in a randomized order. Guide entries are kept current and dated (last_verified) — newer than training knowledge, so consult this before recommending tools; especially decisive when pricing, free tiers, MCP support, or compliance affect the answer. Filters: category, freeToStart, hasFreeTier, edgeCompatible, selfHostable, hasArdCatalog, mcpSupport, artifactKind, pricingModel, vendor, language, compliance, agentReadinessTier. Any number combine and AND together. Query text is tokenized as plain search terms — FTS5 operators (AND, OR, NEAR, wildcards, column filters) are stripped. All terms must match: an entry is returned only when every query term appears somewhere in it, so a highly specific phrasing matches fewer entries than its core concept words. Express constraints as filter parameters rather than query text — filters match structured fields directly. Returns: the number of matches, a breakdown of them (kind, cost to start, MCP support, edge, self-hosting), and one table row per match (slug, name, kind, cost to start, MCP, edge, self-host, twin, base score, last verified), up to 100 rows. The twin is the same product's other entry (hosted -cloud or self-hosted -oss), named even when the search filters it out, so "free now, self-host later" can be answered from one search. Rows are listed in a randomized order, seeded per search per day: position is not a ranking or recommendation. Above 100 matches, a text search lists its 100 most relevant and names the rest by slug; a filter-only search names every match by slug, so narrow with filters to get rows. Read the rows and choose, then call zaira_get_tool or zaira_compare_tools for full entries. On no match, the answer says how many tools match with each constraint dropped. Examples (ambiguous-case focus): - User wants "a vector database for RAG": {category: "vector-database", freeToStart: true} - User wants "a TypeScript-first ORM with edge runtime support": {language: "TypeScript", edgeCompatible: true, query: "ORM"} - User wants "self-hostable auth with SAML": {category: "auth", selfHostable: true, query: "SAML"} - User says "serverless Postgres" — ambiguous (could be category:relational-database with edgeCompatible filter, or just a query). Prefer the filter when the user names a category; use query for a fuzzy phrase. - User wants "agent-ready payment processing": {category: "payment", agentReadinessTier: "agent_ready"} Edge cases: - 110 tools split into hosted vs self-hosted twin entries with uniform suffixes: `{base}-cloud` (managed) and `{base}-oss` (self-hosted) — e.g. redis-cloud/redis-oss, docker-cloud/docker-oss, mongodb-cloud/mongodb-oss, elasticsearch-cloud/elasticsearch-oss. Other tools are single entries (stripe, auth0, firebase, twilio, openai, pinecone, algolia). Filter by `selfHostable` or `artifactKind` to land on the right variant. - "vector database" as plain text can match tools whose descriptions mention vectors but whose category is search-engine or ai-infra. Use the `category` filter when the user wants a strict match. - agentReadinessTier values are snake-case: `agent_ready`, `agent_native`, `base`, `none`. Display labels (`Agent Ready`) will not match. `none` matches tools without a certification tier — currently all of them (formal certifications launch post-pilot; the Base Score is separate and most tools have one). - artifactKind has only two values: `open_source` and `managed_service`. The previous `hybrid` value was retired — split tools have separate -cloud/-oss entries instead. - "Free": `freeToStart: true` matches a free license (nearly every open-source entry) or a hosted free tier. `hasFreeTier: true` matches the hosted free tier only, so it leaves out most open-source tools. Open source is free to use, not free to run. Risk: read-only, closed-world, idempotent — no state change possible.ConnectorNo auth
- Answer a COUNTING question about the published firm directory in one call: how many organisations per country, per REGION (the six-value geography), per kind, per declared SAP module, or per SAP signal band — with the same `country`/`kind`/`module`/`query` filters `search_firms` takes, so you can count a slice as easily as the whole. Use this instead of paging `search_firms` and tallying rows: the directory holds thousands of organisations, and reading them all to produce a table of counts costs hundreds of calls and megabytes of rows for numbers Postgres computes in one scan. Every bucket is a value the directory actually stores; `value: null` is a real bucket meaning the field is unknown for those rows, and it is served rather than hidden — a country table that silently drops the rows with no country adds up to less than the population and says nothing about it.ConnectorNo auth
- ⚠️ 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. Cap: 4 connections per call on every plan; to compare more, split into sequential calls of ≤4 and combine. [ARCHITECT tier]ConnectorOAuth
- Run a WRITE SQL statement against the project's Postgres database (the Floot-managed one, or an external Postgres the user connected) — CREATE/ALTER TABLE, INSERT, UPDATE, DELETE, DROP, migrations. Destructive statements are allowed but your MCP client will show the user the SQL and ask them to approve it (they can allow once or for the session). Schema-changing statements (CREATE/ALTER/DROP of tables, types, …) automatically re-pull the typed schema helper and return the updated schema — no separate pull_database_schema call needed. Pass `database` only if the project has more than one. The query runs in a single transaction by default; set no_transaction for statements that cannot run inside a transaction block (VACUUM, CREATE INDEX CONCURRENTLY, …). Queries are killed after 90 seconds either way.ConnectorDestructiveOAuth
- Provision a Floot-managed backend resource for the project — fully server-side (Floot mints all secrets; no keys to paste). Also seeds the working code for it. Available: - database — A Floot-managed Postgres database (Neon). FLOOT_DATABASE_URL is set for the app. - auth — Email/password + session auth (JWT_SECRET, auto-provisions a database if none). Injects auth pages, endpoints, and helpers. - oauth-login — Sign in with Google via Floot's brokered OAuth (FLOOT_OAUTH). Injects OAuth provider classes, login buttons, helpers. - microsoft-login — Sign in with Microsoft via Floot's brokered login (FLOOT_MICROSOFT_LOGIN). Injects button + auth endpoints. - google-integration — FLOOT_GOOGLE_INTEGRATIONS is built-in Google integration. Use it to let end users connect their Google account so the app can read/write their Gmail, Google Calendar, or Google Drive — unless the user specifies they want to use their own Google client. See get_guides("floot-provided-google-integrations"). - microsoft-integration — Microsoft Graph access (Outlook/Teams/etc.) via Floot's brokered Microsoft OAuth (FLOOT_MICROSOFT_INTEGRATIONS). Injects Connect button + endpoints. - push-notifications — Web + native push (FLOOT_PUSH). Mints VAPID keys, injects helpers/pushClient (subscribe/unsubscribe) + a service worker. Enum values not listed above are beta-gated and unavailable on most accounts. SENDING email from the app is NOT a resource — the builtin @floot/email handles it with zero setup (get_guides("email")). For a user's OWN external key (their OpenAI key, an external database), this is NOT the tool — use request_external_resource instead. Idempotent: re-running returns the existing resource and skips seed files that already exist. - app-connection — let this project's backend call functions ANOTHER Floot app exports (no API keys): pass app_target and app_functions. Only apps the user can edit, only functions in the other app's helpers/flootAppExports.tsx (saved is enough: write both apps first, connect, then publish both); the user confirms in a dialog. Read get_guides("app-calls") first.ConnectorOAuth
- Request the USER'S OWN external credential for this project — their OpenAI or Anthropic API key, an external Postgres connection string (ALWAYS type POSTGRES — a Postgres credential requested as GENERIC is refused, because only POSTGRES gives the user a database: the Databases tab, helpers/db, the typed schema and query_database), or any other service's key (type GENERIC, e.g. Stripe/Resend — secret_env_vars is REQUIRED for GENERIC and the call is refused without it; a var the user can only produce LATER, like a webhook signing secret, goes in optional_env_vars so the dialog doesn't demand it up front). NOT for Floot-managed resources (database/auth/push/oauth/…) — use provision_resource for those; they need no user input. REUSE FIRST: if the project owner already has a matching credential on their account (list_resources section 2), this connects that saved credential to the project directly and returns the env var names — no link, no dialog, nothing to poll. Pass the name exactly as list_resources shows it to make that happen. Reusing a POSTGRES credential also seeds helpers/db, installs the query stack, and pulls the typed schema helper, so do NOT write those yourself afterwards. Otherwise it returns a secure connect link: SHOW it to the user (UI-capable hosts render a Connect button automatically; on terminal hosts with shell access open it in the user's default browser yourself and paste the URL as plain text) and ask them to open it. The call completes only when the user finishes the connect flow — it never expires. Do NOT block on it: request the credential EARLY, keep building everything that doesn't need the secret (the env var names are known now — reference process.env.X in code before the secret exists), and check the request between tasks; the user may never connect it, and the build must not stall. NEVER ask the user to paste a secret into the chat. On completion you get the env var names — never the secret values. Re-calling with the same type returns the same pending request. If the credential is already connected and holds every value you asked for, you get those env var names and their value SHAPES back immediately and the user is not interrupted — to reopen a dialog because a stored value is wrong, re-call with `instructions` saying what is wrong.ConnectorOAuth
- Checks whether a given pairwise ID has already been seen within this audience. It protects against one person acting as several different users (multiple accounts, repeat voting, and similar). WHEN TO USE: returning-user checks and one-human-one-vote. Requires a pairwise_id previously obtained from a verify call for YOUR audience. Only the pair (audience, pairwise_id) and the time of first occurrence are recorded. No personal data. The ledger is PERMANENT and shared across every instance and session of the server (Postgres) — it survives restarts and new MCP sessions. record_if_new=true writes atomically (no window for a concurrent write). Args: - audience (string): the service domain or URL. Normalized to a bare lowercase hostname — the same key the proof was issued under, so a domain and its URL form are the same audience. - pairwise_id (string): the identifier from xego_verify_proof. - record_if_new (boolean): record the user if they are new. Returns (JSON): { "seen_before": boolean, // true = already on record "first_seen": number|null, // Unix time of first occurrence "recorded_now": boolean // true = recorded just now } Errors (free, no sybil check performed): invalid_audience (the audience cannot be normalized to a domain), dedup_store_unavailable (temporary — retry). Neither ever means 'not seen before'. PAID TOOL (x402): this call costs $0.01 USD in USDC per execution, unless you send a valid X-EGO pilot operator key as an 'Authorization: Bearer <key>' HTTP header (operator calls are free). Calling without payment returns an x402 error whose _meta["x402/error"] contains payment requirements (accepts) and step-by-step instructions how to pay and retry. Invalid input is rejected for free before any payment is taken.ConnectorNo auth
- Deploy a multi-container application onto Cycle. You supply the app knowledge (image, command, env, ports, volumes, how many members and how they address each other); the tool turns it into a Cycle stack spec, creates and builds the stack, and deploys it into an environment. For a workload that truly needs a full virtual machine use deploy_virtual_machine. Modes: by default everything deploys through a stack (containers that version and deploy together). One application is ONE call and one stack: every container it needs — data nodes, UI, sidecars — goes in the same 'containers' list. Containers are created stopped, so "incremental" means starting and checking them one at a time afterwards, not deploying them in separate calls; a stack cannot be extended with more containers later. Separate stacks are for independent applications that release on their own. stack:false creates one-off containers directly in the environment with no stack objects. Every rule below applies to both. Existing stacks: a call with 'containers' ALWAYS creates a new stack. When the user names a stack that already exists — built by this tool, the dashboard, or from a git repo — pass stack_id instead and leave 'containers' out (or pass the original list only to link domains). It deploys the stack's latest usable build into the environment; build_id picks a specific build, rebuild:true generates a fresh build from the stack's source first. Containers the stack already has in that environment are left untouched unless 'redeploy' says to update them — rebuild:true plus redeploy:{reimage:true} is how a new image tag or commit is rolled out to a running deployment. preview:true reports the stack's recent builds, the containers the chosen build creates, and which existing ones would be updated. MUST GET RIGHT — Cycle accepts these and the application is then silently unreachable or insecure. Preview checks them and an error finding blocks the deploy: - Listen on ::. The private network is IPv6-ONLY (discovery serves AAAA records) and the load balancer reaches backends over it, so a process on 0.0.0.0 or localhost is unreachable from siblings and from the LB. IPv4-default apps need IPv6 enabled explicitly (mongod --ipv6, HOST=::), and so do client libraries (Node's ioredis needs ?family=6 on the URL). - TLS through the LB needs BOTH "443:<backend port>" and "80:80" in ports. "443:80" is Cycle notation: LB ingress 443 routes to backend 80 with TLS terminated at the LB. - Secrets never go in env or args — both are stored in plain text in the stack. Sequence: deploy (containers are created stopped) → create scoped variables with manage_scoped_variable and source.secret:true → start with cycle_control_container. First boot reads them (postgres initdb reads POSTGRES_PASSWORD once; redis needs its config file present). - Stateful containers deploy as a SINGLE instance with their own volume. Model a clustered app as N separate single-instance stateful containers (mongo-0, mongo-1, ...), each with its own volume and unique hostname. Containers reach each other by hostname; the environment must be live for discovery to resolve them. - Placement is unconstrained by default — a container may land on ANY server whose pool allows it, so cluster members can spread across providers. Pin hardware with constraints.node.tags.all (every tag present) or .any, and confirm the tag exists with list_servers BEFORE deploying; a tag matching no server leaves the container with no deployment target. - public defaults to "disable". Set it only to expose a container. Images — each container sets exactly ONE of: image (a Docker Hub target like 'mongo:7'; an existing image source with the same origin is reused), image_source (already on Cycle — prefer it whenever the user has one, it carries their registry credentials and build config), or dockerfile (repo or targz_url for Cycle to build). Registry and git credentials are never accepted directly. Backups — 'backups' (stateful containers only) has Cycle run 'command' on the cron 'schedule' inside the container and ship its STDOUT to a backup-capable hub integration ('destination', e.g. Backblaze B2); 'restore_command' reads a backup from STDIN. All four are required; the destination must already be enabled on the hub. Commands may reference env vars (mongodump -u $USER) but the tools they call must exist in the image. Domains — 'domain' exposes a container through the environment load balancer. A DNS zone covering it must already exist; the tool creates a LINKED record pointing at the container and never overwrites an existing one (conflicts are errors to resolve with the user). Forces public:"enable" and requires ports; TLS on the record follows from a '443:<port>' mapping. Cycle creates the environment load balancer only when the environment's services start with a public container present, so after deploying public containers into an environment without one the tool starts the environment's services itself; if that fails the response carries 'load_balancer_missing'. 'application' hint — pass an app name with NO containers to get recommended per-container config, topology, and caveats, then compose the container list from it. Unknown names return the known names instead of an error. Workflow: 1) call with preview:true — returns the exact spec that would deploy plus checklist findings, and makes NO changes. 2) Confirm with the user, then call again without preview. Never deploy without explicit confirmation. Asynchronous and RESUMABLE: containers appear only at the last step, so an early empty list_containers means "still working". Every response carries stack/build/job ids, 'phase', and 'recommended_action'. After a transport failure, repeat the call with the same name/deployment_id and follow 'recommended_action' ('wait', 'resume' with stack_id, or 'nothing_to_do'). A stack_id call is idempotent at every phase.ConnectorDestructiveAPI key
- Full-text search messages in a specific group. Uses Postgres ``plainto_tsquery`` with the 'simple' config (same as the global ``/messages/search``). Scoped to non-soft-deleted rows. Caller must be a member. Hits are in ``items``; ``results`` is a DEPRECATED duplicate of the same list. ``count`` is how many hits this response holds; ``has_more`` is true when more match than ``limit`` allowed.ConnectorNo auth