"Connecting to PostgreSQL database, retrieving table schema, and executing SQL queries" matching MCP tools:

• scalix_db_text_to_sqlA

  Scalix CloudA

  Translate a plain-language question into a candidate SQL query using pattern-matching against the live schema (no AI model — simple questions only: counts, averages, filtered selects on a named table). Returns the SQL without executing it, with a confidence score; low confidence means the table was guessed. Review the statement and tables_used, then run it with scalix_db_query. For complex questions, read scalix_db_schema and write the SQL directly.

  ConnectorNo auth

• import_schemaA

  SeedBase Test DataA

  Import a database schema into a project from pasted content: SQL DDL (CREATE TABLE …, raw pg_dump/mysqldump schema output works), SQL INSERT dumps, CSV/TSV, JSON, or ORM model code (Django, Prisma, SQLAlchemy, …). Replaces the project's current schema.

  Connector

  Destructive

  No auth

• get_ddlA

  SeedBase Test DataA

  Get a project's schema as CREATE TABLE statements. Accepts a project id or name and an optional SQL dialect (postgresql, mysql, sqlite, mssql).

  ConnectorNo auth

• sql_preflightA

  vrsaiA

  Call this immediately before an autonomous agent, coding assistant, or automated pipeline executes PostgreSQL write, DDL, or administrative SQL against a production-capable connection. It parses the complete statement set with a real PostgreSQL AST parser and prevents the costly mistake of an agent silently deleting, truncating, or dropping data it should not have touched. It detects unbounded DELETE and UPDATE (no WHERE clause), TRUNCATE, destructive DROP and CASCADE, mutation of caller-declared protected relations, and availability-sensitive DDL such as a non-concurrent CREATE INDEX or hazardous ALTER TABLE change. It returns a deterministic PROCEED, REVIEW, or BLOCK decision with stable reason codes, a per-statement inventory, and an explicit coverage report of exactly what was and was not assessed — parse failure and unparseable or unmodeled syntax always fail closed to REVIEW, never PROCEED. PostgreSQL is the only supported dialect in this version. It does not apply when executing SQL or connecting to a database. Paid invocation: 50000 micro-USD per successful execution.

  Connector

  Destructive

  No auth

• lookup_stream_objectA

  mcpA

  Lookup a stream object by its source object identifier. **Parameters:** * The 'parent' parameter is the name of the stream in the form: 'projects/{project name}/locations/{location}/streams/{stream name}', for example: 'projects/my-project/locations/us-central1/streams/my-stream'. * The 'source_object_identifier' parameter is the source database object identifier. Different source databases have different identifier formats. Examples: * Oracle, PostgreSQL, SQL Server and Spanner databases the identifier is 'schema' and 'table'. * MySQL databases the identifier is 'database' and 'table'.

  ConnectorNo auth

• socrata_find_datasetsA

  socrata-mcp-serverA

  Search for datasets across all Socrata-powered government open-data portals, or scope to one portal with the domain parameter. Returns dataset IDs, names, abbreviated column lists, domains, and update timestamps. Use socrata_get_dataset to fetch the full typed column schema before writing queries — columnNames here are preview-only and lack type information.

  ConnectorNo auth

• treasury_query_datasetA

  treasury-fiscaldata-mcp-serverA

  Query any Treasury Fiscal Data endpoint by path, field list, filters, sort, and page. Call treasury_list_datasets first to get the correct endpoint path and exact field names — a typo in either causes a 400. Filter syntax: each condition is { field, operator, value } where operator is eq/gt/gte/lt/lte/in (e.g., record_date:gte:2024-01-01). Multiple conditions are ANDed together. All response values are strings per the API contract, including numbers and dates; "null" (string) means no value. Supply canvas_id to stage the page result as a DataCanvas table — read its column schema with treasury_dataframe_describe, then run SQL over it with treasury_dataframe_query (requires CANVAS_PROVIDER_TYPE=duckdb on the server).

  ConnectorNo auth

• vibekit_db_queryA

  VibeKitA

  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

• get_db_schemaA

  FinBridgeA

  Inspect the schema of the local finbridge database (SQLite with ingested KR/US company fundamentals, filings, and daily prices): tables, views, columns, per-table row counts (counted in the background and refreshed every 30 minutes; null with rows_note "counting…" right after a server start), and ready-to-run example queries for query_db. Read this before writing a query_db statement. It returns no company data itself — get_db_schema describes the tables, query_db runs the SELECT. Args: (none) Returns: {tables: [{name, columns: [{name, type}], rows}], views: [{name, columns: [{name, type}]}], examples: [sql_string]} Key objects: - companies: KR companies have source='dart' + stock_code (6-digit), US companies source='edgar' + ticker - financials: one row per company x fiscal_year x quarter (quarter=0 = annual); raw unscaled KRW/USD amounts - prices_daily: daily OHLCV per company_id - views v_financials (financials joined with company name/ticker/stock_code) and v_latest_annual (latest annual row per company) — prefer these in query_db Examples: - Call before writing SQL for query_db, to learn table/column names. - Check row counts to see how much data the nightly ingest has loaded. Use when: preparing a query_db, or checking ingest coverage. Don't use for live market data (use the dart_/edgar_/fred_/crypto_ tools). Errors: 'database has not been built yet' — the ingest pipeline has not run on the server.

  ConnectorAPI key

• add_columnA

  DockA

  Append a single column to a workspace's table schema. Position is auto-computed as next-after-max so the contiguity invariant holds. Key collision (409) if a column with the same key already exists. Editor role required. Use this for per-column additions; use get_workspace_schema + update_workspace_columns (PUT on /columns) for full schema replacement or reordering. Multi-surface workspaces accept `surface_slug` to target a specific table sheet (use `list_surfaces` to enumerate); omit to fall through to the workspace's primary table surface.

  ConnectorNo auth

• eia_query_routeA

  eia-energy-mcp-serverA

  Fetches data from a leaf route with optional facet filters, date range, frequency, and column selection. Use eia_describe_route first to discover valid facet IDs, facet values, column IDs, and frequency codes. Data values are strings in the response (EIA API returns all numeric values as strings, e.g. "9.13"); cast to DOUBLE in SQL when arithmetic is needed. Returns a preview inline and stages nothing by default — one upstream request, whatever total says. Pass stage: true to also page past the preview and stage the accumulated set as a DataCanvas table, then pass the returned dataset name to eia_dataframe_query for SQL. Every dataset a tenant stages lands in the same canvas, so tables from different routes cross-join by name with nothing to thread between calls.

  ConnectorNo auth

• treasury_get_debtA

  treasury-fiscaldata-mcp-serverA

  Fetch national debt (Debt to the Penny) — total public debt outstanding broken into publicly-held debt and intragovernmental holdings. Three modes: "latest" returns the most recent business day's record; "date" returns the record for a specific date (must be a business day — the API only records debt on days markets are open); "series" returns a date range, staging the full result as a DataCanvas table when canvas_id is set or the range matches more than 500 rows — read the table's column schema with treasury_dataframe_describe, then run SQL over it with treasury_dataframe_query. Records go back to 1993-04-01.

  ConnectorNo auth

• list_databasesA

  shiplyB

  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

• detect_form_fieldsA

  Extend MCPA

  Detect the fillable fields in a PDF form and return an edit schema (edit group): each property is a detected field with its type (text/checkbox/radio/dropdown/signature/table), page index, and bounding box. Use it to see what a form asks for, or as the scaffold for a structured fill — set extend_edit:value on each field and pass the populated schema to edit_pdf. Pass inputSchema to re-run detection against an existing schema (mapping mode) — for the edit-schema rules, call get_documentation with https://docs.extend.ai/editing/configuration.md first. Inspect runs with get_form_detection_run. Follow any llmContext guidance included in results.

  ConnectorOAuth

• query_dbA

  FinBridgeA

  Run a single read-only SELECT query against the local finbridge database (ingested KR/US fundamentals, filings, daily prices). The statement must start with SELECT or WITH; multiple statements, PRAGMA, and any write/DDL keywords (INSERT/UPDATE/DELETE/DROP/ALTER/CREATE/ATTACH/...) are rejected. The query is executed as SELECT * FROM (<your sql>) LIMIT <limit> on a read-only connection. The escape hatch for questions no dedicated tool answers — Japan, Taiwan and Europe are largely reachable only this way. Prefer screen_companies for ordinary fundamental screens (it handles per-market period and currency rules that a hand-written query will get wrong), and call get_db_schema first for the table shapes. Args: - sql: one SELECT (or WITH ... SELECT) statement. A single trailing ';' is tolerated. - limit: max rows returned, 1-500 (default 50) - response_format: 'markdown' (default, table) or 'json' (compact) Returns: {columns: [name], rows: [[cell, ...]], row_count, truncated} — truncated=true means more rows matched than 'limit'. Examples (v_financials / v_latest_annual views are the easiest entry points): - Largest companies by latest annual revenue: "SELECT name, ticker, stock_code, fiscal_year, revenue FROM v_latest_annual ORDER BY revenue DESC LIMIT 10" - Samsung Electronics annual trend: "SELECT fiscal_year, revenue, operating_income, net_income FROM v_financials WHERE stock_code = '005930' AND quarter = 0 ORDER BY fiscal_year DESC" - KR vs US company counts: "SELECT source, COUNT(*) AS n FROM companies GROUP BY source" - Recent Samsung Electronics closes: "SELECT date, close FROM prices_daily p JOIN companies c ON c.id = p.company_id WHERE c.stock_code = '005930' ORDER BY date DESC LIMIT 20" (prices_daily holds KR, US, TW; US history starts 2023-03-28) Use when: custom aggregation/joins over ingested data that screen_companies cannot express. Don't use for anything that writes — it will be rejected — or for live quotes (use the live-source tools). Errors: non-SELECT input, ';' inside, or forbidden keywords -> rejected with the reason; unknown table/column -> SQL error with a hint to call get_db_schema first.

  ConnectorAPI key

• get_db_schemaA

  FinBridgeA

  Inspect the schema of the local finbridge database (SQLite with ingested KR/US company fundamentals, filings, and daily prices): tables, views, columns, per-table row counts (cached 5 minutes), and ready-to-run example queries for query_db. Args: (none) Returns: {tables: [{name, columns: [{name, type}], rows}], views: [{name, columns: [{name, type}]}], examples: [sql_string]} Key objects: - companies: KR companies have source='dart' + stock_code (6-digit), US companies source='edgar' + ticker - financials: one row per company x fiscal_year x quarter (quarter=0 = annual); raw unscaled KRW/USD amounts - prices_daily: daily OHLCV per company_id - views v_financials (financials joined with company name/ticker/stock_code) and v_latest_annual (latest annual row per company) — prefer these in query_db Examples: - Call before writing SQL for query_db, to learn table/column names. - Check row counts to see how much data the nightly ingest has loaded. Use when: preparing a query_db, or checking ingest coverage. Don't use for live market data (use the dart_/edgar_/fred_/crypto_ tools). Errors: 'database has not been built yet' — the ingest pipeline has not run on the server.

  ConnectorOAuth

• query_dbA

  FinBridgeA

  Run a single read-only SELECT query against the local finbridge database (ingested KR/US fundamentals, filings, daily prices). The statement must start with SELECT or WITH; multiple statements, PRAGMA, and any write/DDL keywords (INSERT/UPDATE/DELETE/DROP/ALTER/CREATE/ATTACH/...) are rejected. The query is executed as SELECT * FROM (<your sql>) LIMIT <limit> on a read-only connection. Args: - sql: one SELECT (or WITH ... SELECT) statement. A single trailing ';' is tolerated. - limit: max rows returned, 1-500 (default 50) - response_format: 'markdown' (default, table) or 'json' (compact) Returns: {columns: [name], rows: [[cell, ...]], row_count, truncated} — truncated=true means more rows matched than 'limit'. Examples (v_financials / v_latest_annual views are the easiest entry points): - Largest companies by latest annual revenue: "SELECT name, ticker, stock_code, fiscal_year, revenue FROM v_latest_annual ORDER BY revenue DESC LIMIT 10" - Samsung Electronics annual trend: "SELECT fiscal_year, revenue, operating_income, net_income FROM v_financials WHERE stock_code = '005930' AND quarter = 0 ORDER BY fiscal_year DESC" - KR vs US company counts: "SELECT source, COUNT(*) AS n FROM companies GROUP BY source" - Recent Samsung Electronics closes: "SELECT date, close FROM prices_daily p JOIN companies c ON c.id = p.company_id WHERE c.stock_code = '005930' ORDER BY date DESC LIMIT 20" (prices_daily holds KR, US, TW; US history starts 2023-03-28) Use when: custom aggregation/joins over ingested data that screen_companies cannot express. Don't use for anything that writes — it will be rejected — or for live quotes (use the live-source tools). Errors: non-SELECT input, ';' inside, or forbidden keywords -> rejected with the reason; unknown table/column -> SQL error with a hint to call get_db_schema first.

  ConnectorOAuth

• chembl_dataframe_queryA

  chembl-mcp-serverA

  Run a read-only SQL SELECT over the bioactivity rows chembl_get_bioactivities spilled to a canvas — rank, group, dedupe, and aggregate across the FULL set, not the inline preview. Reference each staged table by the name chembl_get_bioactivities returned — bioactivities for its potency_ranked view, bioactivities_null_potency for null_potency; discover the staged tables and their columns with chembl_dataframe_describe. Compute honest aggregates here (e.g. SELECT molecule_chembl_id, MEDIAN(pchembl_value) AS med FROM bioactivities WHERE standard_type = 'IC50' GROUP BY 1 ORDER BY 2 DESC). Two independent bounds apply, each reported on its own field: truncated is true when the SQL result exceeded the canvas row cap, and rendered_rows says how many of the returned rows the markdown table holds once its character budget is reached (below row_count on a wide or long result). Page past either bound with SQL LIMIT/OFFSET — append e.g. LIMIT 500 OFFSET 500 and re-call; offsets reach rows beyond the canvas row cap. Requires CANVAS_PROVIDER_TYPE=duckdb.

  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

• execute_sqlA

  flootA

  Run a WRITE SQL statement against the project's Postgres database — 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.

  Connector

  Destructive

  OAuth

• generate_queryA

  ALM X++ MCP ServerA

  WHEN: developer needs correct X++ select or T-SQL for D365 tables with proper joins. Triggers: 'X++ select', 'generate a query', 'SQL for', 'join with', 'how to query', 'générer une requête', 'write a select statement', 'select from', 'X++ query for', 'requête X++', 'écrire une select'. Generate both X++ select statements and equivalent T-SQL queries for D365 F&O tables. Uses real field names, relations, and indexes from the knowledge base to produce correct joins. Supports: field selection, multi-table joins (auto-detects relations), WHERE filters, ORDER BY, TOP/firstonly, cross-company. Also accepts natural language descriptions like 'find all open sales orders for customer 1001 with CustTable join'. [!] For multi-table joins, call find_related_objects (or get_relation_graph if the relation index is loaded) FIRST to get the correct FK relations -- this tool will then produce accurate join conditions. [!] The generated X++ is a template -- adapt it to your custom code context before using in production. Returns side-by-side X++ and SQL with explanations.

  ConnectorNo auth