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

• baselight_get_table_metadata

  Baselight

  Retrieve detailed schema and metadata for a specific table using Baselight format @username.dataset.table. Use this to understand table structure, column types, and constraints before writing SQL queries. Tables must be referenced in SQL with double quotes.

  Connector

• baselight_get_table_metadata

  baselight

  Retrieve detailed schema and metadata for a specific table using Baselight format @username.dataset.table. Use this to understand table structure, column types, and constraints before writing SQL queries. Tables must be referenced in SQL with double quotes.

  Connector

• get_ddl

  SeedBase Test Data

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

  Connector

• bls_dataframe_query

  bls-labor-mcp-server

  Run a single-statement SELECT against the canvas dataframes registered by bls_get_series. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected. System catalogs (information_schema, pg_catalog, sqlite_master, duckdb_*) are denied at the bridge layer — use bls_dataframe_describe to list available dataframes. Supports JOINs, aggregates, window functions, and CTEs. Optional register_as persists the result as a new dataframe with a fresh TTL for chained analysis. Canvas SQL operations consume zero BLS API quota. Requires CANVAS_PROVIDER_TYPE=duckdb.

  Connector

• lemma_get_circuit

  mcp

  Retrieve a zero-knowledge proof circuit by its circuitId via GET /v1/circuits/{circuitId}. A circuit defines the constraints that proofs must satisfy and binds to a single schema. Returns CircuitMeta { circuitId, schema, description?, inputs?, verifier?: { type: 'onchain'|'offchain', address?, chainId? }, artifact?: { location: { type: 'ipfs'|'https', wasm, zkey } } }. Use this before lemma_submit_proof to confirm the circuit's schema, public inputs, and verifier configuration. Circuits are immutable; new variants get new circuitIds.

  Connector

• structured_json_repair

  x402 JSON Repair

  Repair messy or invalid JSON (the kind LLMs and tools often emit) into clean, valid JSON, and optionally validate/coerce it against a JSON Schema. Pure deterministic compute — no network or model calls. What it fixes: trailing commas, single-quoted strings, unquoted keys, Python literals (None/True/False), NaN/Infinity, Markdown code-fence wrappers, and truncated/garbled tails. When to use: you received text that should be JSON but JSON.parse fails, or you have JSON that must conform to a specific schema and want types coerced (e.g. "36" -> 36, "true" -> true). When NOT to use: the input is already known-valid JSON and no schema check is needed. Args: - input (string, required): the raw/malformed JSON text. - schema (object, optional): a JSON Schema (draft 2020-12) to validate and coerce against. - coerce (boolean, optional, default true): coerce primitive types to satisfy the schema before validating. Returns structuredContent: { "ok": boolean, // true if valid JSON (and schema-valid when a schema was given) "data": any, // the repaired/validated JSON value; null if unfixable "changed": boolean, // true if any repair or coercion modified the input "errors": string[], // actionable messages when ok is false "repairs": string[] // description of each fix applied }

  Connector

• query_registry

  Arclan MCP Registry

  Search the Arclan registry for MCP servers. By default returns only connectable servers (active, mcp_partial, auth_gated). Use status=stdio to browse local-only servers available for installation. Use status=all to query the full index. Use production_safe=true to restrict to servers with uptime > 97% and handshake success > 95%. Use read_only=true to restrict to servers with no write or exec tools. Use this before connecting to an MCP server to check its validation status and score. After using a server, call report_server to contribute reliability data.

  Connector

• get_component

  petal-components-mcp

  Get the full schema for one petal_components component: attrs, slots, defaults, allowed values, and a working HEEx usage example. Call this every time you are about to write a tag like <.button>, <.modal>, <.table>, or <.field> so the attrs and slots match the real library instead of training-data guesses.

  Connector

• load_filing_workflow

  Shibui Finance

  Load filing workflow for SEC / EDGAR, insider trades, 8-K, Form 4, 10-K queries. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL whenever the user asks about filings, "who filed", "filed a form", filing dates, filing activity, SEC filings, EDGAR, insider trading/buys/sells (Form 3/4/5), 8-K events, 10-K/10-Q reports, ownership filings (SC 13G/13D), proxy statements, or any query involving the sec_filings table. Can be combined with other workflow tools.

  Connector

• rollback_project

  RationalBloks

  Rollback a project to a previous version. ⚠️ WARNING: This reverts schema AND code to the specified commit. Database data is NOT rolled back. Use get_version_history to find the commit SHA of the version you want to rollback to. After rollback, use get_job_status to monitor the redeployment. Rollback is useful when a schema change breaks deployment.

  Connector

• fema_search_nfip

  fema-mcp-server

  Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.

  Connector

• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 20 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

  Connector

• strale_balance

  Strale - 290+ API capabilities for AI agents

  Returns the current Strale wallet balance. Call this before executing paid capabilities to verify sufficient funds, or after a series of calls to reconcile spend. Returns balance in EUR cents (integer) and formatted EUR string. Requires an API key — returns an auth instruction if none is configured.

  Connector

• baselight_sdk_query_execute

  baselight

  Execute a SQL query on Baselight and wait for results (up to 1 minute). The query executes and returns the first 100 rows upon completion, or info about a pending query that needs more time. Use DuckDB syntax only, table format "@username.dataset.table" (double-quoted), SELECT queries only (no DDL/DML), no semicolon terminators, use LIMIT not TOP. If query is still PENDING, use `sdk-get-results` to continue polling. If totalResults > returned rows, use `sdk-get-results` with offset to paginate.

  Connector

• shippingrates_search

  ShippingRates MCP Server

  Search the ShippingRates database by keyword — matches against carrier names, port names, country names, and charge types. Use this for exploratory queries when you don't know exact codes. For example, search "mumbai" to find port codes, or "hapag" to find Hapag-Lloyd data coverage. Returns matching trade lanes, local charges, and shipping line information. FREE — no payment required. Returns: { trade_lanes: [...], local_charges: [...], lines: [...] } matching the keyword. Related tools: Use shippingrates_port for structured port lookup by UN/LOCODE, shippingrates_lines for full carrier listing.

  Connector

• eia_query_route

  eia-energy-mcp-server

  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; large result sets (total > length) spill to a DataCanvas table when canvas is enabled — use the returned canvas_id and dataset name with eia_dataframe_query for SQL analysis. Pass the same canvas_id on subsequent eia_query_route calls to accumulate multiple route results into one canvas for cross-route joins.

  Connector

• list_prebuilds

  mcp-server

  Lists pre-configured reports (prebuilds) available for a connector. **What is a prebuild?** A prebuild is a standardized report maintained by Quanti for a given connector (e.g., Campaign Stats for Google Ads). It defines the BigQuery table structure (columns, types, metrics) and the associated API query. **When to use this tool:** - When the user asks "what reports are available for [connector]?" - When the user doesn't know which data or metrics exist for a connector - BEFORE get_schema_context, to explore available reports for a connector - To understand the data structure before writing SQL **Difference with get_schema_context:** - list_prebuilds → discover which reports/tables EXIST for a connector (catalog) - get_schema_context → get the actual BigQuery schema for the client project (effective data) **Response format:** Returns a JSON with for each prebuild: its ID, name, description, BigQuery table name, and the list of fields (name, type, description, is_metric). Fields marked is_metric=true are aggregatable metrics (impressions, clicks, cost...), others are dimensions (date, campaign_name...). **SKU examples**: googleads, meta, tiktok, tiktok-organic, amazon-ads, amazon-dsp, piano, shopify-v2, microsoftads, prestashop-api, mailchimp, kwanko

  Connector

• manage_schema

  mcp

  Manage the database schema: read current schema, apply changes, preview changes, and audit migration history. Actions: - "get": Get the current schema (tables, columns, indexes) and api_base - "apply": Apply a declarative schema. Diffs against current and runs the safe DDL. - "dry_run": Preview the SQL that "apply" would run, without executing - "list_migrations": List applied migrations (most recent first) Parameters by action: get: { app_id, action: "get" } apply: { app_id, action: "apply", schema, name? } dry_run: { app_id, action: "dry_run", schema } list_migrations: { app_id, action: "list_migrations" } Schema example: { tables: { posts: { columns: { id: { type: "uuid", primaryKey: true, default: "gen_random_uuid()" }, title: { type: "text", nullable: false }, author_id: { type: "uuid", references: { table: "users", column: "id", onDelete: "CASCADE" } }, created_at: { type: "timestamptz", default: "now()" } } } } } Idempotency: "apply" is safe to call multiple times. If the schema is already up-to-date, returns "Schema is up to date". Destructive operations: Require explicit opt-in via the _drop (table-level) or _dropColumns (column-level) fields. Common errors: - VALIDATION_INVALID_SCHEMA: schema format does not match the DSL - STATE_PREREQUISITE_MISSING: add _drop / _dropColumns to authorize destructive ops - QUOTA_TABLE_LIMIT: max 50 tables per app - RESOURCE_NOT_FOUND: app_id does not exist

  Connector

• update_schema

  RationalBloks

  Update a project's schema (saves to database, does NOT deploy). ⚠️ CRITICAL: Follow ALL rules from create_project: • FLAT format (no 'fields' nesting) • string: MUST have max_length • decimal: MUST have precision + scale • Use "datetime" NOT "timestamp" • DON'T define: id, created_at, updated_at • NEVER create users/customers/employees tables (use app_users) ⚠️ MIGRATION RULES: • New fields MUST be "required": false OR have "default" value • Cannot add required field without default to existing tables • Safe: {new_field: {type: "string", max_length: 100, required: false}} WORKFLOW: 1. Use get_schema to see current schema 2. Modify following ALL rules 3. Call update_schema (saves only) 4. Call deploy_staging to apply changes 5. Monitor with get_job_status NOTE: This only saves the schema. You MUST call deploy_staging afterwards to apply changes.

  Connector

• faostat_dataframe_query

  faostat-mcp-server

  Run a single-statement SELECT against the canvas tables staged by faostat_query_observations and faostat_commodity_profile (table names look like faostat_xxxxxxxx). Use this for cross-country and cross-item aggregation, GROUP BY rankings, joins, and time-series analysis over the full result set the inline preview only sampled. Standard DuckDB SQL — joins, aggregates, window functions, CTEs all work. Read-only: writes, DDL, DROP, COPY, PRAGMA, ATTACH, and external-file table functions are rejected; system catalogs (information_schema, sqlite_master, duckdb_*) are denied — list staged tables via faostat_dataframe_describe. Every row carries its data-quality `flag` (A=Official, E=Estimated, I=Imputed, …) — keep it in projections and honor it in interpretation.

  Connector