"Understanding SQL Server Schema and Querying" matching MCP tools:

• 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

• 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

• execute_sqlA

  flootA

  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. By default the whole query (every statement in it) runs on one connection in one transaction that Floot opens and commits: if any statement fails, or the query times out before its COMMIT, nothing is applied. Leave BEGIN/COMMIT/ROLLBACK out of the query — a COMMIT or ROLLBACK inside it ends that transaction early, so a later failure no longer undoes what came before. Set no_transaction for statements that cannot run inside a transaction block (VACUUM, CREATE INDEX CONCURRENTLY, …); each statement then applies as it finishes. Queries are stopped after 75 seconds either way; split a longer migration into several calls. A disconnect does not stop a call that has started: if you got no result, check the schema before running it again.

  Connector

  Destructive

  OAuth

• omnarai_infoA

  omnarai-mcpA

  Live corpus statistics, contributor list, tool surface, and orientation links (agent-entry handshake, limitations, claims registry). Use this to orient before querying.

  ConnectorNo auth

• get_table_schemaA

  Mostly RightA

  One public table's columns (name, type, and any published profile such as null counts, distinct counts or ranges), its immutable version_id, and its capabilities: whether anonymous sampling, keyed querying and Parquet download are enabled, and, when the publisher declared text search, capabilities.search (spec_digest and keyword_columns for query_table's search). Private workspace tables are not available here: use query_run for sealed output evidence and compare it with the approved recipe document already in the conversation. If that document is unavailable, report the limitation; do not invent a schema or publish a dataset to inspect it. Example: {"table_id": "0f2f6bfa-4a63-4f75-9a0b-1a7d9c5b2e10"}. Read this before writing a query_table call: the column names it lists are the only ones the query grammar accepts.

  ConnectorNo auth

• brapi_dataframe_queryA

  brapi-mcp-serverA

  Run SQL across in-memory dataframes. Dataframes auto-populate when find_* tools spill (named `df_<uuid>`) — the dataframe name appears inline on every find_* response that spilled (`result.dataframe.tableName`), so the typical flow is find_* → read the name → query here. Use brapi_dataframe_describe to inspect schema and provenance for a known name. SELECT only — writes/DDL/COPY/PRAGMA/ATTACH/file-reads are rejected. Use SQL as the paging idiom: `LIMIT/OFFSET` to walk results, projection to trim columns, aggregation to summarize. Use `registerAs` to chain — the result lands as a new dataframe.

  ConnectorNo auth

• plan_datasetA

  Misata Studio: verified synthetic dataA

  See the tables, sizes and relationships the engine would build, before any rows exist. Free (no rows are made), so it is worth calling before generate_dataset on anything non-trivial: review what it understood and assumed, then adjust your request or schema before spending a real call. Args: request: Plain-English description (needs an LLM key, unless `schema`/`ddl` is also given — then it is still used to ground realism, e.g. locale and what columns mean). schema: A Misata schema dict (see the server instructions for the format). No key needed for structure. ddl: CREATE TABLE statements. No key needed for structure. Returns: route: "chat" (not a dataset request — see `reply`), "design" (the model designed the tables) or "pack" (matched a built-in shape). tables: name, estimated rows, columns, foreign keys for each table the engine would build. understanding: what the engine read the request as (business, archetype, assumptions). requirements: every specific thing the request asked for, so you can see what was understood.

  ConnectorNo auth

• list_databasesA

  Bitquery: Crypto & Blockchain DataA

  CATALOG of the databases raw SQL can reach — the SHOW DATABASES equivalent. For each database: what it holds, which tool runs SQL against it, how to see its tables, and which specialized tools answer the same questions without SQL. Call it before writing raw SQL against an unfamiliar database. Takes no parameters and answers instantly. trading_rt comes first: DEX trades, OHLC / volume bars, prices and supply for every trading chain over the last ~30 days — query it with execute_sql, browse it with list_tables, describe_table and search_columns. The per-chain databases (eth_api, bsc_api, matic_api, arbitrum_api, base_api, optimism_api, robinhood_api, arc_api, tron_api, solana, bitcoin, bitcoin_flow, ripple, ripple_flow, cardano, cardano_flow, altcoin, altcoin_flow - Litecoin, Dogecoin and Dash in one database, rows told apart by blockchain_id - bitcash, bitcash_flow for Bitcoin Cash, and stellar) hold transfers, calls, events, transactions and balances; browse them with chain_list_tables, chain_describe_table and chain_search_columns (pass the database name) and query each one with that chain's <chain>_transfers_raw_sql tool (xrp_, ada_, ltc_ / doge_ / dash_, bch_ and xlm_transfers_raw_sql for the ones above after bitcoin_flow). zcash and zcash_flow are queried with zec_transfers_raw_sql but the schema browser does not cover them: that tool's description lists their tables. stellar_flow is not browsable either - it starts in December 2023 and misses some ledgers, so query stellar instead. Address labels have no SQL access at all — use the label tools. Prefer a specialized tool whenever one answers the question: it already filters on the right keys and is far cheaper than hand-written SQL.

  ConnectorOAuth

• query_datasetA

  WITAN MarketsA

  Run one read-only SQL statement over a dataset version on the server (requires auth): DuckDB reads the version's Parquet parts as the table `records` (extra fields of an allowExtra schema are the JSON column `_extra`). Use it for counts, averages and filters instead of paging through read_dataset. Limits: versions up to 2 GiB of parts, 20 seconds, 1000 rows returned (`truncated` says if more matched). The result size counts toward your egress; a paid dataset answers with its price. A schema has no time type: times (measured_at, published_at …) are ISO 8601 text — compare them as text, or cast with measured_at::TIMESTAMP for date arithmetic.

  ConnectorOAuth

• query_sqlA

  MarketCodeA

  Run a read-only SQL statement against the property warehouse. SELECT and WITH only; reachable schemas are marts_core, marts_facts, dimensions and location. Results are capped by `max_rows` (max 5000). Use :name bound parameters via `params` rather than interpolating values into the string. This is the last resort. If a curated tool or `query_semantic` can answer the question, it will be cheaper and faster. UK only. Costs 125 credits. You are charged if the query reaches the warehouse and then times out or errors there; a query rejected before it runs — bad SQL, an unreachable schema, a column that does not exist — is free, and the error says what to change.

  ConnectorNo auth

• osv_list_ecosystemsA

  osv-advisory-mcp-serverA

  Return the supported ecosystem identifier strings for osv_query_package and osv_query_batch: every ecosystem the OSV schema names that OSV.dev accepts at query time, plus GIT, as verified on 2026-09-24. Ecosystem strings are case-sensitive exact matches — passing "pypi" instead of "PyPI" returns an error from the API. Use this tool to discover valid ecosystem strings before querying, or to verify an ecosystem identifier from a lockfile format. The list is static and may lag ecosystems added after that date.

  ConnectorNo auth

• faostat_dataframe_describeA

  faostat-mcp-serverA

  List the canvas tables (faostat_xxxxxxxx) staged by faostat_query_observations and faostat_commodity_profile, each with its source tool, the query parameters that produced it, creation/expiry timestamps, row count, and column schema. Call this before faostat_dataframe_query to discover the exact table and column names to reference in SQL. Tables are listed newest-first and paged: pass `name` to describe one table outright, or page with `offset` + `limit` — when the response reports `truncated`, pass the returned `nextOffset` to fetch the rest.

  ConnectorNo auth

• generate_sqlA

  Razi ToolsA

  Translate a natural-language request into a SQL statement. Returns JSON { sql } containing the query text and nothing else — no validation report, no complexity score, and the query is never executed or checked against a real database. It has no knowledge of your schema beyond what the request states, so table and column names are guesses unless you supply them. Review before running, especially anything that writes. Paid model call. Anonymous callers get 3 per hour per IP and are then refused with 401; signed-in callers get 15 per minute per IP. Capped at roughly 500 tokens. Answers are cached, so the same request returns the same query.

  ConnectorNo auth

• generate_sqlA

  Razi Text GenerationA

  Translate a natural-language request into a SQL statement. Returns JSON { sql } containing the query text and nothing else — no validation report, no complexity score, and the query is never executed or checked against a real database. It has no knowledge of your schema beyond what the request states, so table and column names are guesses unless you supply them. Review before running, especially anything that writes. Paid model call. Anonymous callers get 3 per hour per IP and are then refused with 401; signed-in callers get 15 per minute per IP. Capped at roughly 500 tokens. Answers are cached, so the same request returns the same query.

  ConnectorNo auth

• classify_database_modelA

  Entity EnricherA

  Start a billed analysis proposing database keys, SQL types, indexes and relationship ownership on a linked schema. Requires editor. Registration already starts the initial pass; use this after relevant edits. Incremental scope covers new properties or changed JSON types/multilingual flags; an empty scope does not rerun unchanged fields. Returns job_id: poll get_job_status and inspect get_schema's working copy. Correct proposals with property tools, then publish_schema to ship changes. Entity-level indexes and ownership choices: enricher://docs/database-sync.

  ConnectorOAuth

• fetch_database_deltasA

  Entity EnricherA

  Read the next ordered window of SQL deltas and canonical payloads for a database sync. claim=false is replayable; claim=true leases the window for 120 seconds. Apply a claimed window transactionally, including schema deltas, before ack_database_deltas. limit is also bounded by the registration's page_limit. snapshot_required pauses delivery until the replica reapplies its snapshot. No LLM call. Leasing, cursors, quarantine and snapshot handling: enricher://docs/database-sync.

  ConnectorOAuth

• describe_memory_schemaA

  AiAkiv MemoryA

  Return the memory database's tables, columns, and types. Use this when a SQL query needs the schema first. Do not use it to read stored content; it describes structure alone. Read-only and cheap. The schema itself is public information; the data in those tables stays protected by RLS. Returns: Every table with its columns and their types.

  ConnectorOAuth

• coverageA

  RestoSignalsA

  List the states RestoSignals currently has data for and how many scored venues each holds (with a count of hot leads, opening_score >= 80). Call this FIRST to see live inventory before new_openings — querying a state with no data returns nothing. Free (0 credits).

  ConnectorNo auth

• query_app_databaseA

  Roar AIA

  Run ONE read-only SQL statement against a hosted app's own PostgreSQL database, in the PostgreSQL SQL dialect (https://www.postgresql.org/docs/current/sql.html). Writes are refused by the database itself. Returns immediately with a runId; the rows are fetched with get_query_result. Table and column names come from get_app_database_schema, because the app's schema is its own, not ours.

  ConnectorOAuth

• coverageA

  RestoSignalsA

  List the states RestoSignals currently has data for and how many scored venues each holds (with a count of hot leads, opening_score >= 80). Call this FIRST to see live inventory before new_openings — querying a state with no data returns nothing. Free (0 credits).

  ConnectorNo auth

• list_sourcesA

  Agentcy - Your AI Marketing AgentA

  List available data sources and configured domains. Call this to discover which services and domains are available before querying. If exactly one domain exists, use it automatically without asking.

  ConnectorOAuth