"How to interact with SQL databases" matching MCP tools:

• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 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.

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• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 23 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.

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• 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.

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• sixta_rewrite_query

  sixta-connect

  Turn a flagged anti-pattern into the safe, equivalent rewrite — no connection needed. Paste a SQL query and get ready-to-run rewrites anchored to deterministic rules: `= NULL` → `IS NULL`, `NOT IN (subquery)` → `NOT EXISTS` (NULL-safe), deep OFFSET → keyset pagination, `ORDER BY RAND()` → a keyed random sample — each with its semantics caveat spelled out. Every literal rewrite is then re-analyzed in-process and reported as 'clean' or 'still flags X', so the safe rewrite is self-checked — no need to feed it back through sixta_analyze_query. Use when the user asks 'how do I fix / rewrite this query' or after sixta_analyze_query flags a smell. Input is analyzed in memory and never stored.

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• scalix_db_text_to_sql

  Scalix Cloud

  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.

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• subjects

  Asub Ax

  Browse the ÅSUB (Statistics Åland) PxWeb subject tree. Pass a sub-path like "Statistik" or "Statistik/BE" to list folders (type "l") and tables (type "t", id ends in ".px"); omit path to list the top-level databases (Statistik, Utredning).

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• water_get_series

  usgs-water-mcp-server

  Get a daily or instantaneous time series for one USGS site and parameter over a date range, as time-ordered value records. Large sets (>500 records) return the most recent 500 with truncated=true; with DataCanvas enabled they instead spill to a canvas (canvas_id/table_name) for SQL via water_dataframe_query. Use water_find_sites and water_list_parameters to resolve inputs.

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• query_layer

  Arcgis Pittsburgh

  Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

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• data_query

  stagenth · 数据工具箱

  查询 / 过滤 / 分组聚合数据文件，返回**实际数据行（JSON）**供 AI 直接分析（1 credit/次）。 支持 CSV/TSV/JSON/NDJSON/Parquet，两种用法： · 原始 SQL（表名固定 t）：sql="SELECT 商品, sum(销量) s FROM t GROUP BY 商品 ORDER BY s DESC LIMIT 5" · 结构化（不用写 SQL）：group_by=["地区"], measures=["销售额"], agg="sum", sort_by="销售额", descending=true, limit=10 SQL 仅允许单条只读 SELECT/WITH，禁止读文件/建表/联网。结果硬上限 1000 行，超出置 truncated=True。失败自动退款。 返回 {ok, format, mode, columns, total_rows, returned_rows, truncated, rows[]}。

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• load_comparison_workflow

  Shibui Finance

  Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.

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• load_comparison_workflow

  Shibui Finance

  Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.

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• fema_dataframe_query

  fema-mcp-server

  Run a read-only SQL SELECT against a DataCanvas table staged by fema_search_nfip. Enables aggregation, GROUP BY, SUM/COUNT, time-series, and filtered analysis over the full NFIP claims result without re-fetching from the API. Call fema_dataframe_describe first to get the exact table name and column names needed for valid SQL. Only SELECT statements are allowed — DDL, DML, COPY, and file-reading functions are blocked.

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• faostat_dataframe_describe

  faostat-mcp-server

  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.

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• query_layer

  Arcgis Lakecountyil

  Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

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• query_layer

  Arcgis Tigard

  Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

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• query_layer

  Arcgis Detroit

  Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

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• query_layer

  Arcgis Victoria

  Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.

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• create_database

  shiply

  Provision a SQL database — D1 (default, free) or Neon Postgres (--postgres, developer plan). Optionally attach it to an owned site's Worker env in the same call (siteSlug); otherwise attach it later with attach_database.

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• post_data_convert

  webbersites-x402-data-api

  Deterministic data-format conversion — the shape work an LLM cannot do reliably token-by-token. POST {data, from, to}: JSON, NDJSON, CSV, TSV, or a SQL INSERT dump in; any of the same out. Handles RFC 4180 quoting (commas, quotes, newlines in values), flattens nested objects to dot-notation columns, unions ragged records into a stable column set, and parses SQL string literals with '' and \' escapes. No AI, no network — same input, same bytes, every time. ($0.005 per call, paid via x402)

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• sql_from_english

  Zambo AI Tools

  Convert plain-English database questions into working SQL queries — with explanation and optimization notes. Describe what you want to pull from your database and get production-ready SQL. Handles JOINs, aggregations, subqueries, window functions. Use when user says 'write a query to', 'get me all X where Y', 'SQL for', 'how do I query'.

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