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568,626 tools. Updated 2026-09-14 21:43

"sql server" matching MCP tools:

  • 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).
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  • 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.
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  • 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.
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  • 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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  • 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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  • 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.
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Matching MCP Servers

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    Enables AI agents to interact with SQL Server databases for debugging and data issue resolution, providing tools for query execution, schema inspection, and diagnostics.
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    10 npm
    MIT

Matching MCP Connectors

  • 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.
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  • 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.
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  • Initiates the creation of a Cloud SQL instance. * The tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * The instance creation operation can take several minutes. Use a command line tool to pause for 30 seconds before rechecking the status. * After you use the `create_instance` tool to create an instance, you can use the `create_user` tool to create an IAM user account for the user currently logged in to the project. * IMPORTANT: Set `ipv4_enabled` to 'false' if creating a Private Service Connect or a Private Service Access instance. * Set `free_trial` to 'true' to create a free trial instance. Free trial instances let you test majority of Cloud SQL features for up to 30 days without financial commitment. Subject to eligibility and availability. * The value of `data_api_access` is set to `ALLOW_DATA_API` by default. This setting lets you execute SQL statements using the `execute_sql` tool and the `executeSql` API. Unless otherwise specified, a newly created instance uses the default instance configuration of a development environment. The following is the default configuration for an instance in a development environment: ``` { "tier": "db-perf-optimized-N-2", "data_disk_size_gb": 100, "region": "us-central1", "database_version": "POSTGRES_18", "edition": "ENTERPRISE_PLUS", "availability_type": "ZONAL", "tags": [{"environment": "dev"}] } ``` The following configuration is recommended for an instance in a production environment: ``` { "tier": "db-perf-optimized-N-8", "data_disk_size_gb": 250, "region": "us-central1", "database_version": "POSTGRES_18", "edition": "ENTERPRISE_PLUS", "availability_type": "REGIONAL", "tags": [{"environment": "prod"}] } ``` The following instance configuration is recommended for SQL Server: ``` { "tier": "db-perf-optimized-N-8", "data_disk_size_gb": 250, "region": "us-central1", "database_version": "SQLSERVER_2022_STANDARD", "edition": "ENTERPRISE", "availability_type": "REGIONAL", "tags": [{"environment": "prod"}] } ```
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  • 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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  • List running processes on an agent-managed device — live read via the agent tunnel. Wraps POST /api/getDeviceProcesses (permission: devices). Returns rows as reported by GETPS: (process id, name, parent, memory, etc. — exact shape depends on the agent version). Common diagnostic patterns: pair with agent_services to answer 'is the SQL Server service running but stuck?'; correlate top memory/cpu processes with eventlog_search criticals. Read-only by design — the process-kill endpoint (KILLPS) is deliberately NOT exposed via mcpmond. Server-side timeout is 60s; expect 400 if the device is offline or not enrolled. Example: agent_processes({device_id: 42})
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  • List registered tables in a DataCanvas session — schema, row count, and column names. Shows what datasets are available for SQL queries via socrata_dataframe_query. Only meaningful when CANVAS_PROVIDER_TYPE=duckdb is set. Use after socrata_query_dataset spills a large result set to canvas.
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  • Run a read-only SQL SELECT against water data tables staged on a DataCanvas by water_get_series or water_find_sites. Workflow: run water_get_series or water_find_sites (get canvas_id + table_name) → water_dataframe_describe (confirm the table and its columns) → water_dataframe_query (SQL analysis). Only SELECT statements are permitted. At most 10,000 rows are returned; a query matching more is capped and the response sets truncated=true — scope with WHERE/LIMIT, and use SELECT COUNT(*) or water_dataframe_describe to learn the true match count. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.
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  • List the tables and columns staged on a DataCanvas so you can write valid SQL for openaq_dataframe_query without guessing column names. Returns each measurement table (measurements_<sensorId>) with its row count and column names. Requires DataCanvas to be enabled.
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  • 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 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 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.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • 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.
    ConnectorNo auth
  • 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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  • Fetch configuration and metadata for the current Postmark server, including name, color, delivery settings, bounce/spam threshold, and message stream settings.
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