"Semantic layer for SQL query execution with dimensions and measures" 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

• list_virtual_dimensionsA

  Costory: Your Finops MCPA

  List virtual dimensions (custom cost axes) in the organization. `virtualDimensionId` in inputs equals `id` from list/get/search. Returns id, virtualDimensionId (same as id), name, bqName, description, status, computeStatus, tags (string[] of tag names), hasPendingDraft, and optionally draftValidation.ok when includeDraftStatus is true. bqName is the immutable BigQuery/CEL field name (e.g. virtual_environment) — set once at create from the initial name and never updated, even when name changes. Always use bqName (not name) for groupBy/filterCel in query. Use query to match name/bqName/description/tag; status filters DRAFT vs COMPLETED. Newly created MCP drafts typically have status DRAFT; published VDIMs are COMPLETED. Paginate with limit (default 50, max 100) and offset. Call this before get when the user refers to a VDIM by name rather than id. EXAMPLE: "Show our environment virtual dimensions" → { query: "environment" }

  ConnectorOAuth

• execute_sqlA

  Google Big QueryA

  Run a SQL query in the project and return the result. Prefer the `execute_sql_readonly` tool if possible. This tool can execute any query that bigquery supports including: * SQL Queries (`SELECT`, `INSERT`, `UPDATE`, `DELETE`, `CREATE`, etc.) * AI/ML functions like `AI.FORECAST`, `AI.KEY_DRIVERS`, `ML.EVALUATE`, `ML.PREDICT` * Any other query that bigquery supports. Example Queries: ```sql -- Insert data into a table. INSERT INTO `my_project.my_dataset`.my_table (name, age) VALUES ('Alice', 30); -- Create a table. CREATE TABLE `my_project.my_dataset`.my_table ( name STRING, age INT64); -- DELETE data from a table. DELETE FROM `my_project.my_dataset`.my_table WHERE name = 'Alice'; -- Create Dataset CREATE SCHEMA `my_project.my_dataset` OPTIONS (location = 'US'); -- Drop table DROP TABLE `my_project.my_dataset`.my_table; -- Drop dataset DROP SCHEMA `my_project.my_dataset`; -- Create Model CREATE OR REPLACE MODEL `my_project.my_dataset.my_model` OPTIONS ( model_type = 'LINEAR_REG' LS_INIT_LEARN_RATE=0.15, L1_REG=1, MAX_ITERATIONS=5, DATA_SPLIT_METHOD='SEQ', DATA_SPLIT_EVAL_FRACTION=0.3, DATA_SPLIT_COL='timestamp') AS SELECT col1, col2, timestamp, label FROM `my_project.my_dataset.my_table`; ``` Queries executed using the `execute_sql` tool will always have the default job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field. Query Execution Behavior: * If the query completes within the synchronous timeout (default 20 seconds or custom `timeout_ms`), the tool returns `job_complete: true` and the initial result rows directly. For fast queries, `job_id` may be omitted as no persistent background job is created; no further action or polling is needed. * If the query takes longer than `timeout_ms`, the tool returns `job_complete: false` and a `job_id`. In this case, use the `get_query_results` tool with `job_id` to poll until `job_complete: true`, or use `cancel_job` to abort the running query. * You can optionally specify `timeout_ms` to configure the maximum synchronous wait time in milliseconds (defaults to 20,000 ms), and `job_timeout_ms` to enforce a hard server-side timeout after which BigQuery automatically terminates the job.

  Connector

  Destructive

  No auth

• glim_web_searchA

  glim.shA

  Semantic web search powered by Exa. Returns titles, URLs, and the top query-relevant excerpt per result. Compact text by default; pass format='json' for full structured data incl. all excerpts per result. Use glim_web_fetch(url) for full page content. Matching is semantic, so a query with no real match still returns ten nearest-neighbour results rather than zero - judge relevance from the excerpts, not from the result count.

  ConnectorNo auth

• searchA

  Costory: Your Finops MCPA

  Unified search across your entire Costory workspace — dimension values, events, alerts, dashboards (with their conditionsCel), dashboard templates, reports, virtual dimensions, and budgets. PRIMARY tool for discovering CEL field names: each dimensions result includes `dimension` (the exact CEL/groupBy name, e.g. cos_sub_account_id), `label`, and `topMatches`. Use type: ["dimensions"] to focus on dimensions only. An empty query (query: "") with type: ["dimensions"] returns every dimension with its top values — use this when you need the full field catalog before building filterCel. With a keyword, results are filtered to matching values (e.g. query: "prod" finds production values across dimensions). Use this when a user mentions a product, team, project, or service name and you need to discover where it appears in the cost data before querying. Returns matching dimension values, related events, alerts, dashboards, dashboardTemplates, reports, virtualDimensions, budgets. Virtual dimension hits include id, name, bqName (immutable query field — set at create, never changes), status, and description. Each dashboard result carries a "conditionsCel" string — the dashboard's CEL filter (empty when none) — so before calling update_dashboard you can decide whether to set "extendDashboardConditions: true" on your new widget. Budget results include id (parent budget id for URLs) and name/year; call get with the budget id to obtain the budgetVersionId needed for query. IMPORTANT: Use short, concise search terms — e.g. if the user says 'my kubernetes dashboard', just search for 'kubernetes', not the full phrase. Optional "type" array restricts results to specific entity buckets (dashboards, reports, alerts, budgets, dimensions, virtual_dimensions, events). FOLLOW-UP: After calling search, use get to fetch full details for dashboards, budgets, reports, virtual dimensions, and cost alerts by ID. For dimension values, use "query" to query data grouped by or filtered on the matched dimensions. When the user wants to add to a dashboard, use the id from the dashboards bucket as input to update_dashboard. EXAMPLES: • "List all CEL dimensions" → { query: "", type: ["dimensions"] } • "Find account-related dimensions" → { query: "account", type: ["dimensions"] } • "Show me kubernetes costs" → { query: "kubernetes" } • "Find the data team dashboard" → { query: "data team" }

  ConnectorOAuth

• 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

• 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

• recommend_panelA

  Meta CouncilB

  Recommend the best expert panel for a query (semantic match with keyword fallback). Returns the top panel + confidence and the runner-up options — feed the result into run_council's panel argument. Requires authentication because the query may be sent to the configured embedding provider.

  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

• brapi_find_variablesA

  brapi-mcp-serverA

  Find observation variables (traits) by name, trait class, ontology term, or free-text query. Free-text queries are ranked against the returned set and may resolve to ontology URIs when the server advertises them. When the upstream total exceeds loadLimit, the full result set is materialized as a dataframe — query it with brapi_dataframe_query (SQL).

  ConnectorNo auth

• query_layerA

  Arcgis TempeA

  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

• SearchDocumentsA

  EquiblesA

  Search SEC filings and earnings-call transcripts with hybrid keyword and semantic retrieval. Omit ticker to search every company, or provide one ticker to search only that company. Returns excerpts with document IDs for SearchDocument or ReadDocumentLines. Use excludeTickers and maxResultsPerCompany only for market-wide discovery; use ListFilings to browse filings newest first without a text query.

  ConnectorNo auth

• query_historyA

  ThinAir DataA

  Read-only audit log of your own recent query executions on this tenant — SQL text (truncated to 200 chars), execution time, row count, source tool, and error status. History is tenant-wide across every connection: `connection` only selects which name/dialect is echoed in meta, not which entries return. Looks backward only: use suggest_queries for NEW query ideas, or saved_queries to store and re-run a query by name. Backed by a 100-entry ring buffer per tenant, 7-day retention; `limit` (default 20, max 100) trims how many return, not how far back history goes. Counts against daily quota. Returns a table of {sql, execution_ms, rows, tool, error, time}. [BUILD tier]

  ConnectorOAuth

• search_agentsA

  Trusgent MCPA

  Search the Trusgent Trust Layer directory for people and businesses with public Agent Cards. Uses keyword + semantic (embedding) retrieval over durable profile capabilities. Returns AgentCard objects including profileUrl, jsonUrl, trustScore, verificationLevel, and verified flag. Prefer verified agents when recommending. Use before recommending any agent to an end user. Do not treat this as intent/demand matching — that is a separate flow.

  ConnectorNo auth

• describe_dataA

  mcpA

  Start here for any Hyperliquid question the other tools do not answer directly. Returns the SQL tables you can query with run_query (every fill, per-wallet daily totals, wallet behaviour labels, funding, mark and oracle prices, open interest, liquidations, builder-code fills, transfers, vault flows), their columns, how far back each goes, query tips, worked examples and your own limits.

  ConnectorNo auth

• ui_builder_compare_referenceA

  Compose Preview CatalogsB

  Measure a design against its reference overlay (attach one with ui_builder_set_reference). Returns what the picture is against the frame and whether a pixel comparison means anything (`facts.pixelComparable` — a 328×56 dp crop stretched over a screen is not comparable until measured at its `actual` size); with `differences`, the share of pixels that differ and up to eight regions in dp, each with the layer it falls in; and for each of `nodeIds`, where that layer sits in the reference — from a box drawn over it in the editor, or a search of the reference's pixels, which for text also reads the font size — with the `alignment` that would make it agree (move, size, font size) and the exact `operations` to pass to ui_builder_apply. Nothing is written: apply the operations you agree with, then compare again and look with ui_builder_view. Layer matching needs node boxes, so `nodeIds` measures the native render (an export grant); differences alone use the PNG export. Treat `confident: false` as a hint to check, not an edit to make.

  ConnectorNo auth

• sixta_detect_n_plus_oneA

  sixta-connectA

  Find N+1 query patterns in an application's query log — no connection needed. Paste an ORM/SQL trace (Rails ActiveRecord, Django, Hibernate, Prisma, or a raw SQL log) and get the query shapes that fire many times in the trace (one parent query, then the same per-row lookup repeated) with the framework-specific eager-load fix (includes / select_related / JOIN FETCH / include). Use when the user pastes app logs or asks 'why are there so many queries' / 'do I have an N+1'. Input is analyzed in memory and never stored.

  ConnectorOAuth

• search_rentals_structuredA

  arcasos-rentalsA

  Use ONLY when the query consists entirely of explicit numeric/categorical constraints with NO descriptive language (no mood, view, atmosphere, or aesthetic words). Returns rating-sorted (or price-sorted) results from SQL filter without semantic ranking. For ANY query containing descriptors like 'cozy', 'quiet', 'luxury', 'river view', 'modern', use search_rentals_natural instead — it produces better results in a single call. Returns Schema.org Accommodation format.

  ConnectorNo auth

• openfda_dataframe_describeA

  openfda-mcp-serverA

  List the tables and column schemas on a DataCanvas staged by an openFDA search tool. Call before openfda_dataframe_query to discover the exact table name, column names, and DuckDB types needed for valid SQL. row_count is the full staged result set, not the inline preview count. Columns typed JSON hold nested openFDA objects/arrays — query them with DuckDB json functions.

  ConnectorNo auth