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524,213 tools. Updated 2026-09-06 14:24

"Databricks" matching MCP tools:

  • Search the Analytics Legends Academy — the written training modules on SAP Datasphere, Business Data Cloud, SAP Analytics Cloud, BW/4HANA and Databricks — by track, level and free text. `_meta.tranche_total_row_count` carries the live catalogue size on every call; it is the only count to quote. Returns the catalogue entry: id, slug, EN/FR title, track, level, duration in minutes, tags and the editor's summary. DO NOT CONFUSE IT WITH `list_sap_modules`, which serves a different population under the same word: that one is the 40-row PRODUCT taxonomy (codes such as SAC, DATASPHERE) used to normalise product wording. This one is the course catalogue. Without `query`, rows come back in the catalogue's own CURRICULUM order — the order a reader is meant to take them in — track by track. This catalogue is written training, NOT SAP certification tracks: this server publishes no certification data at any tier, so a certification question has no answer here rather than a partial one. CATALOGUE ONLY — the module BODY is subscriber content, served by `get_academy_module` on this same endpoint with a subscriber key (Consultant tier or above), which is the same door the €29.90 Consultant Pass opens on the site. On THIS endpoint the machine-access subscription is the MCP Pass (€39.90/month, analyticslegends.ai/pricing/), which opens the ENTIRE paid tranche from one key; the €29.90 Consultant Pass is its web-subscriber equivalent and opens the same tier floor here. `status` and `is_preview` are SERVED, never filtered on: they are the two flags the platform marks free access with, they do not coincide (measured 2026-08-16: 38 rows `status='available'`, 56 rows `is_preview`), and you decide which one your answer needs. PAGINATED: pass `_meta.next_cursor` back as `cursor` with the same filters until it is null. Read `_meta.available_tracks` and `_meta.available_levels` — both counted on the served population at call time — before assuming a facet value exists.
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  • Search the Analytics Legends market-news corpus. It is watched FOR SAP analytics (Datasphere, Business Data Cloud, SAC, BW/4HANA, Databricks, the 2027/2030 maintenance window), but it is NOT an all-SAP corpus: measured 2026-07-30, ~84 % of active rows sit in the `AI` category and are general enterprise-AI trade press (cloud platforms, model releases, funding rounds) with no SAP content at all. An UNFILTERED call therefore returns mostly non-SAP items — pass `query` or `category` when the question is about SAP, and never present an unfiltered page as 'the SAP analytics news'. Say what you actually got. Each item returns the Analytics Legends citation URL AND the upstream publisher's source_url — cite both, and prefer source_url when you need a page that certainly carries the item. NO ITEM HERE HAS A PAGE OF ITS OWN on analyticslegends.ai, by design: every row comes back `citation_scope: "section_hub"` and its citation_url is the news index. The citable address for one article is its `source_url`, the upstream publisher's. Do not present the hub as the article's page.
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  • Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.
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  • See what FinOps guidance is available: billing mechanics, commitment strategy, allocation and chargeback, AI cost management, and per-provider cost handbooks (AWS, Azure, GCP, OCI, Databricks, Snowflake, ...). Use this to discover what the library covers before deciding what to fetch. When the question already names a FinOps domain, phase, persona or maturity, call ``find_references`` instead of scanning this full list. Returns a dict shaped ``{"references": [...], "total": N}`` where each entry includes ``name``, ``title``, a one-line ``description``, the discriminating FCP facets (``fcp_domain``, ``fcp_capability``, ``fcp_phases``, ``fcp_personas_primary``, ``fcp_maturity_entry``) and ``approx_tokens``. Read ``approx_tokens`` before fetching: the library runs from about 3,000 to over 25,000 tokens per file. Above roughly 10,000, prefer ``get_reference(name, section=...)`` and pull the part you need.
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  • Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.
    ConnectorNo auth
  • Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.
    ConnectorNo auth

Matching MCP Servers

  • A
    license
    C
    quality
    D
    maintenance
    A read-only MCP server that enables users to query Databricks SQL, browse metadata, and monitor Delta Lake tables. It also supports tracking Databricks Jobs, DLT Pipelines, and cluster metrics through natural language interfaces.
    25
    4
    MIT

Matching MCP Connectors

  • Your Databricks Lakehouse in natural language: run SQL on your SQL warehouses, track long-running qu

  • KyomiOAuth

    AI data intelligence platform that brings your data warehouse, dashboards, monitoring, and accumulated business knowledge into any MCP client. Connects to BigQuery, Snowflake, PostgreSQL, MySQL, ClickHouse, Redshift, Databricks, SQL Server, and Azure Synapse.

  • Returns connection status and URLs. When all providers are connected, returns authenticated:true and empty pending[]. When credentials are missing, returns connect_url for the toolkit and per-install URLs.
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  • Returns the current toolkit state: installed MCPs, their connection status, the accounts connected to each one, and how many catalog tools each exposes.
    ConnectorNo auth
  • Convert a SQL statement from one dialect to another — mysql, postgres, sqlite, tsql, oracle, snowflake, bigquery, redshift, spark, hive, presto, trino, duckdb, clickhouse, databricks, doris, starrocks and more. Deterministic parser (sqlglot), not an LLM: the same input always produces the same output, and syntax errors come back with the exact line and column. Use it when migrating queries between databases or debugging dialect-specific syntax.
    ConnectorNo auth
  • Executa uma instrução SQL num SQL warehouse (Statement Execution API). Retorna colunas + linhas quando termina dentro do wait_timeout; senão devolve statement_id + state pra polling via databricks_get_statement. Se `warehouse_id` não for informado, escolhe um warehouse RUNNING automaticamente. PREFIRA queries parametrizadas (`parameters`) a interpolar valores na string (proteção contra SQL injection). SQL é arbitrário (pode DML/DDL) — confirme antes de mutar dados. Bulk support: accepts warehouse_ids for batched execution.
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  • Lista as tabelas de um schema Unity (name, table_type, data_source_format). Informe `catalog_name` e `schema_name`.
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  • The official mcp.ai marketplace — the in-platform catalog of every MCP/tool, AND the way to run them. Covers capability requests like "find an MCP that does X", "consulta um CPF", "is there a tool for Y". Core flow: action=search discovers MCPs by intent → describe returns one MCP's full profile (every tool with its id + params, pricing, auth) so you pick the right tool_id → invoke RUNS that tool. KEY: invoke works even when the MCP is NOT installed — it runs the tool pontualmente (one-off), without adding the MCP to the toolkit and without bloating the tool list. If the MCP needs a credential/login, invoke returns a connect link; if it is paid and the wallet is empty, invoke returns a checkout/top-up link (the user opens it, then you retry). Use install only to make an MCP PERMANENT in the active toolkit (its tools then show up natively in future sessions); prefer invoke for a single/occasional use. list_tools lists what is callable right now. subscribe/cancel handle per-MCP billing; report_bug sends feedback; request_mcp asks us to build a NEW MCP when nothing fits. Search/describe flag installed_in_toolkit vs installed_in_workspace. Writes (install/uninstall/subscribe/cancel and the one-off install behind invoke) require workspace owner/admin. It also carries the mcp.ai PROMPT LIBRARY, which is about ready-made prompt TEXT rather than MCPs: search_prompts finds one, get_prompt returns its full text with {{variables}} filled, and publish_prompt saves a prompt and returns a shareable mcp.ai/p/<slug> link that opens without login.
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  • MCP.AI for IDE agents (Cursor, etc.): log in in the browser, copy the access token. Best: add it to this server's config as a header `Authorization: Bearer <token>` for a permanent, non-expiring connection. Or paste it here for a session-only login: call with { token: "<jwt>" } after the user pastes, or with no args to get the link.
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  • Lista os SQL warehouses do workspace (id, name, state, cluster_size, warehouse_type). Use o `id` em databricks_run_sql (ou deixe o run_sql escolher um RUNNING automaticamente).
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  • 100 REAL rows from a Dataplex product's primary table — the same governed views sold on Snowflake and Databricks. No signup needed. Use this to evaluate schema and content quality.
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  • Status + resultado de um ou mais statements por id (polling de queries longas que voltaram PENDING/RUNNING do run_sql). Aceita lista (`statement_ids`).
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  • Detalha uma ou mais tabelas (colunas, tipos) por nome completo `catalog.schema.table`. Aceita lista (`full_names`).
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  • Report a bug, missing feature, or send feedback. Include the conversation array with recent messages for reproduction.
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