"Snowflake" matching MCP tools:

• snowflakeA

  osint-terminalC

  Snowflake ID: Decode Twitter/Discord snowflake → timestamp (offline)

  ConnectorNo auth

• snowflake-okA

  Snowflake 63-bit digit shape, value discardedC

  Snowflake 63-bit digit shape, value discarded

  ConnectorNo auth

• snowflake-workerC

  Snowflake worker-id bits, id discardedC

  Snowflake worker-id bits, id discarded

  ConnectorNo auth

• snowflake-ts2D

  Snowflake timestamp bits, value discardedC

  Snowflake timestamp bits, value discarded

  ConnectorNo auth

• snowflake-tsD

  Snowflake timestamp bits, id discardedC

  Snowflake timestamp bits, id discarded

  ConnectorNo auth

• snowflake-shapeD

  Snowflake id shapeC

  Snowflake id shape

  ConnectorNo auth

• snowflake-seqD

  Snowflake sequence bits, id discardedC

  Snowflake sequence bits, id discarded

  ConnectorNo auth

• queryA

  Costory: Your Finops MCPA

  MINIMUM VALID CALL: { "queries": [{ "type": "cost", "name": "a", "metricId": "cost", "currency": "USD" }], "datePreset": "MTD", "aggBy": "Day" } Required per series: type (cost|metric|usage|formula|budget|externalMetric) and name. Put labels in alias. Unified query tool for cost data, custom metrics, usage metrics, external (live integration) metrics, period comparisons, formulas, and budgets. QUERY NAMING: set type and name (prefer short ids like a/b/c for formulas); put human labels in alias (e.g. "Cost by environment") — never in name. Example: { type: "cost", name: "a", alias: "Cost by environment", groupBy: "cos_environment", ... }. For costs: metricId (cost column, default "cost") and currency (default "USD"). Use costMetricId and currency from get when aligning with a budget. For custom business metrics: use [{ type: "metric", metricId: "..." }] — get IDs from list_metrics. For infra usage metrics (e.g. CPU hours, network bytes): use [{ type: "usage", metricId: "..." }] — call suggest_usage_metrics first to discover valid metricIds for your scope. For live external metrics (not saved as Costory metrics): use [{ type: "externalMetric", provider: "...", integrationId: "...", metricName: "...", aggregator: "SUM", groupByFields: [], conditions: "..." }] — discover provider, integrationId, and metricName via list_metrics with includeExternal: true and a specific search term. Tsuga: metricName is the provider metric name; groupByFields are provider metric attributes; conditions is an optional provider filter string. Datadog: same shape as Tsuga — metricName is the Datadog metric name (e.g. system.cpu.user), groupByFields are tag keys (e.g. host, service), conditions is an optional Datadog tag filter (e.g. env:prod). When query is set it is the Datadog metrics query string (pass-through); metricName / aggregator / conditions / groupByFields are ignored; .rollup is required and the interval must be ≥ 24h (daily / weekly / monthly or seconds ≥ 86400). Costory will not fill an empty weekly series. CloudWatch: set provider: "cloudwatch"; metricName is Namespace/MetricName (e.g. AWS/EC2/CPUUtilization); groupByFields are CloudWatch dimension names (e.g. InstanceId); conditions is an optional dimension filter. BigQuery: set provider: "bigquery"; metricName is the fully-qualified table id (project.dataset.table); dateColumn, metricColumn, and gapFillingMethod are required — pick dateColumn/metricColumn from list_metrics `schema` (first DATE / first NUMERIC) and default gapFillingMethod to FORWARD_FILL; groupByFields are string column names (not CEL); conditions is an optional BigQuery WHERE predicate ANDed with the date range. S3: set provider: "s3"; identical field shape to bigquery — metricName is the fully-qualified table id returned by list_metrics (a Costory-managed external table over the customer's mirrored Parquet); same schema-derived columns; conditions is the same optional WHERE predicate. Snowflake: set provider: "snowflake"; metricName is DATABASE.SCHEMA.TABLE; dateColumn, metricColumn, and gapFillingMethod are required; groupByFields are string column names; conditions is an optional Snowflake WHERE predicate ANDed with the date range (e.g. PRIMARY_PROVIDER_AND_MODEL RLIKE 'azure-.*/doctor-ai.*'). BigQuery, S3, and Snowflake conditions are appended as AND (…); statement separators, comments, and unbalanced quotes/parentheses outside string literals are rejected. Use externalMetric for exploration when no saved metric matches; prefer saved { type: "metric" } when one exists. PERIOD: prefer `datePreset` (same DatePreset enum as dashboards/reports, e.g. MTD, LAST_MONTH, TRAILING_30_DAYS, LAST_3_MONTHS, YTD) over hand-computed from/to whenever a preset matches — mutually exclusive with from/to. Response includes the resolved period dates. For comparison: add compare: {} (or compare: { from, to }) — omit compare dates to auto-derive the preceding period (preset-aware, e.g. LAST_MONTH → previous calendar month). For formulas: add { type: "formula", formula: "a / b" } referencing other queries by name. For budgets: use [{ type: "budget", budgetId: "..." }] — despite the field name, this must be the budget version ID (same value as budgetVersionId from get); search returns the parent budget id only, so call get with that id to obtain budgetVersionId before querying. Optional chartType on each query: BAR, LINE, AREA, WATERFALL, or TABLE (defaults to LINE). groupBy is the SPLIT dimension, filterCel is the SCOPE (CEL). Before guessing CEL field names, call search with type: ["dimensions"] — empty query lists all fields; a keyword narrows to matching values. Costory label dimensions use a cos_ prefix (e.g. cos_service_name). Unlabelled resources have null on label dimensions; use filterCel with == null / != null (not is_null or string "null"). Custom virtual dimensions: use immutable `bqName` from list/get VDIM tools as `groupBy` / `filterCel` (not display `name`). Poll `computeStatus` until `COMPLETED` after publish. Optional analyze.changePoint (true or { ignoreWeekends }) runs change-point detection once per query after a timeseries result (incompatible with compare). Optional limit (integer 1–1000): max groups/rows per series. Do NOT set limit unless you need a different cap — when omitted, results default to 100 groups. Set limit above 100 (e.g. 250 or 500) when the user asks for a long tail or full breakdown list. OPTIONAL: After receiving results, consider calling "list_events" for the same date range to correlate cost changes with events, and "suggest_actions" to present follow-up options to the user. EXAMPLES: • "What are my total costs this month?" → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }], datePreset: "MTD", aggBy: "Day" } • "Break down AWS costs by service over the last 90 days" → { queries: [{ type: "cost", name: "a", alias: "AWS by service", metricId: "cost", currency: "USD", groupBy: "cos_service_name", filterCel: "cos_provider in [\"AWS\"]" }], datePreset: "TRAILING_90_DAYS", aggBy: "Week" } • "Show costs for resources without an environment label" → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD", filterCel: "cos_environment == null" }], datePreset: "TRAILING_30_DAYS", aggBy: "Day" } • "How did our costs change vs last month?" → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }], datePreset: "LAST_MONTH", compare: {} } • "Show CPU hours alongside compute costs" (call suggest_usage_metrics first to get valid metricIds) → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "usage", name: "b", metricId: "k8s_cpu_hours" }], datePreset: "TRAILING_30_DAYS", aggBy: "Week" } • "What is our cost per request?" → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "metric", name: "b", metricId: "<metric-id>" }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "TRAILING_30_DAYS" } • "Cost per request volume" (after list_metrics with includeExternal: true and search: "request") → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "externalMetric", name: "b", provider: "tsuga", integrationId: "<integration-id>", metricName: "<metric-name>", aggregator: "SUM" }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "TRAILING_30_DAYS", aggBy: "Week" } • "Cost per BigQuery revenue table" (after list_metrics with includeExternal: true and search: "revenue") → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "externalMetric", name: "b", provider: "bigquery", integrationId: "<integration-id>", metricName: "my-project.analytics.revenue", dateColumn: "event_date", metricColumn: "amount", gapFillingMethod: "ZERO", aggregator: "SUM" }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "TRAILING_30_DAYS", aggBy: "Week" } • "Cost per CPU usage from Datadog" (after list_metrics with includeExternal: true and search: "cpu") → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "externalMetric", name: "b", provider: "datadog", integrationId: "<integration-id>", metricName: "system.cpu.user", aggregator: "AVG", groupByFields: ["host"] }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "TRAILING_30_DAYS", aggBy: "Week" } • "Cost per EC2 CPU from CloudWatch" (after list_metrics with includeExternal: true and search: "CPUUtilization") → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }, { type: "externalMetric", name: "b", provider: "cloudwatch", integrationId: "<integration-id>", metricName: "AWS/EC2/CPUUtilization", aggregator: "AVG", groupByFields: ["InstanceId"] }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "TRAILING_30_DAYS", aggBy: "Week" } • "Budget per calendar month" → { queries: [{ type: "budget", name: "a", budgetId: "<budgetVersionId>" }], datePreset: "LAST_3_MONTHS", aggBy: "Month" } (budgetVersionId from get, not the parent id from search) • "Budget month-to-date by day (cumulative within each month — which day did we reach the budget?)" → { queries: [{ type: "budget", name: "a", budgetId: "<budgetVersionId>", rollingAggregation: { aggregator: "SUM", window: { preset: "MONTH" } } }], datePreset: "MTD", aggBy: "Day" } • "Formula: month-to-date cost vs month-to-date budget (both rolling SUM per month, e.g. utilization a/b)" → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD", rollingAggregation: { aggregator: "SUM", window: { preset: "MONTH" } } }, { type: "budget", name: "b", budgetId: "<budgetVersionId>", rollingAggregation: { aggregator: "SUM", window: { preset: "MONTH" } } }, { type: "formula", name: "c", formula: "a / b" }], datePreset: "MTD", aggBy: "Day" } • Custom one-off range → { queries: [{ type: "cost", name: "a", metricId: "cost", currency: "USD" }], from: "2026-01-15", to: "2026-02-12", aggBy: "Day" }

  ConnectorOAuth

• export_datasetA

  Misata Studio: verified synthetic dataA

  Export a generated dataset as a file. Returns a `download_url` the person can open (or you can fetch, e.g. with curl) for as long as the dataset is held, about 2 hours. Give the person the link rather than pasting file contents into the chat. Args: dataset_id: from a prior generate_dataset call. format: data: csv, parquet, jsonl, json, avro, xlsx, feather, orc, sqlite, duckdb, sql. code and docs: dbt, notebook, dictionary, dbml, mermaid, prisma, sqlalchemy, typescript, jsonschema, expectations, django, openapi, mockapi, demo. `sql` is schema.sql (DDL with keys) + data.sql (COPY/INSERT) — the way to seed a real database: run the returned SQL through your own database connection, since this server never holds a database credential itself. dialect: for `sql` only: postgres, mysql, sqlite, mssql, oracle, bigquery, snowflake. inline: also return the file itself as `base64` (only for files under a few MB). Use it when you must write the file yourself and cannot fetch a URL. Returns: filename, content_type, bytes, download_url, expires_at (unix seconds), and `base64` when `inline` and small enough.

  ConnectorNo auth

• create_diagramA

  mcpA

  When: use when Datadef's own model should design a whole new diagram from a description or a pasted schema. To build it yourself step by step, use create_blank_diagram; to change an existing diagram, canvas_view and the canvas_* tools, or edit_diagram. Generates a professional data architecture diagram from a description and saves it to the user's Datadef account. Use it for any data-shaped diagram: medallion and lakehouse architectures, ETL/ELT pipelines, streaming topologies, data mesh, star and snowflake schemas, ER diagrams, lineage maps, cloud architectures, and data platform designs. EXISTING SCHEMAS: if the conversation or the repository has the schema itself (SQL DDL, a MySQL or pg_dump export, migrations, schema.prisma), pass the file contents as the prompt, unedited. Datadef parses it instead of asking a model: every table, column, type, primary key and foreign key comes out exactly as written, in seconds. Warehouse DDL with no declared foreign keys (fact_sales.customer_key → dim_customer.customer_key) gets its links from key names, drawn dashed. Add a sentence only if you want something other than a picture of that schema; "turn these tables into a star schema" or "design the pipeline that loads them" goes to the model instead. Write a specific prompt. Name the actual technologies (Snowflake, dbt, Airflow, Kafka, Fivetran), the layers or zones you want, and the tables that matter, the diagram is only as detailed as the description. Zone names you give are treated as a specification, not a suggestion. SCOPE: if the user's request is open-ended ("diagram our platform", "show me something"), ask them how much detail they want before calling this, or say which scope you chose. Default to scope "overview". A dense 40-node diagram is impressive and usually not what someone wanted from a one-line request; they can always ask you to expand it. TIMING: this waits up to ~35 seconds; fast generations come back finished, with a preview image and a markdown line to show the user. Slower ones (1-3 minutes total) return a diagram_id while generation continues: call get_diagram with that id after ~60 seconds (poll every 30s) to get the finished diagram. If you cannot call tools again on your own, never leave the user empty-handed: give them the Open link, say it will be ready in about a minute, and offer to show the diagram inline when they next ask. Never call create_diagram a second time while one is still generating; you would create a duplicate. Do not use image generation for these. This produces a real, editable diagram. Next: show the user the preview line it returns; to adjust the result, canvas_view then the targeted canvas_* tools.

  ConnectorAPI key

• fetchA

  MintMCP DocsA

  Fetch the full markdown content of a MintMCP documentation page by its id. Use it after search (or list_docs) to read a page in full before answering, for example the Snowflake connector setup, the SCIM provisioning guide, or the tool governance reference. If you have a public docs URL or a slug instead of a search-result id, use get_page.

  ConnectorNo auth

• transpile_sqlA

  ai-netcafeA

  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

• transpile_sqlA

  AI NetCaféA

  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

• transpile_sqlA

  netcafe-live-dataA

  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

• transpile_sqlA

  netcafe-devkitA

  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

• canvas_search_iconsA

  mcpA

  When: Use before adding nodes that should show a vendor logo (Snowflake, Kafka, Power BI) and you are unsure of the icon id. Find icon ids in Datadef's library (2000+ cloud and data-tool logos) by product name, e.g. "Amazon Aurora PostgreSQL", "k8s", "Azure Event Hubs". Pass `queries` to look up many names in one call (best id per name); pass `query` for the top matches of one name. Scores: 90+ sure, 75+ good. A name with no match has no icon; leave the icon field out rather than guessing. Batch: Pass `queries` with every vendor at once (up to 40, best id per name) instead of one call per vendor. Next: canvas_add_nodes (or canvas_update_nodes) with icon set to a returned id; omit icon when nothing matched.

  ConnectorAPI key

• canvas_replace_nodeA

  mcpA

  When: Use when one component changes identity or type (Redshift becomes Snowflake, a card becomes a service icon). The node keeps its id, connections, lineage, zone, position and fields. Turn a node into a different component in place — Redshift becomes Snowflake, a batch job becomes streaming. It keeps its id, every connection and lineage row, its zone, position, owners, schedule, colours and columns; only what you pass changes (a new label with no icon picks the new product's icon). canvas_update_nodes does the same for many nodes at once. Batch: One node per call. To convert many nodes, use canvas_update_nodes with `type` (and `label`, `icon`) for each, in one call. Operates on one Datadef diagram, named by diagram_id. Returns compact JSON: the ids it created, changed or removed, the diagram revision, a one-line summary and the next step. A viewer-role account is refused. Next: canvas_view to check the result.

  Connector

  Destructive

  API key

• list_referencesA

  Cloud FinOps Skill & MCPA

  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.

  ConnectorNo auth

• add_keyframesA

  MorphaA

  Add many keyframes to ONE element's ONE property in a single call, with an optional loop mode applied in the same call. The idiomatic form when every layer in a multi-element animation gets its own track (ripple dot pulse, snowflake fall, equaliser-bar wave). Factors elementId + property out of the loop body and folds set_track_loop in. Use set_keyframes_batch instead when you need to mix elements/properties in one atomic call.

  Connector

  Destructive

  No auth

• search_companies_by_technologiesA

  company-technographicsA

  Find companies using the given technologies, ranked by usage. Names are case-insensitive ('snowflake' == 'Snowflake'); 10,000+ technologies are tracked (use list_technologies to explore). match='any' needs at least one technology, 'all' needs every one.

  ConnectorNo auth

• searchA

  MintMCP DocsA

  Search the full MintMCP documentation and get back matching pages with their ids, titles, and URLs. Use this first for any MintMCP question: setup, capabilities, security, or a specific connector (Snowflake, Slack, GitHub) or feature (SCIM, SIEM export, prompt security, Coworker Agents). Then pass a result id to the fetch tool to read the page. Coverage spans architecture, enterprise controls, and per-connector setup guides.

  ConnectorNo auth