"Running queries in Apache Superset" matching MCP tools:

• listMetricDefinitions

  Cortex

  List all available engineering metric definitions. USAGE - Call this endpoint BEFORE querying metrics (queryPointInTimeMetrics): 1. Once at start: Call with view='basic' to discover all available metrics - cache this response 2. Once per metric: Call with view='full' and key=METRIC_KEY to get detailed metadata - cache each response 3. Use cached metadata to construct valid point-in-time queries Cache responses in your context. Only refresh if no longer in your context window or explicitly requested (ex to check if metric readiness has changed). Query parameters: - view: 'basic' (default) returns minimal info, 'full' includes sources and query metadata - key: Filter metrics by key (supports multiple values and comma-separated lists) Full view provides query construction metadata: - supportedAggregations: Valid aggregation methods for the metric - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Valid orderBy attributes for metric queries: - orderByAttribute: The metric value itself (returned in full view) - Source attributes: Any attribute from the metric's source (e.g., "source_name.attribute_name") - Dimension attributes: Any attribute from related dimensions (e.g., "source_name.dimension_name.attribute_name") Filter operators by type (for constructing queries): - STRING: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, LIKE, NOT_LIKE, IN, NOT_IN, ANY - INTEGER/DECIMAL/DOUBLE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, IN, NOT_IN, BETWEEN, ANY - DATETIME/DATE: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, GREATER_THAN, LESS_THAN, GREATER_THAN_OR_EQUAL, LESS_THAN_OR_EQUAL, BETWEEN - BOOLEAN: EQUAL, NOT_EQUAL, IS_NULL, IS_NOT_NULL, IN, NOT_IN - ARRAY: EQUAL, CONTAINS, IN Error responses: - 400: Invalid view parameter (must be 'basic' or 'full') - 403: Restricted Feature (contact help@cortex.io)

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• verify_proof

  icme-preflight-mcp

  Independently verify a ZK proof from a prior check_action call. Confirms the guardrail check was performed correctly without re-running it — any third party or monitoring agent can verify in under one second. No additional cost. Wait a few minutes after the check for the proof to be generated. Single-use per proof.

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• queryPointInTimeMetrics

  Cortex

  Execute point-in-time queries for one or more engineering metrics. Returns current metric values for specified time periods, with support for batch queries and optional period-over-period comparisons. Time range (startTime/endTime) cannot exceed 6 months (180 days). PREREQUISITES - Follow this workflow: 1. Discover all available metrics ONCE: Call listMetricDefinitions (view='basic') - cache this response 2. Get metric query metadata ONCE per metric: Call listMetricDefinitions (view='full', key=METRIC_KEY) - supportedAggregations: Valid aggregation methods - orderByAttribute: Attribute path for sorting by metric values - groupByOptions[].key: Valid groupBy keys (use exact values, do NOT guess) - filterOptions[].key: Valid filter keys (use exact values, do NOT guess) Cache the full view response for each metric. Reuse the metadata from cached responses for subsequent queries on the same metric. 3. Construct query: Use the query metadata from the full view responses in step 2 to build valid point-in-time requests IMPORTANT: Cache only results from listMetricDefinitions. Do NOT cache point-in-time query results - always execute fresh queries for current data. Only refresh cached listMetricDefinitions responses if no longer in your context window or explicitly requested. Do NOT guess attribute names - always use exact values from listMetricDefinitions responses. Response includes: - Lightweight metadata: Column definitions optimized for programmatic use - Row data: Actual metric values and dimensional data - No heavy schemas: Source definitions excluded (get from listMetricDefinitions instead) Error responses: - 400: Invalid metric names, date range, validation errors, or unsupported metric combinations - 403: Feature not enabled (contact help@cortex.io)

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• get_app_status

  BorealHost

  Get app installation status and log. Poll this after install_app() to track progress. Requires: API key with read scope. Args: slug: Site identifier app_id: App ID from install_app() response Returns: {"id": "uuid", "app_name": "forge", "status": "running"|"installing"|"failed", "install_log": "..."} Statuses: "installing", "running", "stopped", "failed", "uninstalled"

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• tfstatus

  InsideOut (Riley)

  MONITORING: Quick status check for Terraform deployments Check the current status of a Terraform deployment job. Use this tool to quickly check if a deployment is running, completed, or failed. Returns job status, job_id, and other metadata without streaming logs. Use tflogs to stream the actual deployment logs. REQUIRES: session_id from convoopen response (format: sess_v2_...). OPTIONAL: job_id to target a specific deployment (use tfruns to discover IDs). **LIVENESS**: The response carries two distinct timestamps: - `updated_at` — last semantic change (only bumped when status / drift / version actually differ). Useful for sorting deployments; NOT a per-poll heartbeat. - `last_refresh_at` — last successful Oracle decode (stamped on every poll where reliable reached Oracle, even if nothing in the row changed). Use this to confirm reliable is still actively talking to Oracle for a long-running RUNNING job. Absent on rows that haven't been refreshed since the column was added. 💡 TIP: Examine workflow.usage prompt for more context on how to properly use these tools.

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• ceevee_get_version

  CareerProof MCP

  Get detailed CV version including structured content, sections, word count, and audience profile. cv_version_id from ceevee_upload_cv or ceevee_list_versions. Use to inspect CV content before running analysis tools. Free.

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• dns_check_dkim

  DMARKOFF — DMARC, SPF & DKIM Analytics

  Live DKIM DNS lookup — queries <selector>._domainkey.<domain> TXT record in real time and returns the DKIM key record, errors and warnings. Does NOT require a project — works for any domain, even ones not monitored. Use this to verify a DKIM selector exists, check key length, or diagnose signing failures.

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• dns_check_spf

  DMARKOFF — DMARC, SPF & DKIM Analytics

  Live SPF DNS lookup — queries DNS in real time and returns the SPF record, DNS-lookup count, parsed include tree, TXT diagnostics, errors and warnings. Does NOT require a project — works for any domain, even ones not monitored. Use this to verify SPF configuration, diagnose "too many DNS lookups" issues, or check a domain before adding it to a project.

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• get_stats

  Velvoite — EU Financial Regulatory Compliance

  Get an overview of the Velvoite regulatory corpus. Returns document counts by source, regulation family, entity type, urgency distribution, obligation summary, and date range. Call this FIRST to orient yourself before running queries. No parameters needed.

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• verify_code_integrity

  Solentic

  Verify the code running on Blueprint servers. Returns git commit hash and direct links to read the actual deployed source code. Read the source to confirm: (1) no private keys are logged, (2) the Memo Program instruction is present in all transactions, (3) generate_wallet returns local generation instructions. Don't trust — read the code yourself via the source endpoints.

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• settle_balance

  AI Cortex Storage

  Charges the saved payment method to settle your outstanding balance. Requires explicit user approval before running. For trial accounts, returns the activation URL instead. Minimum charge: $5.

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• property_report

  UK Property Intelligence

  Full data pull for a UK property in one call. Returns sale history, area comps, EPC rating, rental market listings, current sales market listings, rental yield calculation, and price range from area median. Requires a street address + postcode for subject property identification. Postcode-only (e.g. "NG1 2NS") returns area-level data without a subject property — use property_comps or property_yield for postcode-only queries.

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• execute_sql_readonly

  Google Big Query

  Run a read-only SQL query in the project and return the result. Prefer this tool over `execute_sql` if possible. This tool is restricted to only `SELECT` statements. `INSERT`, `UPDATE`, and `DELETE` statements and stored procedures aren't allowed. If the query doesn't include a `SELECT` statement, an error is returned. For information on creating queries, see the [GoogleSQL documentation](https://cloud.google.com/bigquery/docs/reference/standard-sql/query-syntax). Example Queries: -- Count the number of penguins in each island. SELECT island, COUNT(*) AS population FROM bigquery-public-data.ml_datasets.penguins GROUP BY island -- Evaluate a bigquery ML Model. SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`) -- Evaluate BigQuery ML model on custom data SELECT * FROM ML.EVALUATE(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Predict using BigQuery ML model: SELECT * FROM ML.PREDICT(MODEL `my_dataset.my_model`, (SELECT * FROM `my_dataset.my_table`)) -- Forecast data using AI.FORECAST SELECT * FROM AI.FORECAST(TABLE `project.dataset.my_table`, data_col => 'num_trips', timestamp_col => 'date', id_cols => ['usertype'], horizon => 30) Queries executed using the `execute_sql_readonly` tool will have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.

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• get_logs

  BorealHost

  Retrieve container logs (error, access, or PHP). Requires: API key with read scope. Args: slug: Site identifier log_type: "error" (Nginx/Apache errors), "access" (HTTP request log), or "php" (PHP-FPM errors, WordPress sites only) lines: Number of lines to retrieve (1–500, default: 100) search: Optional keyword filter — only lines containing this string Returns: {"log_type": "error", "lines": ["2024-01-15 ... error ...", ...], "count": 42, "truncated": false} Errors: NOT_FOUND: Unknown slug VALIDATION_ERROR: Invalid log_type or lines out of range

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• atlas_get_batch_gem_status

  CareerProof MCP

  Check progress of a batch GEM job. Returns status (pending/running/completed/failed) and progress percentage. When status='completed', fetch results with atlas_get_batch_gem_results(job_id). job_id comes from atlas_start_batch_gem response. Free.

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• ateam_test_status

  ateam-mcp

  Poll the progress of an async skill test. Returns iteration count, tool call steps, status (running/completed/failed), and result when done. (Advanced — use ateam_test_skill with wait=true for synchronous testing.)

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• run_query

  VirtualFlyBrain

  Run a query on VirtualFlyBrain using a VFB ID and query type. Supports batch requests — pass an array of IDs to run the same query_type on all of them, or use the queries array for mixed ID/query_type combinations. When multiple queries are provided, results are returned as a JSON object keyed by "ID::query_type". IMPORTANT: Do NOT pass tool names (like "get_term_info" or "search_terms") as query_type — those are separate tools. Valid query_types are returned by get_term_info in the Queries array for each entity. Common query_types include: PaintedDomains, AllAlignedImages, AlignedDatasets, AllDatasets (for templates); SimilarMorphologyTo, NeuronInputsTo, NeuronNeuronConnectivityQuery (for neurons); ListAllAvailableImages, SubclassesOf, PartsOf, NeuronsPartHere, NeuronsSynaptic, ExpressionOverlapsHere (for classes). Available query_types vary by entity type — ALWAYS call get_term_info FIRST to see which queries are available for a given ID, as attempting invalid query types will result in an error message directing you to use get_term_info.

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• cdc_get_dataset_schema

  cdc-health-mcp-server

  Fetch the full column schema for a CDC dataset — names, data types, descriptions, row count, and last-updated timestamp. Essential before writing SoQL queries against unfamiliar datasets.

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• list_databases_tool

  Wherobots

  List all databases in a given catalog. ⚠️ WORKFLOW: Call this after list_catalogs_tool to explore a specific catalog. 📋 PREREQUISITES: - Call search_documentation_tool first to understand what you're looking for - Call list_catalogs_tool to discover available catalogs 📋 NEXT STEPS after this tool: 1. Use list_tables_tool to find tables in a database 2. Use describe_table_tool to get table schemas before writing queries This tool retrieves all databases within a specified catalog. Parameters ---------- catalog : str The name of the catalog. ctx : Context FastMCP context (injected automatically) Returns ------- DatabaseListOutput A structured object containing database information. - 'catalog': The catalog name. - 'databases': List of database names. - 'count': Number of databases found. Example Usage for LLM: - When user asks for a specific catalog's databases. - Example User Queries and corresponding Tool Calls: - User: "List all databases in the 'wherobots' catalog." - Tool Call: list_databases('wherobots') - User: "What databases are in the foursquare catalog?" - Tool Call: list_databases('foursquare')

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