"SQL-Based Technologies or Concepts" matching MCP tools:

• get_query_patterns

  Shibui Finance

  REQUIRED before stock_data_query, 20 SQL patterns prevent timeouts/wrong results Must be called once per session immediately after get_database_schema. Contains query patterns for time-series selection, return calculations, screening joins, window functions, backtesting, and performance optimization. Time-series queries will timeout or return wrong results without these patterns. After this tool returns, call stock_data_query to execute SQL.

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• load_earnings_workflow

  Shibui Finance

  Load earnings workflow for EPS surprises, beat/miss, estimates, revenue. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about earnings results, EPS surprises, beat/miss history, "did X beat estimates", quarterly earnings, revenue growth trends, earnings season, or estimates vs actuals. Can be combined with other workflow tools.

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• fema_search_nfip

  fema-mcp-server

  Search National Flood Insurance Program (NFIP) claims data by state, county, ZIP code, and year range. Returns claim counts, amounts paid on building and contents, flood zones, and loss years. state is required — the full NFIP dataset is 2.7 million rows; unfiltered access is prohibited. When DataCanvas is enabled (CANVAS_PROVIDER_TYPE=duckdb) and results exceed the inline preview, the full result set is staged on a canvas for SQL aggregation via fema_dataframe_query. Use fema_dataframe_describe to inspect the staged table schema before writing SQL. Without canvas, results are returned inline up to the limit.

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• load_fundamental_workflow

  Shibui Finance

  Load fundamental workflow for valuation, cash flow, margins, balance sheet. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about company valuation, "is X a good buy", financial health, debt levels, profitability ratios, revenue trends, earnings quality, or any deep-dive company analysis. Can be combined with other workflow tools.

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• load_technical_workflow

  Shibui Finance

  Load technical workflow for RSI, MACD, SMA, Bollinger Bands, entry/exit. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks about RSI, MACD, moving averages, Bollinger Bands, support/resistance, overbought/oversold, momentum, trend, chart patterns, golden cross, entry/exit signals, or "is X oversold/overbought". Can be combined with other workflow tools.

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• execute_sql_readonly

  mcp

  Execute any valid read only SQL statement on a Cloud SQL instance. To support the `execute_sql_readonly` tool, a Cloud SQL instance must meet the following requirements: * The value of `data_api_access` must be set to `ALLOW_DATA_API`. * For a MySQL instance, the database flag `cloudsql_iam_authentication` must be set to `on`. For a PostgreSQL instance, the database flag `cloudsql.iam_authentication` must be set to `on`. * An IAM user account or IAM service account (`CLOUD_IAM_USER` or `CLOUD_IAM_SERVICE_ACCOUNT`) is required to call the `execute_sql_readonly` tool. The tool executes the SQL statements using the privileges of the database user logged with IAM database authentication. 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. The `execute_sql_readonly` tool has the following limitations: * If a SQL statement returns a response larger than 10 MB, then the response will be truncated. * The tool has a default timeout of 30 seconds. If a query runs longer than 30 seconds, then the tool returns a `DEADLINE_EXCEEDED` error. * The tool isn't supported for SQL Server. If you receive errors similar to "IAM authentication is not enabled for the instance", then you can use the `get_instance` tool to check the value of the IAM database authentication flag for the instance. If you receive errors like "The instance doesn't allow using executeSql to access this instance", then you can use `get_instance` tool to check the `data_api_access` setting. When you receive authentication errors: 1. Check if the currently logged-in user account exists as an IAM user on the instance using the `list_users` tool. 2. If the IAM user account doesn't exist, then use the `create_user` tool to create the IAM user account for the logged-in user. 3. If the currently logged in user doesn't have the proper database user roles, then you can use `update_user` tool to grant database roles to the user. For example, `cloudsqlsuperuser` role can provide an IAM user with many required permissions. 4. Check if the currently logged in user has the correct IAM permissions assigned for the project. You can use `gcloud projects get-iam-policy [PROJECT_ID]` command to check if the user has the proper IAM roles or permissions assigned for the project. * The user must have `cloudsql.instance.login` permission to do automatic IAM database authentication. * The user must have `cloudsql.instances.executeSql` permission to execute SQL statements using the `execute_sql_readonly` tool or `executeSql` API. * Common IAM roles that contain the required permissions: Cloud SQL Instance User (`roles/cloudsql.instanceUser`) or Cloud SQL Admin (`roles/cloudsql.admin`) When receiving an `ExecuteSqlResponse`, always check the `message` and `status` fields within the response body. A successful HTTP status code doesn't guarantee full success of all SQL statements. The `message` and `status` fields will indicate if there were any partial errors or warnings during SQL statement execution.

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• call_creative_worlds

  Heista

  Heista's creative direction engine — same engine the Creative Director specialist runs internally, exposed over MCP. ONE-SHOT: give a brief, get N finished creative outputs. For back-and-forth refinement, or output shapes the `medium` enum below does not cover, use chat_with_creative_worlds instead. OUTPUT SHAPE switches on the `medium` arg: • omitted → N territory cards (default exploration). Each card sits on different psychology / craft / feel / world axis coordinates so the set spans the creative space rather than orbiting one insight. Card has: name, campaign line, 5-8 sentence pitch, one-sentence strategic bet, resolved axis state names, creative-director rationale. • `tvc` → N TVC scripts (15-90s — hook, arc, resolve, sound design, end line). • `billboard` / `ooh` / `print` → N out-of-home concepts (visual concept + line + placement rationale). • `social` → N social-video concepts (hook + format type + middle beat + payoff, optimised for Reels / TikTok / Shorts). • `activation` / `experiential` → N activation concepts (space design + user journey + peak moment + takeaway artifact). • `audio` → N sonic / radio concepts (sonic scene + voice + audio arc). • `campaign` → N full campaign platforms (insight → big idea → strategy → visual world → production roadmap). The engine can also produce manifesto / copy, naming, packaging, PR stunts, content series, brand positioning, partnerships — these output shapes are NOT in the medium enum, so use chat_with_creative_worlds when the user wants one of those. USE WHEN: user says "give me ideas / options / directions / territories", "what angles work for...", "show me three / five ways to...", "write a TVC for...", "draft billboard concepts for...", "I need fresh thinking on...". DO NOT USE to refine one existing direction (use chat tool), to critique work, for OKRs / internal docs / strategy decks, or anything outside advertising creative direction. INPUTS: brief (the creative problem, free text), count (2-6 concepts), optional brand_id (from list_brands or any create_powersource_* — when provided the engine grounds output in the brand's buyer tensions, voice, and selling points), optional medium (above), optional lens_hint (apply a playbook or signature move as a creative constraint), idempotency_key (safely retryable for 5 minutes). Returns the finished creative output as narrative text PLUS a structured array of resolved axis coordinates for programmatic use. Metered — typically 3-15 credits per call depending on count and brand context size. Charged after success on actual token usage.

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• audit_stack

  Exploit Intelligence Platform — CVE, Vulnerability and Exploit Database

  Audit a technology stack for exploitable vulnerabilities. Accepts a comma-separated list of technologies (max 5) and searches for critical/ high severity CVEs with public exploits for each one, sorted by EPSS exploitation probability. Use this when a user describes their infrastructure and wants to know what to patch first. Example: technologies='nginx, postgresql, node.js' returns a risk-sorted list of exploitable CVEs grouped by technology. Rate-limit cost: each technology requires up to 2 API calls; 5 technologies counts as up to 10 calls toward your rate limit.

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• get_schema_context

  mcp-server

  Step 1 of schema discovery: returns the catalog of tables relevant to the user's question. Each table comes with its dataset, business name, dw_table_name and a short description — but NOT the field-level details (no columns, no types, no semantic codes). Use the catalog to identify the most promising candidate(s), then call **get_table_schema** to fetch the full structure of a specific table before writing SQL. **IMPORTANT for SQL queries**: Use ONLY the `dataset.table` format (e.g., `prod_google_ads_v2.campaign_stats`). NEVER prefix with a project_id.

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• fcc_get_provider

  fcc-broadband-mcp-server

  Returns a national-level coverage profile for a specific holding company (by hoconum): states served, technologies deployed, and the number of locations covered at each download speed tier. Use fcc_search_providers to find valid hoconum values. Data is from FCC Form 477 (as of June 2021).

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• load_comparison_workflow

  Shibui Finance

  Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.

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• stock_data_query

  Shibui Finance

  Stock prices, earnings, revenue, P/E, dividends, filings, screener, comparisons Run a SQL query against 64 years of US stock market data. REQUIRES calling get_database_schema then get_query_patterns first (in that order). This tool has no schema or query patterns built in. Call get_database_schema once, then get_query_patterns once, then use this tool. Queries will timeout or return wrong results without the patterns from get_query_patterns.

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• audit_domain

  ContrastAPI

  Perform comprehensive domain audit: combines domain_report + live HTTP security headers + technology fingerprinting. By default report.dns.txt is filtered to security-relevant entries (SPF, DMARC, DKIM, MTA-STS, TLS-RPT) and report.dns.total_txt_records reports the honest pre-filter count; pass include_all_txt=true for the raw TXT list. Use when you need the full picture (recon + active checks); use domain_report for passive-only assessment. Response carries next_calls — chain with subdomain_enum (always emitted) and ssl_check (when an A record resolves) for the residual recon depth (tech_fingerprint already inline as `technologies`). Free: 30/hr (costs 6 credits), Pro: 500/hr. Returns {domain, report, technologies, live_headers, summary, next_calls}.

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• suggest_terms

  Arco Lexicon

  Scans a block of text against all published Arco Lexicon terms using deterministic string matching — no LLM calls. Returns two lists: terms whose canonical names appear explicitly in the text (detected), and terms whose concepts are present but whose canonical names are absent (suggested). Maximum 10,000 characters. Use this to audit an article or passage for correct and complete Arco terminology. Use verify_alignment instead when you want a scored alignment report rather than a term discovery list.

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• load_comparison_workflow

  Shibui Finance

  Load comparison workflow for X vs Y, peer analysis, relative valuation. REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to compare companies, "X vs Y", "how does X compare to Y", peer benchmarking, sector peers, side-by-side metrics, or relative valuation. Can be combined with other workflow tools.

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• get_sc_theory

  SCModeling

  Reference guide to supply-chain simulation concepts: ordering policies, BOM, FDD formulas, event-driven simulation. Pure static text — no engine call, deterministic output. Use this when the user asks a conceptual 'how does this work' question rather than asking for a number.

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• wikidata_search_entities

  wikidata-mcp-server

  Search Wikidata for items or properties by text query. Returns QIDs or PIDs with labels, descriptions, and match metadata indicating whether the hit was on a label or alias. Use type="item" for real-world concepts (people, places, works) and type="property" to find predicate P-IDs. The API returns no total count — pagination is offset-based with no result ceiling indicator.

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• setup_mmm

  mcp-server

  Configures a Marketing Mix Modeling (MMM) study for a project. **What is MMM?** Marketing Mix Modeling measures the real contribution of each marketing channel (Google Ads, Meta, etc.) on a KPI (leads, revenue, conversions), accounting for external factors (seasonality, holidays, promotions). **Recommended workflow:** 1. Use get_schema_context to discover the project's tables/columns 2. Generate input SQL queries (KPI, channels, exogenous variables) 3. **Validate each query before calling setup_mmm:** Use execute_query to run a COUNT(*) wrapper on each input query (e.g., SELECT COUNT(*) FROM (<query>)). If any query returns 0 rows, do NOT include it in setup_mmm — warn the user that the data source is empty and ask whether to proceed without it or fix the query. 4. Call setup_mmm with the validated SQL queries — the study is automatically launched after setup 5. Do NOT call run_mmm after setup_mmm: the first run is triggered automatically **Important:** run_mmm is only needed to RE-RUN an existing study later, not after initial setup. **Input queries format:** Each query must return a "time" column (DATE) and the requested metrics. - role="kpi": a "kpi" column (the target KPI) - role="channel": "spend" and "impressions" columns + channel_name - role="exogenous": columns named after the exogenous variables + columns[] **Granularity**: "weekly" is recommended (MMM standard). SQL should aggregate by week. **Important**: Adapt the SQL dialect to the project's data warehouse type (BigQuery, Snowflake, Redshift).

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• get_help

  mcp-server

  Searches the official Quanti documentation (docs.quanti.io) to answer questions about using the platform. **When to use this tool:** - When the user asks "how to do X in Quanti?", "what is a connector?", "how to configure BigQuery?" - When the user needs help configuring or using a connector (Google Ads, Meta, Piano, etc.) - To explain Quanti concepts: projects, connectors, prebuilds, data warehouse, tag tracker, transformations - When the user asks about the Quanti MCP (setup, overview, semantic layer) **This tool does NOT replace:** - get_schema_context: to get the actual BigQuery schema for a client project - list_prebuilds: to list pre-configured reports for a connector - get_use_cases: to find reusable analyses - execute_query: to execute SQL **Available topic filters:** connectors, data-warehouses, data-management, tag-tracker, mcp-server, transformations

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• load_screening_workflow

  Shibui Finance

  Load screening workflow to find, filter, rank stocks, "top N by...". REQUIRES get_database_schema then get_query_patterns to be called first (in that order). Call BEFORE writing SQL when the user asks to find, screen, rank, or filter stocks — "find stocks that...", "top 10 by...", "best dividend stocks", value/growth screens, sector ranking, or any multi-factor selection. Can be combined with other workflow tools.

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