"Converting Natural Language to SQL Queries" matching MCP tools:

• ask-about-project-dataA

  cupix-compassA

  Query the construction project database using natural language (Text-to-SQL). Converts natural language into SQL to retrieve captures, annotations, progress metrics, schedules, and other project records. Pass the user's question as-is without modification. For trade visibility, use `analyze-progress-and-forecasts` instead. **WORKFLOW:** - **Default**: call this tool with only `query`. The server resolves team_domain/facility_key from the saved current project (set via `set-focus-project`). Do NOT call `list-my-projects` again just to obtain these values. - Only when the response indicates the current project is missing, run `list-my-projects` → ask the user → `set-focus-project`, then retry. - Pass explicit team_domain/facility_key **only** when the user clearly wants to query a different project than the saved one. **Available tables:** - progresses: SI progress metrics (level, category, phase, workarea, cost, dates) - captures: Camera captures metadata (level, camera_model, capture_state, user_email) - records: Capture events with timestamps (captured_at, state, id) - photo_notes: Photonotes (description, state, user_email, created_at) - voice_notes: Voicenotes (level, description, state, user_email, created_at) - facilities: Site info (name, address, size, location, bim_count, created_at) - users: User profiles (name, email) - workareas: Spatial zones (level, name, user_name) Args: query: Natural language question (pass as-is, no SQL syntax) team_domain: Omit by default. Pass only to override the current project. facility_key: Omit by default. Pass only to override the current project. user_intent: REQUIRED. Pass the user's original question or request verbatim. Used for analytics only, does not affect results. scope: Previous ask-about-project-data result identity to search within. limit: Number of rows per page. Values above 200 are capped at 200. cursor: Cursor for the next page of the same search. Returns: List of TextContent with query results and metadata

  ConnectorNo auth

• searchA

  JobYap Job SearchA

  Search JobYap job postings by natural-language query. Matches job titles, falling back to significant keywords when the full phrase finds little. Returns result ids, titles and citable URLs for use with fetch. For structured filtering (location, company, remote, freshness) prefer search_jobs.

  ConnectorNo auth

• query_financial_secA

  Calera FINSEC — Certified SEC MemoryA

  Certified SEC fact query. Returns verified values with provenance or SAFE_REFUSAL. Does not invent numbers. Treat SAFE_REFUSAL as success-of-honesty, not a tool failure. Monetary answers default to the filer's reporting currency; request FX conversion via target_currency / usd_only or natural-language 'in USD'.

  ConnectorNo auth

• nlqdb_readA

  nlqdb — analytical memory for AI agentsA

  Read-only natural-language query over your agent's memory — SELECT / aggregate / JOIN over existing data. Guaranteed never to write, create, or modify: a request whose plan would change data is refused (use nlqdb_query for that), so this tool is safe to mark 'always allow' in your host. Auto-targets your only database; pass `db` to pick one when you have several. Returns rows + the compiled SQL in trace.

  ConnectorOAuth

• openstreetmap_query_rawA

  openstreetmap-mcp-serverA

  Execute a raw Overpass QL query for advanced spatial queries that the convenience tools do not cover. Use for multi-type queries, union queries, relation membership, historical queries, or any operation requiring full Overpass QL expressiveness. The query must include [out:json]. Example: "[out:json][timeout:15];node[\"natural\"=\"peak\"](47.5,-122.5,47.7,-122.2);out body;" Returns one page of the result set: use limit and offset to page through it, and read totalFound and truncated to see how much the query matched. Validate complex queries at overpass-turbo.eu before use. For simple "what's near X?" or "what's in this area?" queries, use openstreetmap_query_nearby or openstreetmap_query_bbox instead.

  ConnectorNo auth

• searchPapersA

  qubitsok — Quantum Computing Jobs, Papers & ResearchersA

  Search quantum computing research papers from arXiv. Use when the user asks about recent research, specific papers, or academic topics in quantum computing. NOT for jobs (use searchJobs) or researcher profiles (use searchCollaborators). Supports natural language queries decomposed via AI into structured filters (topic, tag, author, affiliation, domain). Date range defaults to last 7 days; max lookback 12 months. Returns newest first, max 50 results. Use getPaperDetails for full abstract and analysis of a specific paper. Examples: "trapped ion papers from Google", "QEC review papers this month", "quantum error correction".

  ConnectorNo auth

• discover_brandsA

  marketA

  Find fashion brands using natural language, structured filters, or both. Best for queries like "Italian streetwear brands", "Scandinavian minimalist brands", "Japanese technical outerwear", "brands with avant-garde tailoring", or qualified similarity such as "brands like Rick Owens for technical outerwear". For a plain "brands like X" request, use find_similar_brands. Country adjectives ("Italian", "Scandinavian", "Nordic", "Japanese", "Iberian", "Benelux") are parsed server-side into shipping-origin filters; you don't need to translate them to ISO codes. `query` is optional — provide a query, structured filters, or both. Brand country/shipping signals are best-effort and separate from product availability.

  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

• quote_routeA

  402 DispatcherA

  FREE. Rank Bazaar APIs for a natural-language task without paying. Prefer over search_bazaar when you need ranked candidates (economy=cheapest; verified=reliability score). Prefer over list_discovered_apis when matching a task, not dumping the cache. Use route_and_call next to execute; do not use this to pay.

  ConnectorNo auth

• query_nem_dataA

  nempulseA

  Ask a natural-language question about NEM BESS data; returns generated SQL, result rows and a plain-English explanation. Scope each question to roughly one region-month or less — aggregates spanning more (e.g. a full year by region, or per-day top-N across all regions) can exceed the 15s query timeout. For per-day top-N / bottom-N questions, phrase them so the generated SQL uses a window function (ROW_NUMBER/RANK) rather than a per-day correlated subquery — the latter has been observed to silently return all-null rows with no error. Only dispatch_prices, daily_revenue, optimal_dispatch, bess_price_profile and market_events are reachable here; the market_* cache tables (market_monthly, market_regression, market_corr_tracker, market_daily_price, market_daily_fleet) behind the market-analysis page live in a separate database and are NOT queryable through this tool — a question about them will be recomputed from dispatch_prices instead, which is slower and easy to phrase incorrectly.

  ConnectorNo auth

• 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

• searchA

  OrbitA

  Find and evaluate public API endpoints that match your query. Set `q` to a natural language query, keywords, an API name, or a question — results are matched by meaning and keyword; each result includes `id`, `resourceType`, `name`, `description`, `method`, `url`, and `evaluateGuide` — an evaluation of what the endpoint does, when to use it, and its limitations. Review `evaluateGuide` to pick the best fit, then pass each chosen result's `id` and `resourceType` (as `type`) to `integrate`. Paginate with `cursor` from `meta.nextCursor` (`limit` defaults to 10, max 25; pagination stops at 40 results total). No authentication required. Best practices for querying: - Use focused keyword queries that include the product or provider name along with the endpoint details, for example "PayPal create invoice". - Alternatively, use natural language queries such as "PayPal API to create an invoice". - Avoid jumbled queries that cram many unrelated keywords into a single query, for example "paypal invoice payment delivery payments ordering". - Avoid OR-separated queries such as "paypal invoice OR paypal create invoice OR paypal OR invoice creation". - If you need to explore multiple intents, try each as a separate call.

  ConnectorNo auth

• execute_sql_readonlyA

  Google Big QueryA

  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: ```sql -- 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 always have the 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.

  ConnectorNo auth

• search_serversA

  MCP MarketplaceA

  Search the MCP Marketplace catalog. With a free-text `query` and default `sort`, results are ranked by semantic similarity (gte-small embeddings + cosine similarity), so natural-language queries like 'manage my calendar', 'something to read PDFs', or 'database for my agent' work as well as keyword searches. Each result includes `security_score` (0-10), `risk_level` (low/moderate/high/critical), `critical_findings` (count of severity=critical|high findings), pricing, rating, install count, and a URL. `ranking_mode` in the response indicates whether semantic or keyword matching was used. Before recommending an install, call get_server for full details including every flagged finding — critical_findings > 0 means the server has known security issues you must surface to the user.

  ConnectorNo auth

• list_eventsA

  ClearMarketA

  Browse or search ClearMarket prediction-market events. Filter by category, platform, Resolution Clarity Grade, or free-text `q`. `q` is token-AND across question + tags, so SHORT KEYWORD queries match best ("microstrategy bitcoin", "fed rate") — natural-language phrases often return nothing. Returns compact graded summaries: slug, question, venues_covered, primary grade, rcg_score (0-100, for ranking clarity), last_price, and status (open / resolved). Start here when you have a topic but not a slug; then call get_event for the full graded record. Categories: economics, financials, crypto, companies, technology, politics, geopolitics, health, climate.

  ConnectorNo auth

• search_listingsA

  SPACEXPLORATIONA

  Search commercial real estate listings. Returns paginated hits with facet counts. For AI-driven search, call interpret_search first to convert a natural-language query into structured filters, then pass those filters — and its bounds, when present — here.

  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

• query_databaseA

  flootA

  Run a READ-ONLY SQL query against the project's Postgres database (SELECT, EXPLAIN, etc.). Writes are rejected — use execute_sql for those. Returns JSON: `{rows, rowCount, command, truncated?}` (or `{results: [...]}` for multi-statement queries). Pass `database` only if the project has more than one.

  ConnectorOAuth

• search_rentals_naturalA

  arcasos-rentalsA

  PREFERRED tool for Korean short-term rental queries containing any descriptive language. ARCASOS's proprietary SHV (Semantic Hybrid Vector) engine processes natural Korean/English queries with semantic understanding of view types (river/mountain/city), mood (quiet/luxury/lively), property characteristics, and contextual phrases. Pass the user's natural language query AS-IS — do NOT extract slots. Returns semantically pre-ranked results in Schema.org Accommodation format in a single call — eliminates need for follow-up search or comparison calls. Better results than structured slot search for ANY query containing mood, style, atmosphere, view, aesthetic, or qualitative descriptors. Use this to minimize token usage and latency.

  ConnectorNo auth

• search_datasheetsA

  sheetsdata-mcpA

  Semantic search across all extracted datasheets. Finds components matching natural language queries about specifications, features, or capabilities. Best for broad spec-based discovery across all parts (e.g. 'low-noise LDO with PSRR above 70dB'). Only searches datasheets that have been previously extracted — not all parts that exist. For finding specific parts by number, use search_parts instead.

  ConnectorNo auth

• proximens_geo_bulk_searchA

  Proximens OracleA

  Pro-tier. Run many GEO-principle searches in a single fast call. INPUT: queries (array of 2-100 natural-language strings, each 3-500 chars); optional top_k_per_query (1-10, default 5) and category filter. RETURNS: JSON with a results array (per query: the query, its matched principles, and a count), plus total_queries, total_matches and processing time. USE WHEN you need many lookups at once, e.g. a full-site audit or a keyword list, instead of repeated search_principles calls.

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