"Natural Language to SQL Conversion and Executing MySQL Queries to Retrieve Data" matching MCP tools:

• get_data_source_schemaA

  CustomerDashboardA

  List the tables and columns available in a connected data source, so you can write correct widget queries. Supported for PostgreSQL, MySQL, SQL Server, Oracle, Aurora, Redshift and Google Sheets.

  ConnectorNo auth

• 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

• vibekit_db_queryA

  VibeKitA

  Run a read-only SQL query against an app's Postgres database and return up to 200 result rows. SELECT only — writes and DDL (INSERT/UPDATE/DELETE/ALTER/DROP/…) are rejected server-side; use vibekit_chat or vibekit_submit_task to have the agent make data or schema changes. Call vibekit_db_schema first to learn the tables. SQL string, max 5000 chars.

  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

• search_open_grantsA

  foundation-discoveryA

  Search open grant opportunities from Kindora's active foundation-program corpus plus federal and state government grants. FOR-PROFIT APPLICANTS: pass for_profit_applicant=true to search capital a for-profit can take (PRIs, loans, revenue-based financing, patient equity) from CDFIs, impact investors, and PRI-active foundations. The default pool is 501(c)(3)-shaped and will NOT contain those programs. Searches both private foundation grant programs (from IRS data and funder websites) and government grant opportunities — federal (Grants.gov) plus state and district grant portals. Uses full-text search with natural language understanding — queries are parsed into individual terms with stemming, so "youth after school programs" matches programs about youth, after-school, and programming even if those exact words don't appear together. Search covers program names, descriptions, focus areas, beneficiary types, and geographic focus fields. Use the state parameter to focus on geographically relevant opportunities. Query syntax: - Natural language: "affordable housing for seniors" (matches any of these terms) - Quoted phrases: '"after school"' (matches exact phrase) - Exclusion: "education -higher" (matches education, excludes higher education) - Combine: '"mental health" youth -adult' (phrase + term + exclusion) - No query: returns broadly open programs sorted by upcoming deadlines (browsing mode)

  ConnectorNo auth

• 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

• get_outcomeA

  Sanctions ScreeningA

  Retrieve the final OutcomeReceipt for a completed operation. WHEN TO USE: Use after get_status returns success/failure/partial to retrieve the full result with cost and reason codes. WHEN NOT TO USE: Do not use for operations still in pending/executing state — use get_status first. COST: free - no key required LATENCY: ~50ms

  ConnectorNo auth

• get_outcomeA

  Appointment BookingA

  Retrieve the final OutcomeReceipt for a completed operation. WHEN TO USE: Use after get_status returns success/failure/partial to retrieve the full result with cost and reason codes. WHEN NOT TO USE: Do not use for operations still in pending/executing state — use get_status first. COST: free - no key required LATENCY: ~50ms

  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

• query_sqlA

  ThinAir DataA

  Execute a read-only SQL query against the target connection. ONLY SELECT / WITH / EXPLAIN permitted. Write dialect-appropriate SQL for the connection's engine — use PostgreSQL syntax for postgres connections (`SELECT NOW()`, `LIMIT`, `ILIKE`), T-SQL for mssql (`SELECT GETDATE()`, `TOP N`, `LIKE`), MySQL for mysql (`SELECT NOW()`, `LIMIT`). Response meta includes `connection` + `dialect` so you know which syntax worked; reuse that dialect in follow-up calls. Default LIMIT 100 unless the user asks for all rows.

  ConnectorOAuth

• 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

• create_userA

  mcpA

  Create a database user for a Cloud SQL instance. * This tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes. * When you use the `create_user` tool, specify the type of user: `CLOUD_IAM_USER`, `CLOUD_IAM_SERVICE_ACCOUNT`, or `BUILT_IN`. * By default the newly created user is assigned the `cloudsqlsuperuser` role, unless you specify other database roles explicitly in the request. * You can use a newly created user with the `execute_sql` tool if the user is a currently logged in IAM user. The `execute_sql` tool executes the SQL statements using the privileges of the database user logged in using IAM database authentication. The `create_user` tool has the following limitations: * To create a built-in user with password, use the `password_secret_version` field to provide password using the Google Cloud Secret Manager. The value of `password_secret_version` should be the resource name of the secret version, like `projects/12345/locations/us-central1/secrets/my-password-secret/versions/1` or `projects/12345/locations/us-central1/secrets/my-password-secret/versions/latest`. The caller needs to have `secretmanager.secretVersions.access` permission on the secret version. * The `create_user` tool doesn't support creating a user for SQL Server. To create an IAM user in PostgreSQL: * The database username must be the IAM user's email address and all lowercase. For example, to create user for PostgreSQL IAM user `example-user@example.com`, you can use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance":"test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user@example.com`. To create an IAM service account in PostgreSQL: * The database username must be created without the `.gserviceaccount.com` suffix even though the full email address for the account is`service-account-name@project-id.iam.gserviceaccount.com`. For example, to create an IAM service account for PostgreSQL you can use the following request format: ``` { "name": "test@test-project.iam", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `test@test-project.iam`. To create an IAM user or IAM service account in MySQL: * When Cloud SQL for MySQL stores a username, it truncates the @ and the domain name from the user or service account's email address. For example, `example-user@example.com` becomes `example-user`. * For this reason, you can't add two IAM users or service accounts with the same username but different domain names to the same Cloud SQL instance. * For example, to create user for the MySQL IAM user `example-user@example.com`, use the following request: ``` { "name": "example-user@example.com", "type": "CLOUD_IAM_USER", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM user is `example-user`. * For example, to create the MySQL IAM service account `service-account-name@project-id.iam.gserviceaccount.com`, use the following request: ``` { "name": "service-account-name@project-id.iam.gserviceaccount.com", "type": "CLOUD_IAM_SERVICE_ACCOUNT", "instance": "test-instance", "project": "test-project" } ``` The created database username for the IAM service account is `service-account-name`.

  ConnectorNo auth

• gribstream_resolve_shared_parameterA

  GribStream Weather MCPA

  NO AUTH / PUBLIC / READ-ONLY. Resolves one shared parameter preset for a dataset, returning native variables and expressions to use in /timeseries or /runs request bodies. Use this first for common natural-language concepts such as 2 metre temperature or 10 metre wind instead of guessing dataset-native codes such as TMP. This tool does not execute the request, query weather values, or return forecast data.

  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