"How to connect and execute queries on a private SQL database" matching MCP tools:

• execute_query

  BorealHost

  Execute a SQL query on a site's database. Supports SELECT, INSERT, UPDATE, DELETE, and DDL statements. Results are limited to 1000 rows for SELECT queries. Requires: API key with write scope. Args: slug: Site identifier database: Database name query: SQL query string Returns: {"columns": ["id", "title"], "rows": [[1, "Hello"], ...], "affected_rows": 0, "query_time_ms": 12}

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• sql_from_english

  Zambo AI Tools

  Convert plain-English database questions into working SQL queries — with explanation and optimization notes. Describe what you want to pull from your database and get production-ready SQL. Handles JOINs, aggregations, subqueries, window functions. Use when user says 'write a query to', 'get me all X where Y', 'SQL for', 'how do I query'.

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• openstreetmap_query_raw

  openstreetmap-mcp-server

  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.

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• discovery_analyze

  Disco

  Run Disco on tabular data to find novel, statistically validated patterns. This is NOT another data analyst — it's a discovery pipeline that systematically searches for feature interactions, subgroup effects, and conditional relationships nobody thought to look for, then validates each on hold-out data with FDR-corrected p-values and checks novelty against academic literature. This is a long-running operation. Returns a run_id immediately. Use discovery_status to poll and discovery_get_results to fetch completed results. Use this when you need to go beyond answering questions about data and start finding things nobody thought to ask. Do NOT use this for summary statistics, visualization, or SQL queries. Public runs are free but results are published. Private runs cost credits. Call discovery_estimate first to check cost. Private report URLs require sign-in — tell the user to sign in at the dashboard with the same email address used to create the account (email code, no password needed). Call discovery_upload first to upload your file, then pass the returned file_ref here. Args: target_column: The column to analyze — what drives it, beyond what's obvious. file_ref: The file reference returned by discovery_upload. analysis_depth: Search depth (1=fast, higher=deeper). Default 1. visibility: "public" (free) or "private" (costs credits). Default "public". title: Optional title for the analysis. description: Optional description of the dataset. excluded_columns: Optional JSON array of column names to exclude from analysis. column_descriptions: Optional JSON object mapping column names to descriptions. Significantly improves pattern explanations — always provide if column names are non-obvious (e.g. {"col_7": "patient age", "feat_a": "blood pressure"}). author: Optional author name for the report. source_url: Optional source URL for the dataset. use_llms: Slower and more expensive, but you get smarter pre-processing, summary page, literature context and pattern novelty assessment. Only applies to private runs — public runs always use LLMs. Default false. api_key: Disco API key (disco_...). Optional if DISCOVERY_API_KEY env var is set.

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• vibekit_db_query

  VibeKit

  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.

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• explain_architecture

  Zephex

  Maps how files connect across a subsystem — roles and import edges, not file bodies. Ripgrep + import-graph analyzers; detects framework, language, architecture_type. Envelope: focus, summary, hint, data, related_focus, next_calls, meta (meta.cache_hit, meta.tokens_returned, meta.credits, meta.charges_usage). Hosted: 7 credits per success; failures free. Cheapest path: mode overview + concern or seed_files — ~1.5–4k tokens, replaces 10+ blind read_code file opens. Repeat identical calls hit server cache (meta.cache_hit) until force:true. Expensive: mode deep or audit on whole monorepo — use subpath. >10k files auto-degrades to overview. data: entry_points, layer_map, concern_cluster (with concern or seed_files[]), integration_map, auth_flow, dependency_graph; deep adds request_flows + Mermaid; audit adds anti_patterns + health_score. dimension_confidence per slice; warnings on low confidence. focus: api|auth|integrations|database|security|data_flow|error_handling|full. Pass concern (any label) or seed_files[] (1–20 from find_code). subpath scopes monorepos. Call BEFORE cross-cutting edits — how a feature spans modules, where to patch. Do NOT for stack (get_project_context), search (find_code), bodies (read_code), tests, packages, live URL. After: next_calls → read_code outline on hub files. Read-only.

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• get_resource_usage

  Dockhold

  Show your account's compute, database-RAM, and storage pools: how much you've bought, how much is used, and how much is free, plus every app's current size. Call this before any resize tool (the allowed sizes come from its steps fields), and to explain to the user why an app ran out of memory or a deploy was refused for capacity.

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• import_data

  mcp

  Import data into a Cloud SQL instance. If the file doesn't start with `gs://`, then the assumption is that the file is stored locally. If the file is local, then the file must be uploaded to Cloud Storage before you can make the actual `import_data` call. To upload the file to Cloud Storage, you can use the `gcloud` or `gsutil` commands. Before you upload the file to Cloud Storage, consider whether you want to use an existing bucket or create a new bucket in the provided project. After the file is uploaded to Cloud Storage, the instance service account must have sufficient permissions to read the uploaded file from the Cloud Storage bucket. This can be accomplished as follows: 1. Use the `get_instance` tool to get the email address of the instance service account. From the output of the tool, get the value of the `serviceAccountEmailAddress` field. 2. Grant the instance service account the `storage.objectAdmin` role on the provided Cloud Storage bucket. Use a command like `gcloud storage buckets add-iam-policy-binding` or a request to the Cloud Storage API. It can take from two to up to seven minutes or more for the role to be granted and the permissions to be propagated to the service account in Cloud Storage. If you encounter a permissions error after updatingthe IAM policy, then wait a few minutes and try again. After permissions are granted, you can import the data. We recommend that you leave optional parameters empty and use the system defaults. The file type can typically be determined by the file extension. For example, if the file is a SQL file, `.sql` or `.csv` for CSV file. The following is a sample SQL `importContext` for MySQL. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL" } ``` There is no `database` parameter present for MySQL since the database name is expected to be present in the SQL file. Specify only one URI. No other fields are required outside of `importContext`. For PostgreSQL, the `database` field is required. The following is a sample PostgreSQL `importContext` with the `database` field specified. ``` { "uri": "gs://sample-gcs-bucket/sample-file.sql", "kind": "sql#importContext", "fileType": "SQL", "database": "sample-db" } ``` The `import_data` tool returns a long-running operation. Use the `get_operation` tool to poll its status until the operation completes.

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• water_dataframe_query

  usgs-water-mcp-server

  Run a read-only SQL SELECT against water data tables staged on a DataCanvas by water_get_series or water_find_sites. Workflow: run water_get_series or water_find_sites (get canvas_id + table_name) → water_dataframe_describe (confirm the table and its columns) → water_dataframe_query (SQL analysis). Only SELECT statements are permitted. At most 10,000 rows are returned; a query matching more is capped and the response sets truncated=true — scope with WHERE/LIMIT, and use SELECT COUNT(*) or water_dataframe_describe to learn the true match count. Requires DataCanvas to be enabled on this server instance. Returns an error if DataCanvas is not available.

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• list_databases

  shiply

  List the SQL databases (D1 or Neon Postgres) on my account, including which owned site (if any) each is attached to. Call this BEFORE db_query/db_schema-style work to discover a databaseId — those live on a per-database MCP server reached via GET /api/v1/databases/{id} (see llms.txt), which this id feeds.

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• scalix_db_text_to_sql

  Scalix Cloud

  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.

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• ic_leaderboard_connect_github

  Immersive Commons

  Verify a GitHub Personal Access Token against api.github.com/user, then store the resulting username on your IC profile. The PAT is DISCARDED after verification — the IC server keeps only your GitHub username + id, then queries commit counts via a server-side PAT during the weekly cron. Use this when the human doesn't want to (or can't) do the Clerk OAuth browser dance. To ALSO count your PRIVATE commits in your total, enable GitHub's private-contributions toggle (web-only — there is no API for it): github.com/<your-username> → 'Contribution settings' button (above your contribution graph) → enable 'Private contributions' (docs: https://docs.github.com/en/account-and-profile/setting-up-and-managing-your-github-profile/managing-contribution-graphs-on-your-profile/publicizing-or-hiding-your-private-contributions-on-your-profile). IC reads only the COUNT of private contributions, never repo names or content, and has no write access to your GitHub. Args: { pat: string }. Returns: { ok, github: { login, id, name?, avatarUrl? }, next_steps: string[] }. Required scope: github:link.

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• update_youtube_video

  Hermoso

  Edit an existing video on the connected channel: title, description, tags, and/or privacy (unlisted | public | private). THIS IS HOW YOU FLIP AN UNLISTED UPLOAD PUBLIC — post_to_youtube defaults to UNLISTED, and without this there was no way to publish it afterwards. Making a video PUBLIC puts it on the channel where anyone can find it, so show the user exactly what will change and get an explicit yes before calling with privacy:"public". Fields you omit are left untouched. Needs a connected YouTube channel.

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• chembl_dataframe_query

  chembl-mcp-server

  Run a read-only SQL SELECT over the bioactivity rows chembl_get_bioactivities spilled to a canvas — rank, group, dedupe, and aggregate across the FULL set, not the inline preview. Reference each staged table by the name chembl_get_bioactivities returned — bioactivities for its potency_ranked view, bioactivities_null_potency for null_potency; discover the staged tables and their columns with chembl_dataframe_describe. Compute honest aggregates here (e.g. SELECT molecule_chembl_id, MEDIAN(pchembl_value) AS med FROM bioactivities WHERE standard_type = 'IC50' GROUP BY 1 ORDER BY 2 DESC). Two independent bounds apply, each reported on its own field: truncated is true when the SQL result exceeded the canvas row cap, and rendered_rows says how many of the returned rows the markdown table holds once its character budget is reached (below row_count on a wide or long result). Page past either bound with SQL LIMIT/OFFSET — append e.g. LIMIT 500 OFFSET 500 and re-call; offsets reach rows beyond the canvas row cap. Requires CANVAS_PROVIDER_TYPE=duckdb.

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• request_external_resource

  floot

  Request the USER'S OWN external credential for this project — their OpenAI or Anthropic API key, an external Postgres connection string, or any other service's key (type GENERIC, e.g. Stripe/Resend — name the env vars via secret_env_vars). NOT for Floot-managed resources (database/auth/push/oauth/…) — use provision_resource for those; they need no user input. REUSE FIRST: if the project owner already has a matching credential on their account (list_resources section 2), this connects it silently and returns the env var names — no link, no user action, nothing to poll. Pass the name exactly as list_resources shows it to make that happen. Reusing a POSTGRES credential also seeds helpers/db, installs the query stack, and pulls the typed schema helper, so do NOT write those yourself afterwards. Otherwise it returns a secure connect link: SHOW it to the user (UI-capable hosts render a Connect button automatically; on terminal hosts with shell access open it in the user's default browser yourself and paste the URL as plain text) and ask them to open it. The call completes only when the user finishes the connect flow — it never expires. Do NOT block on it: request the credential EARLY, keep building everything that doesn't need the secret (the env var names are known now — reference process.env.X in code before the secret exists), and check the request between tasks; the user may never connect it, and the build must not stall. NEVER ask the user to paste a secret into the chat. On completion you get the env var names — never the secret values. Re-calling with the same type returns the same pending request.

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• generate_wallet

  Blueprint Agentic Staking (Solentic)

  Returns runnable code that creates a Solana keypair. Solentic cannot generate the keypair for you and never sees the private key — generation must happen wherever you run code (the agent process, a code-interpreter tool, a Python/Node sandbox, the user's shell). The response includes the snippet ready to execute. After running it, fund the resulting publicKey and call the `stake` tool with {walletAddress, secretKey, amountSol} to stake in one call.

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• create_relational_database

  mcp

  Provisions a managed MySQL (or MariaDB) database on a dedicated VM on your private network — the relational-database resource (use this instead of create_database when the app needs MySQL/MariaDB, e.g. WordPress, NextCloud, Matomo, many PHP/LAMP apps). Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' with db_engine mysql/mariadb so plan_deploy includes and wires the DB automatically. It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP (port 3306), not a public address. Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_relational_databases until status='ready', then the connection details (private_ip, port 3306, db_name, db_user) are populated. MySQL is created with mysql_native_password auth so older clients/apps connect cleanly. (ClickHouse is a separate resource — use create_clickhouse / list_clickhouse_databases.)

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• create_clickhouse

  mcp

  Provisions a managed ClickHouse database (OLAP / columnar analytics engine, Apache-2.0) on a dedicated VM on your private network — its OWN resource, NOT a relational database. Requires a recent plan_managed_datastore. Use it for analytics / observability workloads that need a column store (PostHog, Langfuse, event analytics, time-series). It is PRIVATE — reachable only from another instance on the same private network, via the DB's internal/private IP on the ClickHouse HTTP port 8123 (CLICKHOUSE_HOST/PORT/USER/PASSWORD/DB env, http://host:8123). Get the ids from plan_managed_datastore/list_flavors (use m1.small+ — ClickHouse needs >=2GB RAM), list_private_networks, list_keypairs. Provisioning takes ~5 min; poll list_clickhouse_databases until status='ready'. HIGH AVAILABILITY: pass ha:true to get THREE machines on three different physical hosts behind a load balancer instead of one: all three take reads and writes, so losing a machine costs no failover and no write pause, and the replacement refills itself from the survivors before it serves again. It costs about 3x the hourly rate (three machines instead of one) and provisions more slowly. Default is a single machine; show the user the price difference and get an explicit yes before turning HA on.

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• create_listing

  Tu Lugar

  Create a real estate listing for the authenticated agent. Always creates a private DRAFT — it is NOT visible publicly. A city or neighborhood is enough to start; NEVER invent coordinates (fabricated pins are discarded). The response returns an edit-page link with a map: ask the user to open it and drop the pin on the exact property location, and wait for their confirmation before publish_listing — publishing is blocked until a human sets the pin and at least one photo. Requires a CONNECTED account with a verified WhatsApp number — if unsure, call account_status FIRST and, if not connected, share the connect steps it returns instead of attempting to create.

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• create_listing

  Tu Lugar

  Create a real estate listing for the authenticated agent. Always creates a private DRAFT — it is NOT visible publicly. A city or neighborhood is enough to start; NEVER invent coordinates (fabricated pins are discarded). The response returns an edit-page link with a map: ask the user to open it and drop the pin on the exact property location, and wait for their confirmation before publish_listing — publishing is blocked until a human sets the pin and at least one photo. Requires a CONNECTED account with a verified WhatsApp number — if unsure, call account_status FIRST and, if not connected, share the connect steps it returns instead of attempting to create.

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