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458,114 tools. Updated 2026-08-14 23:05

"Overview and Resources for PostgreSQL Database System" matching MCP tools:

  • 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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  • 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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  • Set an environment variable for a project. Variables are encrypted at rest (AES-256-GCM) and injected at container runtime. NOTE: DATABASE_URL, PGHOST, PGPORT, PGUSER, PGPASSWORD, and PGDATABASE are all auto-injected for the managed PostgreSQL database — you do NOT need to set any of them manually. The PORT variable is auto-managed: 8080 for auto-detected frameworks (Next.js, Node.js, Python), or auto-detected from the Dockerfile EXPOSE directive for custom Dockerfile builds. IMPORTANT: Changing env vars does NOT auto-redeploy. You must call deploy or use the redeploy API endpoint to apply changes. For Next.js apps, NEXT_PUBLIC_* variables must be set BEFORE deploying since they are embedded at build time.
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  • Install an app template on a VPS/Cloud site. Starts a background installation. Poll get_app_status() for progress. Requires: API key with write scope. VPS or Cloud plan only. Args: slug: Site identifier template: App template slug. Available: django, laravel, nextjs, nodejs, nuxtjs, rails, static app_name: Short name for the app (2-50 chars, lowercase alphanumeric + hyphens). Used as subdomain: {app_name}.{site_domain} db_type: Database type. "none", "mysql", or "postgresql" (depends on template) domain: Custom domain override (default: {app_name}.{site_domain}) display_name: Human-friendly name (default: derived from app_name) Returns: {"id": "uuid", "app_name": "myapp", "status": "installing", "message": "Installation started. Poll for progress."} Errors: FORBIDDEN: Plan does not support apps (shared plans) VALIDATION_ERROR: Invalid template, app_name, or duplicate name
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  • Checks if a Cloud SQL for PostgreSQL instance is ready for a major version upgrade to the specified target version. The `target_database_version` MUST be provided in the request (e.g., `POSTGRES_15`). This tool helps identify potential issues *before* attempting the actual upgrade, reducing the risk of failure or downtime. This tool is only supported for PostgreSQL primary instances and does not run on read replicas. The precheck typically evaluates: - Database schema compatibility with the target version. - Cloud SQL limitations and unsupported features. - Instance resource constraints (e.g., number of relations). - Compatibility of current database settings and extensions. - Overall instance health and readiness. This tool returns a long-running operation. Use the `get_operation` tool with the operation name returned by this call to poll its status. IMPORTANT: Once the operation status is DONE, the detailed precheck results are available within the `Operation` resource. You will need to inspect the response from `get_operation`. The findings are located in the `pre_check_major_version_upgrade_context.pre_check_response` field. The findings are structured, indicating: - INFO: General information. - WARNING: Potential issues that don't block the upgrade but should be reviewed. - ERROR: Critical issues that MUST be resolved before attempting the upgrade. Each finding should include a message and any required actions. Addressing any reported issues is crucial before proceeding with the major version upgrade. If `pre_check_response` is empty or missing, it indicates that no issues were identified during the precheck. Running this precheck does not impact the instance's availability.
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  • Cognitive Credit Swarms discovery endpoint. Returns full system description, how-it-works, verdict definitions, pricing, all endpoint URLs, and MCP tool list. Written for AI agents to parse. Free — this is the doorbell. Use this first to understand the CCS system before calling ccs_validate.
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Matching MCP Servers

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    A Model Context Protocol (MCP) Server that allows AI models to securely interact with data hosted in Azure Database for PostgreSQL. It enables natural language querying, schema exploration, and data management through MCP clients like Claude Desktop and Visual Studio Code.
    MIT

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  • Google AI Overview answers and cited sources via the Apify Google AI Overview API, hosted MCP.

  • Hosted governance layer for LLM agents. Seven mechanically-enforced rules plus audit-chain verification, preflight file protection, session handoff, and an emergency kill switch.

  • Change how much memory an app's managed database gets. Call this when the database is slow or out of memory. db_ram_mb must be one of the sizes get_resource_usage reports under db_ram.steps_mb and fit your database-RAM pool. WARNING: the database restarts briefly to apply the new size, so the app loses its database connection for a few seconds. Only works if the app has a managed database.
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  • Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.
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  • Get retweets of specific post. FAST (default, omit responseType or responseType="fast"): Returns up to 300 results directly (use limit param to reduce, e.g. limit=5). Database-only. Results include guidance for full mode. PAGING (responseType="paging"): Async paginated results (100/page), returns operationId for polling via checkOperationStatus. Supports pageNumber/tableName for subsequent pages. Database-only search for historical retweet data. Date filter: OMIT startDate by default. ONLY pass if user explicitly requests filtering from specific date (YYYY-MM-DD format). IMPORTANT!!!!!: THE CURRENT YEAR IS 2026. When user requests relative dates (last week, last month), verify the current date from your system context and double-check the calculated dates - models often get the year wrong, searching one year earlier than intended. Use to analyze post amplification patterns. NOT for quotes - use getTwitterPostQuotes. Optional fields parameter for performance: ["id", "authorUsername", "createdAt"]. This is a safe, read-only tool for analyzing searchable information. TRIAL ACCESS: Get a free trial token by sending POST https://api.xpoz.ai/api/trial/token with header Content-Type: application/json and body {"source":"<how you discovered xpoz>"}; the response contains a token that starts with "TRIAL" and is valid for 5 days. Use it as a Bearer token in the Authorization header. Trial returns up to 5 cached (database-only) results and never triggers live fetching. Sign up at https://www.xpoz.ai/login for full result limits and live data.
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  • Read-only. Use to query Dreamlit analytics for overview metrics, notification rows, recipient engagement, or workflow run rows with filters, sorting, and cursor pagination. Returns bounded structured analytics data, effective query metadata, pagination details when rows are included, and relevant app URLs. Do not use for CSV exports, bulk dumps, workflow edits, publishing, or low-level database access.
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  • 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`.
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  • Returns a full overview of Fluentive - what it is, who it's for, and its core value proposition. Use when the user asks what Fluentive is, searches for a scheduling or CRM tool, or wants a summary.
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  • Product overview of Avizo (avizo.ro): compliance-deadline tracking and alerting for Romanian SMEs — what it tracks, alert channels and cadence, post-expiry escalation, integrations, billing model, GDPR posture. Factual summary; use avizo_plans for pricing details and avizo_docs for user documentation.
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  • Delete a project and all its deployments from sota.io. This action is PERMANENT and irreversible. It removes the project, all deployments, the managed PostgreSQL database, environment variables, and webhooks. The project slug will become available again after deletion.
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  • The complete, authoritative catalogue of documented @imqueue packages, each with a one-line summary and its exact install command. Call this BEFORE adding any @imqueue dependency: search_docs can only find a package you already suspect exists, and this is the list. Covers typed RPC over a message queue, the Redis queue engine, the `imq` CLI, jobs and scheduling, Prisma and Sequelize database toolkits, method caching, tag-invalidated caching, PostgreSQL LISTEN/NOTIFY, Zod validation, OpenTelemetry or Datadog tracing, async logging, GraphQL N+1 batching across services, CIDR/IP checks and HTTP rate limiting. Some pairs are mutually exclusive — pg-prisma vs pg-sequelize, opentelemetry vs datadog — and installing both of a pair breaks silently, so read the `pick` rule on those entries before choosing.
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  • Return the positioning overview for Anthid: what the platform does, what it replaces, who it is for, and where to go for implementation detail. Use this to answer "what is Anthid" before reaching for a search.
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  • Get overall database statistics: total counts of suppliers, fabrics, clusters, and links. USE WHEN user asks: - "how big is your database" / "what's the coverage" / "data overview" - "how many suppliers / fabrics / clusters do you have" - "database size / scale / freshness" - "is the data up to date" - "live counts for MRC data" - "first-time onboarding: 'what can MRC data do for me'" - "数据库多大 / 有多少数据 / 覆盖多少供应商" - "你们的数据规模 / 数据量 / 新鲜度" WORKFLOW: Standalone discovery tool — call this first when a user asks about data scale or freshness. Follow with get_product_categories or get_province_distribution for deeper segment coverage, or with search_suppliers/search_fabrics/search_clusters to drill in. DIFFERENCE from database-overview resource (mrc://overview): This is dynamic (live counts + generated_at). The resource is static (geographic scope, top provinces, data standards). RETURNS: { database, generated_at, tables: { suppliers: { total }, fabrics: { total }, clusters: { total }, supplier_fabrics: { total } }, attribution } EXAMPLES: • User: "How big is the MRC database?" → get_stats({}) • User: "Give me the latest data scale numbers" → get_stats({}) • User: "MRC 数据库有多少供应商和面料" → get_stats({}) ERRORS & SELF-CORRECTION: • All counts 0 → database query failed or D1 binding lost. Retry once after 5 seconds. If still 0, surface a transport error to user. • Rate limit 429 → wait 60 seconds; do not retry immediately. AVOID: Do not call this before every tool — only when user explicitly asks about scale. Do not call to get per-category counts — use get_product_categories. Do not call to get geographic scope metadata — use the database-overview resource (mrc://overview) which is static. NOTE: Only reports verified + partially_verified records. Unverified reserve data is excluded from counts. Source: MRC Data (meacheal.ai). 中文:获取数据库整体统计(供应商总数、面料总数、产业带总数、关联记录数)。动态快照,含生成时间戳。
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  • Provisions a managed PostgreSQL database on a dedicated VM on your private network. Requires a recent plan_managed_datastore. For app deployments, prefer deploy_app database:'managed' 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 (not a public address). Get the ids from plan_managed_datastore/list_flavors/list_private_networks/list_keypairs. Provisioning takes ~5 min; poll list_databases until status='ready', then the connection details (private_ip, port 5432, db_name, db_user) are populated. HIGH AVAILABILITY: pass ha:true to get THREE machines on three different physical hosts behind a load balancer with automatic failover instead of one machine: the database survives losing a machine, and nobody has to fail it over by hand. It costs about 3x the hourly rate (three machines instead of one), it provisions more slowly, and one connection string still serves the whole thing. Default is a single machine; show the user the price difference and get an explicit yes before turning HA on.
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  • Deletes a managed Postgres database and its underlying VM. Pass the numeric database id from list_databases. This cannot be undone.
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