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162,351 tools. Last updated 2026-05-30 07:24

"Information about SQL Server" matching MCP tools:

  • Returns information about safety features on Makuri, including age verification, content filtering, parental controls, and AI safety guardrails. Use when the user asks about child safety, content moderation, or how Makuri protects minors.
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  • Switch between local and remote DanNet servers on the fly. This tool allows you to change the DanNet server endpoint during runtime without restarting the MCP server. Useful for switching between development (local) and production (remote) servers. Args: server: Server to switch to. Options: - "local": Use localhost:3456 (development server) - "remote": Use wordnet.dk (production server) - Custom URL: Any valid URL starting with http:// or https:// Returns: Dict with status information: - status: "success" or "error" - message: Description of the operation - previous_url: The URL that was previously active - current_url: The URL that is now active Example: # Switch to local development server result = switch_dannet_server("local") # Switch to production server result = switch_dannet_server("remote") # Switch to custom server result = switch_dannet_server("https://my-custom-dannet.example.com")
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  • 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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  • REQUIRED before stock_data_query, 19 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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  • List every Stimulsoft product/platform that has indexed documentation available through this MCP server. Returns a JSON array of { id, name, description } objects covering the full Stimulsoft Reports & Dashboards product line (Reports.NET, Reports.WPF, Reports.AVALONIA, Reports.WEB for ASP.NET, Reports.BLAZOR, Reports.ANGULAR, Reports.REACT, Reports.JS, Reports.PHP, Reports.JAVA, Reports.PYTHON, Server API, etc.). CALL THIS FIRST when the user's question is ambiguous about which Stimulsoft platform they are using, or when you need to pick a valid `platform` value to pass into `sti_search`. The returned platform `id` values are the exact strings accepted by the `platform` parameter of `sti_search`. This tool is cheap (no OpenAI call, no vector search) — call it freely whenever you are unsure about platform naming.
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  • Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.
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  • 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: -- 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 have the job label `goog-mcp-server: true` automatically set. Queries are charged to the project specified in the `project_id` field.
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  • Get full details for a single broker (agent) by their profile slug. Call this when the user asks for more information about a specific broker. Use the slug from search_brokers results.
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  • Returns structured information about what the Recursive platform includes: features, AI model details, supported integrations, and what's included at every tier. Use for systematic feature comparison.
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  • 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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  • 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.
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  • Returns general information about the Makuri platform, including mission, target users, founding details, and company information. Use this tool when the user asks 'what is Makuri', 'who made it', or wants a general overview.
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  • 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.
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  • Creates and saves a new use case (reusable analysis). **When to use this tool:** - When the user asks to "save this analysis", "create a use case", "remember this query" - After building a SQL query the user wants to reuse - To capitalize on a recurring business analysis **Available scopes:** - 'member' (default): Personal use case, visible only to you - 'project': Shared with the entire project team (requires project_id) **Best practices:** - Slug: technical identifier in snake_case (e.g., weekly_campaign_performance) - Name: human-readable name (e.g., "Weekly Campaign Performance") - Description: explain the business context and when to use this analysis - SQL template: include the SQL query if it's generic and reusable
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  • Execute any valid SQL statement, including data definition language (DDL), data control language (DCL), data query language (DQL), or data manipulation language (DML) statements, on a Cloud SQL instance. To support the `execute_sql` tool, a Cloud SQL instance must meet the following requirements: * The value of `data_api_access` must be set to `ALLOW_DATA_API`. * For built_in users password_secret_version must be set. * Otherwise, for IAM users, 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`. * 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` tool has the following limitations: * If a SQL statement returns a response larger than 10 MB, then the response will be truncated. * The `execute_sql` 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 `execute_sql` 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` 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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  • Get detailed information about a specific train connection including all intermediate stops, platforms, and occupancy. Use a trip ID from search_connections results.
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  • 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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  • 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 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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  • Get full details for a single business (listing) by its slug. Call this when the user asks for more information about a specific business. Use the slug from search_businesses results.
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