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458,064 tools. Updated 2026-08-14 20:18

"Microsoft SQL Server: Information and Resources" matching MCP tools:

  • 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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  • Turn a Microsoft Advertising campaign, AD GROUP or AD on (Active) or off (Paused). Pass level:"campaign" + campaignId, level:"adGroup" + adGroupId, or level:"ad" + BOTH adGroupId and adId. ACTIVATING STARTS REAL AD SPEND — you MUST first show the user the campaign name + its daily budget, get an explicit yes, then call with status:"Active" and confirm:true. Pausing is always safe. Microsoft has only these two statuses — its Deleted state is internal-only and cannot be SET — so to remove something use delete_microsoft_ads_object, which is a real delete operation, not a status. The resulting status is READ BACK from Microsoft before you are told it took — and Microsoft may report BudgetPaused / BudgetAndManualPaused / Suspended instead, which the note names explicitly.
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  • Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.
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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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  • 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 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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  • 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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  • Build a campaign on a connected Microsoft Advertising (Bing Ads) account. ALWAYS created Paused — it spends NOTHING until you activate it with set_microsoft_ads_status(confirm:true). Microsoft’s object graph is campaign → ad group → responsive search ad → keywords, so a campaign ON ITS OWN CANNOT SERVE AN IMPRESSION: pass adGroup{name, ad{headlines,descriptions,finalUrls}, keywords[]} and this builds the whole tree. Microsoft has NO atomic multi-object write (unlike Google), so the levels are created in sequence and the campaign is DELETED again if anything below it is rejected — you never inherit a half-built campaign. Microsoft requires 3–15 headlines (≤30 chars) and 2–4 descriptions (≤90 chars); expanded text ads can no longer be created at all. dailyBudget is in the ACCOUNT’S currency, not necessarily USD. LOCATION TARGETING: pass locations[] (country / region / city names, ISO country codes, or numeric Microsoft location ids). A Microsoft campaign has NO geo targeting unless it is set, and Microsoft does not require any — so if you pass none, the campaign IS CREATED and serves WORLDWIDE (Microsoft’s own default), and the returned note says so loudly. That is safe at this stage because the campaign is Paused and spends nothing; it is NOT safe to activate without telling the user, so relay the warning. Nothing is created when a location you DID name cannot be resolved (call microsoft_ads_geo_search to disambiguate, then pass the id). Pass worldwide:true to record that everywhere was deliberate and suppress the nudge. The locations are written and READ BACK inside the same rollback as the rest of the tree, so a campaign is either targeted as asked or does not exist. Everything is READ BACK from Microsoft before you are told it exists; print the returned note verbatim, and if it says the campaign cannot serve yet, say that rather than calling it a finished ad.
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  • Maps only stable Tier1 finding identifiers to approved Tier1 services and public resources. Call after a Tier1 score or email-domain check. Do not submit prose, URLs, customer information, or invented identifiers. This tool performs no arbitrary fetching, makes no contact request, changes nothing, and stores nothing.
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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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  • Get Lenny Zeltser's one-page Vulnerability Advisory Brief template. Covers Bottom Line, Quick Facts, Are We Affected?, Defensive Actions (with What/Why/When/Who), What We Don't Know, and More Information. This server never requests your vulnerability notes and instructs your AI to keep them local—the brief template and guidelines flow to your AI for local analysis.
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  • Search official Microsoft/Azure documentation to find the most relevant and trustworthy content for a user's query. This tool returns up to 10 high-quality content chunks (each max 500 tokens), extracted from Microsoft Learn and other official sources. Each result includes the article title, URL, and a self-contained content excerpt optimized for fast retrieval and reasoning. Always use this tool to quickly ground your answers in accurate, first-party Microsoft/Azure knowledge. ## Follow-up Pattern To ensure completeness, use microsoft_docs_fetch when high-value pages are identified by search. The fetch tool complements search by providing the full detail. This is a required step for comprehensive results.
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  • Query an ArcGIS Feature Service / Map Service layer by its url (from search_datasets). SQL-like `where`, comma-separated `out_fields`, `order_by`, `limit`, `offset`. Returns attribute rows (and geometry). Use where="1=1" + out_fields="*" to sample.
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  • Run a read-only SQL SELECT against a DataCanvas table staged by fema_search_nfip. Enables aggregation, GROUP BY, SUM/COUNT, time-series, and filtered analysis over the full NFIP claims result without re-fetching from the API. Call fema_dataframe_describe first to get the exact table name and column names needed for valid SQL. Only SELECT statements are allowed — DDL, DML, COPY, and file-reading functions are blocked.
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  • Return the protected URL for Red's content administration page (Freshdesk articles, YouTube videos, and visibility controls). Use when a Big Red Book / Big Red Cloud staff member asks to open Red's admin page, the BRC Edu admin page, or the content resources admin. Returns only the customer-facing protected admin URL — never a shared secret, query parameter, token, or bypass link. Opening the link still requires Microsoft Entra sign-in; only authorised staff can access the page. Does not bypass authentication. Does not require a connected company. Do not invent or append secret query parameters. Do not expose BRC_EDU_ADMIN_UPLOAD_SECRET or any upload secret.
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  • Read the brand’s connected Microsoft Advertising (Bing Ads) account(s). Call with NO accountId to list the accounts shared with this brand — do this first to pick a target. Call WITH accountId to list that account’s campaigns (id, name, status, daily budget, campaign type, and whether the budget is SHARED). Microsoft statuses are Active / Paused — never Google’s ENABLED — and Microsoft also sets BudgetPaused, BudgetAndManualPaused and Suspended on its own, so report the status you read rather than assuming a paused campaign was paused by a person. Read-only, free. Needs Microsoft Advertising connected (Settings ▸ Connectors ▸ Microsoft Advertising).
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  • Add keywords to a Microsoft Advertising ad group. Match types are Exact, Phrase and Broad — Microsoft has no broad-match-modifier. Keywords are added Paused unless you set status:"Active"; an Active keyword on a live ad group makes the campaign bid on a new term immediately, so that needs confirm:true. Note that per-keyword bids are honoured but ad-group / keyword BID STRATEGIES are silently ignored by Microsoft — they inherit the campaign’s. Only the keywords Microsoft confirms on the read-back are reported as added.
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  • List the tables and columns staged on a canvas by chembl_get_bioactivities — inspect before calling chembl_dataframe_query to write correct SQL. Returns each table with its row count, kind (table | view), and column names + types. Requires CANVAS_PROVIDER_TYPE=duckdb.
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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 `projectId` field.
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  • Fetch and convert a Microsoft Learn documentation webpage to markdown format. This tool retrieves the latest complete content of Microsoft documentation webpages including Azure, .NET, Microsoft 365, and other Microsoft technologies. ## When to Use This Tool - When search results provide incomplete information or truncated content - When you need complete step-by-step procedures or tutorials - When you need troubleshooting sections, prerequisites, or detailed explanations - When search results reference a specific page that seems highly relevant - For comprehensive guides that require full context ## Usage Pattern Use this tool AFTER microsoft_docs_search when you identify specific high-value pages that need complete content. The search tool gives you an overview; this tool gives you the complete picture. ## URL Requirements - The URL must be a valid HTML documentation webpage from the microsoft.com domain - Binary files (PDF, DOCX, images, etc.) are not supported ## Output Format markdown with headings, code blocks, tables, and links preserved.
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