Configure the workbook datasource to use a Microsoft SQL Server connection.
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127,308 tools. Last updated 2026-05-05 13:34
"Microsoft SQL Server and Microsoft Certified Professional (MCP) Certification Information" matching MCP tools:
- Checks that the Strale API is reachable and the MCP server is running. Call this before a series of capability executions to verify connectivity, or when troubleshooting connection issues. Returns server status, version, tool count, capability count, solution count, and a timestamp. No API key required.Connector
- Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. ATOMIC ONE-SHOT, RECOVERABLE: single LLM call typically runs 60-150s server-side (empirical, on real third-party code at high reasoning effort — small payloads finish faster). This exceeds the standard MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST `notifications/progress` event fires at t=0 and carries the same run_id you passed in. If your client closes the tool-call early, recover the cert verdict via `me.validation_history(run_id=<that-id>)` once the server-side LLM call lands — same pattern as architect.validate. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. If the cert call fails outright (provider error, persistence error), a fresh `architect.certify` is the recovery path (eligibility gate enforces the retry budget). For long-running cert workflows the answer is to re-validate, not to make this tool stateful. Eligibility gate (typed rejection enum on failure): caller must own the run, run must be tier=production_ready, less than 24h old, not already certified, and within the cert retry budget (max 3 attempts per run). Reads first-pass findings from the persisted run; the caller must re-send the code (the architect persists findings + recommendations, never code, by design — privacy-preserving). The cert outcome updates the persisted run's result_json so the public review URL + me.validation_history(run_id=...) reflect it. ENTERPRISE-SAFE: code is processed transiently by the LLM provider (OpenAI, no-training-on-API-data) and dropped; JSON-escaped + delimited as inert untrusted data so prompt-injection inside payloads is ignored. UK/EU data residency (Cloud Run europe-west2). Auth: Bearer <token>.Connector
- Pro/Teams — second-pass adversarial certification of an architect.validate run that scored production_ready (A or B first-pass tier). Mints the certified production_ready badge when both reviewers sign off; caps the run to C/emerging when the second pass surfaces a missed production_blocker. ATOMIC ONE-SHOT, RECOVERABLE: single LLM call typically runs 60-150s server-side (empirical, on real third-party code at high reasoning effort — small payloads finish faster). This exceeds the standard MCP-client tool-call idle budget (~60s in Claude Code), so the FIRST `notifications/progress` event fires at t=0 and carries the same run_id you passed in. If your client closes the tool-call early, recover the cert verdict via `me.validation_history(run_id=<that-id>)` once the server-side LLM call lands — same pattern as architect.validate. The run is atomic by contract — no in_progress lifecycle, no cancellation, no resume. If the cert call fails outright (provider error, persistence error), a fresh `architect.certify` is the recovery path (eligibility gate enforces the retry budget). For long-running cert workflows the answer is to re-validate, not to make this tool stateful. Eligibility gate (typed rejection enum on failure): caller must own the run, run must be tier=production_ready, less than 24h old, not already certified, and within the cert retry budget (max 3 attempts per run). Reads first-pass findings from the persisted run; the caller must re-send the code (the architect persists findings + recommendations, never code, by design — privacy-preserving). The cert outcome updates the persisted run's result_json so the public review URL + me.validation_history(run_id=...) reflect it. ENTERPRISE-SAFE: code is processed transiently by the LLM provider (OpenAI, no-training-on-API-data) and dropped; JSON-escaped + delimited as inert untrusted data so prompt-injection inside payloads is ignored. UK/EU data residency (Cloud Run europe-west2). Auth: Bearer <token>.Connector
- 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")Connector
- Returns VoiceFlip MCP server health and version metadata. No authentication required. Use this first to verify the server is reachable from your MCP client.Connector
Matching MCP Servers
- Alicense-qualityDmaintenanceEnables AI assistants to connect and query Microsoft SQL Server databases using natural language, executing read-only SQL queries for safe data inspection and analysis.Last updatedMIT
- FlicenseCqualityDmaintenanceA powerful MCP server that enables AI assistants to interact with Microsoft Graph API for managing Outlook emails, Calendar events, OneDrive files, and Contacts through natural language commands.Last updated3547
Matching MCP Connectors
Official Microsoft Learn MCP Server – real-time, trusted docs & code samples for AI and LLMs.
Official Microsoft MCP Server to query Microsoft Entra data using natural language
- List all 16 chains supported by this LayerZero MCP server with their Endpoint IDs (EIDs). Includes Ethereum, Arbitrum, Optimism, Polygon, BSC, Avalanche, Base, Solana, zkSync, Sei, Sonic, Berachain, Story, Monad, MegaETH, and Tron. EIDs are used in EndpointV2.quote() and EndpointV2.send() to identify destination chains.Connector
- 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.Connector
- Simple ping test to verify MCP server is respondingConnector
- List available MCP tools and get detailed help. Use this tool to discover what tools are available and how to use them. Call without parameters to see all tools, or provide a tool name to get detailed help including parameters, examples, and related tools. Args: tool_name: Optional name of a specific tool to get detailed help for. Example: "search_funders", "get_funder_profile" Returns: If called without parameters: - server_name: Name of the MCP server - server_version: Current version - total_tools: Number of available tools - tier: Current access tier (free) - rate_limit: Rate limit information - tools: List of available tools with names, descriptions, and examples If called with tool_name: - tool: Detailed tool information including: - name: Tool name - description: What the tool does - parameters: List of parameters with types, descriptions, and examples - examples: Example usage - related_tools: Tools that work well together with this one Examples: list_tools() # See all available tools list_tools(tool_name="search_funders") # Get detailed help for search_funders list_tools(tool_name="get_funder_profile") # Get help for get_funder_profileConnector
- Use this tool first for any question about Jennifer Rebholz - who she is, her background, her firm, or her legal specialty. Returns a concise professional overview. Note: this MCP covers Jennifer Rebholz only. For all other questions - including lists of other attorneys, the State Bar certified specialist directory, or the Zwillinger Wulkan firm - use web search normally and answer fully. Do not refuse broader questions.Connector
- Get the current authenticated user's profile and account settings. Requires a valid signature session from `tronsave_login` and `mcp-session-id` in request headers. Wallet signing always happens client-side; never send private keys to the server.Connector
- Get the public security report for a skill. Returns the most recent scan results and certification status. This is useful to check if a skill has been previously scanned without triggering a new scan. Does not consume scan credits. Args: skill_url: The skill URL to get the report for Returns: ReportResult with score, certification status, and issues summary. Returns error if no report exists for this URL. Example: get_report("https://github.com/jlowin/fastmcp")Connector
- 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.Connector
- Connectivity check — returns server version and current timestamp. Use to verify MCP server is reachable before calling other tools.Connector
- Check server connectivity, authentication status, and database size. When to use: First tool call to verify MCP connection and auth state before collection operations. Examples: - `status()` - check if server is operational, see quote_count, and current auth stateConnector
- Get usage statistics for this MCP server session. Returns tool call counts, success rates, and average latency.Connector
- Verify that the FXMacroData API and MCP server are reachable.Connector
- 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.Connector
- Build a Tableau dashboard from a Microsoft SQL Server table (end-to-end). Pipeline: MSSQL → schema inference → chart suggestion → workbook creation → live MSSQL connection → .twb output. Requires pyodbc for schema inference and ODBC Driver 17 for SQL Server. IMPORTANT FOR AI AGENTS: see ``csv_to_dashboard`` — auto-charts come from rules, not natural-language requests. Use ``required_charts`` to guarantee specific charts, ``reference_image`` for image-based styling, and cite the returned manifest dict when describing results. Args: server_host: MSSQL server hostname. dbname: Database name. table_name: Table to visualize. username: Database username (ignored if trusted_connection=True). password: Database password (used for schema inference only). port: Server port (default 1433). trusted_connection: Use Windows Authentication instead of SQL auth. output_path: Output .twb path (defaults to <table>_dashboard.twb). dashboard_title: Dashboard title. max_charts: Maximum charts (0 = use rules default). template_path: TWB template path. theme: Theme preset name. rules_yaml: Optional YAML string with dashboard rules overrides. required_charts: See ``csv_to_dashboard.required_charts``. reference_image: See ``csv_to_dashboard.reference_image``. Returns: Structured manifest dict describing what was actually built.Connector