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524,397 tools. Updated 2026-09-06 15:30

"Microsoft SQL Server 2008 Overview and Information" 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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  • 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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  • 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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  • [START HERE — market overview] One-call morning brief: market regime (risk appetite), liquidity read, high-impact events next 72h, the engine's actionable reads (has_trade_signal + reads[]), and cross-asset trade-plan ranking — compact projections of get_market_regime / get_liquidity_map / get_economic_calendar / get_actionable_signals / rank_trades, assembled server-side. Optional coin arg scopes the actionable reads to that coin and adds it to the ranked set (ranking stays setup_score-sorted). Drill into any block with the underlying tool. Descriptive + engine verdicts; uncalibrated blocks labeled; not financial advice.
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  • FREE — costs nothing, call it as often as you like. USE THIS FIRST when you know a game by NAME but not by identifier. Returns matching games with their slug, release date and type (main game, DLC, expansion, port, remaster) so you can pick the right one before paying. Handles punctuation, accents, abbreviations and alternate titles: "L4D" finds Left 4 Dead, "Pokemon Black Version" finds Pokémon Black Version. 12,388 titles in the catalogue are duplicated — Pac-Man appears 54 times — so the release date and type are how you tell a 1980 original from a 2008 remaster. Returns no prices. Take the slug and call get_game with it.
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Matching MCP Servers

  • A
    license
    Not graded
    quality
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    maintenance
    Enables AI assistants to connect and query Microsoft SQL Server databases using natural language, executing read-only SQL queries for safe data inspection and analysis.
    MIT
  • A
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    Not graded
    quality
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    maintenance
    This read-only MCP Server allows you to connect to Microsoft SQL Server data from Claude Desktop through CData JDBC Drivers. For full CRUD support, check out the first managed MCP platform: CData Connect AI (https://www.cdata.com/ai/).
    1
    MIT

Matching MCP Connectors

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

  • Microsoft OneNote (Microsoft 365) MCP Pack

  • 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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  • 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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  • 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: ```sql -- 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 always have the job label `goog-mcp-server: true` automatically set in addition to any custom `labels` provided in the request. Queries are charged to the project specified in the `project_id` field.
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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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  • Published Truss information by topic. Localized topics (overview, about, services, engagement, fit, faq) use locale, default en; pass he for Hebrew. Language-independent topics (identity, certifications, testimonials, clients, contact) ignore locale for content selection. Prefer get_truss_overview or topic overview for broad business understanding; prefer list_truss_services for the complete service catalog.
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  • Complete weather overview for a location: current conditions, daily forecast (day/night periods, SPC threats, severity, CAPE, UV), active alerts, and convective outlooks in one call. Data is pre-aggregated across NBM, HRRR, GFS, RTMA, and SPC and unit-converted server-side. This is the primary weather tool; reach for lower-level tools only when you need raw observations or a specific dataset. Accepts a place name directly. Examples: {"location": "Denver"} or {"location": "Portland, OR", "days": 5} or {"lat": 41.4, "lon": -92.9}.
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  • Discover the AdKit MCP contract with progressive drill-down. Top-level paths: "manage" (campaigns, accounts, and reporting across Meta, Google, TikTok, Reddit, X, LinkedIn, and Microsoft, plus drafts), "library" (browse ads library and advertiser list), and "studio" (AI static ad generation). Start with adkit_help() for the root overview, or drill down like adkit_help({ path: "manage google campaigns create" }). A keyword search with q, e.g. adkit_help({ q: "keyword planner" }) → "manage google research keywords", covers the full command catalog: a capability missing from one drill-down path may still exist under another.
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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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  • Query cryptographically verified attributes from Lemma. Use this as the primary tool for finding documents whose attributes match given conditions (e.g., "subject's birthYear lt 2008"). Returns { results: Array<{ docHash, schema, issuerId, subjectId, attributes, isVerified, proof?: { status, circuitId, chainId }, disclosure? }>, hasMore }. The MCP layer enriches each item with an `isVerified` flag derived from `proof.status` (true when status is 'verified' or 'onchain-verified'). Use lemma_get_proof_status to monitor a specific proof; use lemma_get_schema to interpret the keys returned in `attributes`.
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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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  • Get a secure one-time link to register a new database connection with ThinAir Data (postgres, mysql, or mssql). This tool does NOT take a connection string as input — you'll open the returned link and paste the connection string into a secure web form; it is never sent through chat. The response includes `connection_string_format` and `auth_note` for the chosen dialect — surface both to the user verbatim. IMPORTANT for mssql: Azure SQL uses Microsoft Entra, so the connection string is HOST/DATABASE only (no credentials) and the tenant/client/secret go in the form's separate fields — never construct or suggest an `mssql://CLIENT_ID:CLIENT_SECRET@host` string (client secrets break URL parsing).
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  • Provision a Floot-managed backend resource for the project — fully server-side (Floot mints all secrets; no keys to paste). Also seeds the working code for it. Available: - database — A Floot-managed Postgres database (Neon). FLOOT_DATABASE_URL is set for the app. - auth — Email/password + session auth (JWT_SECRET, auto-provisions a database if none). Injects auth pages, endpoints, and helpers. - oauth-login — Sign in with Google via Floot's brokered OAuth (FLOOT_OAUTH). Injects OAuth provider classes, login buttons, helpers. - microsoft-login — Sign in with Microsoft via Floot's brokered login (FLOOT_MICROSOFT_LOGIN). Injects button + auth endpoints. - google-integration — Google API access (Gmail/Calendar/etc.) via Floot's brokered Google OAuth (FLOOT_GOOGLE_INTEGRATIONS). Injects Connect button + endpoints. - microsoft-integration — Microsoft Graph access (Outlook/Teams/etc.) via Floot's brokered Microsoft OAuth (FLOOT_MICROSOFT_INTEGRATIONS). Injects Connect button + endpoints. - push-notifications — Web + native push (FLOOT_PUSH). Mints VAPID keys, injects helpers/pushClient (subscribe/unsubscribe) + a service worker. Enum values not listed above are beta-gated and unavailable on most accounts. SENDING email from the app is NOT a resource — the builtin @floot/email handles it with zero setup (get_guides("email")). For a user's OWN external key (their OpenAI key, an external database), this is NOT the tool — use request_external_resource instead. Idempotent: re-running returns the existing resource and skips seed files that already exist.
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  • REQUIRED before stock_data_query, 23 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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