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127,246 tools. Last updated 2026-05-05 12:02

"A server for retrieving weather information from the internet" matching MCP tools:

  • 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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  • Returns VoiceFlip MCP server health and version metadata. No authentication required. Use this first to verify the server is reachable from your MCP client.
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  • Get the horse racing venue list (racecourses). Surface (turf/dirt/all-weather) varies by card and is reported per-meeting, not per-venue.
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  • Retrieves AI-generated summaries of web search results using Brave's Summarizer API. This tool processes search results to create concise, coherent summaries of information gathered from multiple sources. When to use: - When you need a concise overview of complex topics from multiple sources - For quick fact-checking or getting key points without reading full articles - When providing users with summarized information that synthesizes various perspectives - For research tasks requiring distilled information from web searches Returns a text summary that consolidates information from the search results. Optional features include inline references to source URLs and additional entity information. Requirements: Must first perform a web search using brave_web_search with summary=true parameter. Requires a Pro AI subscription to access the summarizer functionality.
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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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  • Retrieves comprehensive weather data including current conditions, hourly, and daily forecasts. **Specific Data Available:** Temperature (Current, Feels Like, Max/Min, Heat Index), Wind (Speed, Gusts, Direction), Celestial Events (Sunrise/Sunset, Moon Phase), Precipitation (Type, Probability, Quantity/QPF), Atmospheric Conditions (UV Index, Humidity, Cloud Cover, Thunderstorm Probability), and Geocoded Location Address. **Location & Location Rules (CRITICAL):** The location for which weather data is requested is specified using the `location` field. This field is a 'oneof' structure, meaning you MUST provide a value for ONLY ONE of the three location sub-fields below to ensure an accurate weather data lookup. 1. Geographic Coordinates (lat_lng) * Use it when you are provided with exact lat/lng coordinates. * Example: {"location": {"lat_lng": {"latitude": 34.0522, "longitude": -118.2437}}} // Los Angeles 2. Place ID (place_id) * An unambiguous string identifier (Google Maps Place ID). * The place_id can be fetched from the search_places tool. * Example: {"location": {"place_id": "ChIJLU7jZClu5kcR4PcOOO6p3I0"}} // Eiffel Tower 3. Address String (address) * A free-form string that requires specificity for geocoding. * City & Region: Always include region/country (e.g., "London, UK", not "London"). * Street Address: Provide the full address (e.g., "1600 Pennsylvania Ave NW, Washington, DC"). * Postal/Zip Codes: MUST be accompanied by a country name (e.g., "90210, USA", NOT "90210"). * Example: {"location": {"address": "1600 Pennsylvania Ave NW, Washington, DC"}} **Usage Modes:** * **Current Weather:** Provide `location` only. Do not specify `date` and `hour`. * **Hourly Forecast:** Provide `location`, `date`, and `hour` (0-23). Use for specific times (e.g., "at 5 PM") or terms like "next few hours" or "later today". If the user specifies minute, round down to the nearest hour. Hourly forecast beyond 120 hours from now is not supported. Historical hourly weather is supported up to 24 hours in the past. * **Daily Forecast:** Provide `location` and `date`. Do not specify `hour`. Use for general day requests (e.g., "weather for tomorrow", "weather on Friday", "weather on 12/25"). If today's date is not in the context, you should clarify it with the user. Daily forecast beyond 10 days including today is not supported. Historical weather is not supported. **Parameter Constraints:** * **Timezones:** All `date` and `hour` inputs must be relative to the **location's local time zone**, not the user's time zone. * **Date Format:** Inputs must be separated into `{year, month, day}` integers. * **Units:** Defaults to `METRIC`. Set `units_system` to `IMPERIAL` for Fahrenheit/Miles if the user implies US standards or explicitly requests it. * The grounded output must be attributed to the source using the information from the `attribution` field when available.
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  • Real-time weather conditions and multi-day forecasts via Open-Meteo — free, no API key required

  • Forecasts, climate history, severe alerts by location — for outdoor-event planners.

  • Retrieves comprehensive weather data including current conditions, hourly, and daily forecasts. **Specific Data Available:** Temperature (Current, Feels Like, Max/Min, Heat Index), Wind (Speed, Gusts, Direction), Celestial Events (Sunrise/Sunset, Moon Phase), Precipitation (Type, Probability, Quantity/QPF), Atmospheric Conditions (UV Index, Humidity, Cloud Cover, Thunderstorm Probability), and Geocoded Location Address. **Location & Location Rules (CRITICAL):** The location for which weather data is requested is specified using the `location` field. This field is a 'oneof' structure, meaning you MUST provide a value for ONLY ONE of the three location sub-fields below to ensure an accurate weather data lookup. 1. Geographic Coordinates (lat_lng) * Use it when you are provided with exact lat/lng coordinates. * Example: {"location": {"lat_lng": {"latitude": 34.0522, "longitude": -118.2437}}} // Los Angeles 2. Place ID (place_id) * An unambiguous string identifier (Google Maps Place ID). * The place_id can be fetched from the search_places tool. * Example: {"location": {"place_id": "ChIJLU7jZClu5kcR4PcOOO6p3I0"}} // Eiffel Tower 3. Address String (address) * A free-form string that requires specificity for geocoding. * City & Region: Always include region/country (e.g., "London, UK", not "London"). * Street Address: Provide the full address (e.g., "1600 Pennsylvania Ave NW, Washington, DC"). * Postal/Zip Codes: MUST be accompanied by a country name (e.g., "90210, USA", NOT "90210"). * Example: {"location": {"address": "1600 Pennsylvania Ave NW, Washington, DC"}} **Usage Modes:** * **Current Weather:** Provide `location` only. Do not specify `date` and `hour`. * **Hourly Forecast:** Provide `location`, `date`, and `hour` (0-23). Use for specific times (e.g., "at 5 PM") or terms like "next few hours" or "later today". If the user specifies minute, round down to the nearest hour. Hourly forecast beyond 120 hours from now is not supported. Historical hourly weather is supported up to 24 hours in the past. * **Daily Forecast:** Provide `location` and `date`. Do not specify `hour`. Use for general day requests (e.g., "weather for tomorrow", "weather on Friday", "weather on 12/25"). If today's date is not in the context, you should clarify it with the user. Daily forecast beyond 10 days including today is not supported. Historical weather is not supported. **Parameter Constraints:** * **Timezones:** All `date` and `hour` inputs must be relative to the **location's local time zone**, not the user's time zone. * **Date Format:** Inputs must be separated into `{year, month, day}` integers. * **Units:** Defaults to `METRIC`. Set `units_system` to `IMPERIAL` for Fahrenheit/Miles if the user implies US standards or explicitly requests it. * The grounded output must be attributed to the source using the information from the `attribution` field when available.
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  • Fetch HTTP response headers for a URL. Use when inspecting server configuration, security headers, or caching policies.
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  • Composite server-side investigation tool. Pass a question and the server automatically: (1) detects intent (aggregation/temporal/ordering/knowledge-update/recall), (2) queries the entity index for structured facts, (3) builds a timeline for temporal questions, (4) retrieves memory chunks with the right scoring profile, (5) expands context around sparse hits, (6) derives counts/sums for aggregation, (7) assesses answerability, and (8) returns a recommendation. Use this as your FIRST tool for any non-trivial question — it does the multi-step investigation that would otherwise take 4-6 individual tool calls. The response includes structured facts, timeline, retrieved chunks, derived results, answerability assessment, and a recommendation for how to answer.
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  • Analyze an image from a component's datasheet using vision AI. Use this when read_datasheet returns a section containing images and you need to extract data from a graph, package drawing, pin diagram, or circuit schematic. Pass the image_key from the read_datasheet response (the storage path in the image URL). Optionally pass a specific question to focus the analysis. IMPORTANT: For precise numeric values (electrical specs, max ratings), prefer read_datasheet text tables first — they are more reliable than vision-extracted graph data. Use analyze_image for visual information not available in text: package dimensions from drawings, pin assignments from diagrams, graph trends, and approximate values from characteristic curves. Examples: - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png') -> classifies and describes the image - analyze_image(part_number='IRFZ44N', image_key='images/abc123.png', question='What is the drain current at Vgs=5V?')
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  • 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.
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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 camping-specific 7-day weather forecast with suitability ratings and gear recommendations. Uses the Open-Meteo API (no key required) to provide: - Daily high/low temps, conditions, precipitation, wind - Camping suitability rating per day (excellent/good/fair/poor/not_recommended) - Gear recommendations based on conditions - Safety warnings for extreme weather - Best camping days in the forecast period Args: latitude: Campground latitude longitude: Campground longitude campground_name: Campground name for context (optional) check_in_date: Planned check-in date ISO format (optional) check_out_date: Planned check-out date ISO format (optional)
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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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  • Run a generic M/M/c queue simulation. Provide an arrival rate (λ, arrivals/hour), a service rate per server (μ, customers/hour each server can finish), and a server count (c). Optional: distribution shapes, service coefficient of variation, run length. Returns per-hour metrics and an overall summary (avg wait, queue length, offered load, throughput). This is the primary tool for 'how many servers do I need?' / 'what's my average wait?' style questions. ALSO preferred over simulate_scenario for what-if questions about scheduled scenarios (Coffee Shop, ER) when the user wants flat uniform numbers — pull the peak params from describe_scenario and run them here. That usually matches user intent better than collapsing a schedule.
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  • Find weather observation stations near a location. Returns stations sorted by proximity with distance and bearing. Use to discover station IDs for nws_get_observations.
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  • Get a multi-day weather forecast for any Swiss location. Returns daily summaries with temperature, precipitation, and weather icons. This uses official MeteoSwiss Open Data — the same forecasts powering the MeteoSwiss app and website. Accepts: - Postal codes: "8001" (Zurich), "3000" (Bern), "1200" (Geneva) - Station abbreviations: "ZUE" (Zurich Fluntern), "BER" (Bern) - Place names: "Zurich", "Basel", "Lugano" Coverage: ~6000 Swiss locations (all postal codes + weather stations + mountain points). Forecast horizon: up to 9 days. Updated hourly.
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  • Bridge an A2A (Agent-to-Agent Protocol) task to an MCP server. Receives an A2A task, identifies the best matching MCP tool on the target server, executes it, and returns the result wrapped in A2A response format. Enables A2A agents to use any MCP server transparently. Extracts the intent from the A2A task, maps it to an MCP tool, calls the tool, and wraps the result in A2A response format. Use this to let A2A agents interact with any MCP server. Requires authentication.
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  • Get full details of a specific NWS weather alert by its ID. Returns the complete alert including description, instructions, and affected areas. Use alert IDs from get_alerts results. Args: alert_id: The full NWS alert ID (e.g. 'urn:oid:2.49.0.1.840.0.xxx').
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  • Detect unusual electricity demand patterns that signal manufacturing disruptions before they appear in official reports. Monitors 8 US power grid regions (PJM, MISO, ERCOT, CAISO, SPP, ISNE, NYISO, NW) for demand anomalies — sudden drops indicate factory shutdowns, surges indicate production ramp-ups. Returns current SMI score with regional breakdown plus anomalies from the past 7 days ranked by severity. The Supply Manufacturing Index (SMI) uses patent-pending weather normalization to isolate industrial demand from weather-driven consumption. Used by commodity traders for early manufacturing signals and procurement teams to anticipate supply changes.
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