Edit README.md

agentic-workflows/README.md

@@ -57,11 +57,11 @@ Provides real-time weather and a 24-hour forecast using the Open-Meteo API.
 - JSON includes timezone, elevation, and other metadata
 
 **Sample Prompts**:
-1. Retrieve current weather and 24-hour forecast for '{city_name}' with temperature, precipitation, and wind speed.
-2. Get localized weather data for a GPS coordinate to validate a climate model.
-3. Fetch timezone, elevation, and forecast data for environmental analysis.
-4. Provide detailed meteorological data for travel or event planning in '{city_name}'.
-5. Obtain weather data for disaster preparedness in a specific region.
+1. Retrieve current weather and 24-hour forecast for {city} with temperature, precipitation, and wind speed.
+1. Get localized weather data for {city} to validate a climate model using GPS coordinates.
+1. Fetch timezone, elevation, and forecast data for {city} for environmental analysis.
+1. Provide detailed meteorological data for travel or event planning in {city}.
+1. Obtain weather data for disaster preparedness in {city} or its surrounding region.
 
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@@ -91,11 +91,9 @@ Finds nearby tourist attractions and amenities using OpenStreetMap.
 - Returns POIs with names, types, and coordinates
 
 **Sample Prompts**:
-1. List tourist attractions and amenities within walking distance of '{address}'.
-2. Find venues near '{address}' for event planning.
-3. Show family-friendly locations like parks and cafés near '{address}'.
-4. Identify safe, walkable routes with interesting stops in a given area.
-5. Recommend local places of interest near a travel destination.
+1. List tourist attractions and amenities within walking distance of {address}.
+1. Find event venues or spaces near {address} suitable for planning gatherings or meetings.
+1. Show family-friendly places like parks, cafés, or playgrounds near {address}.
 
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@@ -126,7 +124,7 @@ Extracts summaries and linked metadata from Wikipedia.
 1. Summarize '{query}' with article title, overview, URL, and categories.
 2. Return a Spanish-language summary of '{query}' with English metadata.
 3. Provide JSON output with structured Wikipedia data for '{query}'.
-4. List categories and overview of a scientific term from Wikipedia.
+4. List categories and overview of a scientific term '{term}' from Wikipedia.
 5. Compare summaries of related concepts like ‘machine learning’ and ‘AI’.
 
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@@ -141,10 +139,9 @@ Generates geospatial accessibility scores for a city.
 
 **Sample Prompts**:
 1. Generate accessibility heatmap for 'Barcelona' to identify underserved areas.
-2. Compare accessibility in central vs. peripheral neighborhoods in 'Mumbai'.
+2. Compare accessibility in central vs. peripheral neighborhoods in 'Bangalore'.
 3. Extract top 10 underserved zones in 'Toronto' for city development.
-4. Identify locations in London needing new healthcare facilities.
-5. Recommend new supermarket locations in Vienna based on amenity gaps.
+4. Recommend new supermarket locations in Vienna based on amenity gaps.
 
 ## How to wire them up?
 🎥 [Watch the walkthrough video](media/AI_Pipeline.mp4) – fast overview of connecting LLMs to MCP tools via gRPC and building pipelines.