# Global Poverty & Inequality Data ## Links - Product page URL: https://www.agentpmt.com/marketplace/global-poverty-inequality-data - Product markdown URL: https://www.agentpmt.com/marketplace/global-poverty-inequality-data?format=agent-md - Product JSON URL: https://www.agentpmt.com/marketplace/global-poverty-inequality-data?format=agent-json ## Overview - Product ID: 6980d4fd71cad8f61bf5b1a4 - Vendor: Apoth3osis - Type: data - Unit type: request - Price: 500 credits - Categories: Data Science, Financial Data, News & Media Monitoring, Academic & Scientific Research, Public Records & Government Data, Civic Data & Demographics - Generated at: 2026-04-15T18:29:33.052Z ### Page Description Explore how poverty and wealth are distributed across 200+ countries using data from the World Bank. Find out what share of a nation's population lives below the international poverty line, how income is split between the richest and poorest households, and whether conditions are improving or getting worse over time. Compare any country against its region or the world, track progress toward ending extreme poverty, and uncover the story behind the numbers with built-in trend analysis and contextual insights. ### Agent Description Query World Bank poverty and inequality data by country or region name. Returns poverty headcount at $2.15/$3.65/$6.85 thresholds, Gini index, income shares by quintile/decile, and extreme poverty metrics. Supports metric filtering (poverty_headcount, inequality, gini_index, income_distribution), time ranges (latest, last_5_years, YYYY:YYYY), trend analysis, and regional comparisons. Use action 'get_instructions' for full documentation. ## Details Tab ### Details Explore how poverty and wealth are distributed across 200+ countries using data from the World Bank. Find out what share of a nation's population lives below the international poverty line, how income is split between the richest and poorest households, and whether conditions are improving or getting worse over time. Compare any country against its region or the world, track progress toward ending extreme poverty, and uncover the story behind the numbers with built-in trend analysis and contextual insights. ### Actions - `query_poverty_data` (5 credits): Fetch poverty and inequality data for a country or region from the World Bank World Development Indicators database. ### Use Cases Development analysis, poverty research, inequality studies, SDG monitoring, policy research ### Workflows Using This Tool No public workflows currently reference this product. ### Related Content No related content is currently linked to this product. ## Advanced Tab ### DynamicMCP - Setup page URL: https://www.agentpmt.com/dynamic-mcp - Claude setup guide: https://www.agentpmt.com/dynamic-mcp?platform=claude#videos - ChatGPT setup guide: https://www.agentpmt.com/dynamic-mcp?platform=chatgpt#videos - Cursor setup guide: https://www.agentpmt.com/dynamic-mcp?platform=cursor#videos - Windsurf setup guide: https://www.agentpmt.com/dynamic-mcp?platform=windsurf#videos STDIO connector for Claude Code, Codex, Cursor, Zed, and other LLMs that require STDIO or custom connections. This lightweight connector routes requests to `https://api.agentpmt.com/mcp`. All tool execution happens in the cloud and the server cannot edit any files on your computer. ```bash npm install -g @agentpmt/mcp-router agentpmt-setup ``` ### REST API The live page renders cURL, Python, JavaScript, and Node.js examples. Logged-in users see those examples prefilled with their own API and budget credentials. - Purchase endpoint: https://api.agentpmt.com/products/purchase - Authorization format: `Bearer ` ```bash curl -X POST "https://api.agentpmt.com/products/purchase" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer eW91ci1hcGkta2V5LWhlcmU6eW91ci1idWRnZXQta2V5LWhlcmU=" \ -d '{ "product_id": "6980d4fd71cad8f61bf5b1a4", "parameters": { "action": "query_poverty_data", "country_or_region": "example_country_or_region", "metric_type": "all", "time_period": "latest", "include_regional_comparison": true, "include_trends": true } }' ``` ### Autonomous Agents Do not use the abbreviated instructions in this product markdown for wallet-based invocation. Retrieve the full External Agent API markdown document instead. - External Agent API page URL: https://www.agentpmt.com/external-agent-api - External Agent API markdown URL: https://www.agentpmt.com/external-agent-api?format=agent-md ### Schema #### Parameters - Schema type: actions ```json { "actions": { "query_poverty_data": { "description": "Fetch poverty and inequality data for a country or region from the World Bank World Development Indicators database.", "properties": { "country_or_region": { "type": "string", "description": "Country or region name in plain English. Supports 200+ countries (e.g., India, Kenya, Brazil), regional aggregations (e.g., Sub-Saharan Africa, South Asia, Latin America and Caribbean), income groups (e.g., Low Income, High Income, OECD), and global (World or Global). Common abbreviations like USA, UK, UAE are accepted.", "required": true }, "metric_type": { "type": "string", "description": "Type of poverty/inequality metric to query.", "enum": [ "poverty_headcount", "extreme_poverty", "gini_index", "gini", "inequality", "income_distribution", "all" ], "default": "all", "required": false }, "time_period": { "type": "string", "description": "Time period for data retrieval: 'latest' for most recent year, 'last_5_years', 'last_10_years', or a specific range like '2015:2020'.", "default": "latest", "required": false }, "include_regional_comparison": { "type": "boolean", "description": "Include regional and global averages (World, Sub-Saharan Africa, South Asia, Latin America & Caribbean, East Asia & Pacific, Middle East & North Africa) for context.", "default": true, "required": false }, "include_trends": { "type": "boolean", "description": "Include trend analysis showing direction, absolute change, percent change, and data point count when historical data is available.", "default": true, "required": false } } } }, "properties": { "action": { "type": "string", "description": "Action to perform: 'get_instructions' for documentation, 'query_poverty_data' to fetch poverty and inequality data", "required": false, "enum": [ "get_instructions", "query_poverty_data" ] }, "country_or_region": { "type": "string", "description": "Country or region name in plain language (e.g., 'India', 'Sub-Saharan Africa', 'Bangladesh', 'World'). Supports 200+ countries and major regional aggregations.", "required": false }, "metric_type": { "type": "string", "description": "Type of poverty/inequality metric to query:\n- poverty_headcount: Poverty rates at $2.15, $3.65, $6.85/day thresholds\n- extreme_poverty: $2.15/day poverty line with gap analysis\n- gini_index: Gini coefficient (0-100 inequality measure)\n- inequality: Gini coefficient plus income share distribution\n- income_distribution: Income shares by quintile and decile\n- all: All available metrics", "required": false, "default": "all", "enum": [ "poverty_headcount", "extreme_poverty", "gini_index", "gini", "inequality", "income_distribution", "all" ] }, "time_period": { "type": "string", "description": "Time period for data retrieval:\n- 'latest': Most recent available data point\n- 'last_5_years': Data from past 5 years\n- 'last_10_years': Data from past 10 years\n- 'YYYY:YYYY': Specific year range (e.g., '2015:2020', '2010:2022')", "required": false, "default": "latest" }, "include_regional_comparison": { "type": "boolean", "description": "Include regional and global comparison data for context (World, Sub-Saharan Africa, South Asia, etc.)", "required": false, "default": true }, "include_trends": { "type": "boolean", "description": "Include trend analysis showing changes over time if historical data is available", "required": false, "default": true } } } ``` ### Usage Instructions # Global Poverty & Inequality Data Access poverty rates, Gini coefficients, income distribution, and inequality metrics for 200+ countries from World Bank data. Query by country name in plain language with optional trend analysis and regional comparisons. ## Actions ### query_poverty_data Fetch poverty and inequality data for a country or region from the World Bank World Development Indicators database. **Required fields:** - `action` — `"query_poverty_data"` - `country_or_region` (string) — Country or region name in plain English. Supports 200+ countries (e.g., `"India"`, `"Kenya"`, `"Brazil"`), regional aggregations (e.g., `"Sub-Saharan Africa"`, `"South Asia"`, `"Latin America and Caribbean"`, `"East Asia and Pacific"`, `"Middle East"`), income groups (e.g., `"Low Income"`, `"Upper Middle Income"`, `"High Income"`, `"OECD"`), and global (`"World"` or `"Global"`). Common abbreviations like `"USA"`, `"UK"`, `"UAE"` are accepted. Names longer than 100 characters are rejected. **Optional fields:** - `metric_type` (string) — Type of metric to query. Default: `"all"`. - `"poverty_headcount"` — Poverty rates at $2.15, $3.65, and $6.85/day thresholds (2017 PPP) - `"extreme_poverty"` — $2.15/day poverty line plus poverty gap analysis - `"gini_index"` — Gini coefficient (0 = perfect equality, 100 = perfect inequality) - `"gini"` — Alias for `gini_index` - `"inequality"` — Gini coefficient plus income share held by lowest 20% and highest 20% - `"income_distribution"` — Income shares by lowest 10%, lowest 20%, highest 20%, and highest 10% - `"all"` — All available metrics including multidimensional poverty headcount - `time_period` (string) — Time period for data retrieval. Default: `"latest"`. - `"latest"` — Most recent complete year - `"last_5_years"` — Data from past 5 years - `"last_10_years"` — Data from past 10 years - `"YYYY:YYYY"` — Specific year range (e.g., `"2015:2020"`, `"2010:2022"`). A dash-separated range like `"2015-2020"` is also accepted and converted automatically. - `include_regional_comparison` (boolean) — Include regional and global averages (World, Sub-Saharan Africa, South Asia, Latin America & Caribbean, East Asia & Pacific, Middle East & North Africa) for context. Default: `true`. Only the first 3 indicators are compared per region. - `include_trends` (boolean) — Include trend analysis showing direction (improving/worsening/stable), absolute and percent change, and data point count when historical data is available. Default: `true`. For poverty and inequality indicators, a decrease is classified as "improving." **Example — Latest poverty data for a country:** ```json { "action": "query_poverty_data", "country_or_region": "India", "metric_type": "all", "time_period": "latest" } ``` **Example — Inequality trends over a decade:** ```json { "action": "query_poverty_data", "country_or_region": "Brazil", "metric_type": "inequality", "time_period": "2010:2022", "include_trends": true } ``` **Example — Regional poverty overview:** ```json { "action": "query_poverty_data", "country_or_region": "Sub-Saharan Africa", "metric_type": "poverty_headcount", "time_period": "latest", "include_regional_comparison": true } ``` **Example — Gini coefficient lookup:** ```json { "action": "query_poverty_data", "country_or_region": "South Africa", "metric_type": "gini_index", "time_period": "latest" } ``` **Example — Extreme poverty with no comparisons:** ```json { "action": "query_poverty_data", "country_or_region": "Nigeria", "metric_type": "extreme_poverty", "time_period": "last_5_years", "include_regional_comparison": false, "include_trends": true } ``` ## Response Format Responses include: - **data** — Per-indicator results with indicator name, latest value, year, unit (% of population, index 0-100, or % of total income), country name, and source attribution. - **trends** — When enabled, shows oldest/newest year and value, absolute change, percent change, direction (improving/worsening/stable), and data point count. - **regional_comparison** — When enabled, latest values for the same indicators across World, Sub-Saharan Africa, South Asia, Latin America & Caribbean, East Asia & Pacific, and Middle East & North Africa. - **insights** — Auto-generated observations such as poverty level severity, inequality classification, trend direction, and comparison to global averages. - **sdg_alignment** — SDG 1: No Poverty alignment note. - **data_notes** — Contextual notes about PPP, indicator interpretation, and data freshness. If a country name is not recognized, the response returns an error with a hint to use full English country names. ## Supported Metric Indicators | Metric Type | Indicators Included | |---|---| | poverty_headcount | Poverty at $2.15/day, $3.65/day, $6.85/day | | extreme_poverty | Poverty at $2.15/day, Poverty gap at $2.15/day | | gini_index / gini | Gini coefficient | | inequality | Gini coefficient, Income share lowest 20%, Income share highest 20% | | income_distribution | Income share lowest 10%, lowest 20%, highest 20%, highest 10% | | all | All of the above plus Multidimensional poverty headcount | ## Common Workflows 1. **Country poverty profile** — Call with `metric_type: "all"` and `time_period: "latest"` to get a complete snapshot of a country's poverty and inequality status. 2. **Track progress over time** — Use `time_period: "2010:2022"` with `include_trends: true` to see whether poverty is improving or worsening. 3. **Cross-country comparison** — Make separate calls for different countries with the same metric and time period, then compare the results. 4. **Regional benchmarking** — Set `include_regional_comparison: true` to see how a country compares to its region and the world. ## Important Notes - Data is sourced from the World Bank World Development Indicators database. - PPP values are in constant 2017 international dollars. - Most recent data is typically 1-3 years behind the current year due to collection and processing delays. - Data availability varies by country and indicator; some countries may have gaps. - Boolean fields must be actual booleans (`true`/`false`), not strings. - If an unrecognized `metric_type` is provided, the tool defaults to returning all metrics. - Aligns with SDG 1 (No Poverty) and SDG 10 (Reduced Inequalities) monitoring. ### About The Developer - Vendor name: Apoth3osis - Website: apoth3osis.io We build tools that enable AI agents to excel in the mathematical realm. Our small team develops experimental and unique solutions in the AI arena, with a strong focus on modular computing for agentic applications and custom model deployment. We have handled projects for a variety of applications across many sectors, from algorithmic trading and financial analysis, to molecular simulations and predictions, to habitat and biodiversity monitoring and wildlife conservation. ### Frequently Asked Questions No linked FAQs are currently available. ### Dependencies This product has no public dependency products.