# IBGE Nomes MCP > IBGE Nomes MCP gives you access to Brazil's full name history dataset. You can track how popular any given name was across different decades since 1930, compare name trends between specific Brazilian states, or retrieve national popularity rankings filtered by sex and time period. ## Overview - **Category:** data-analytics - **Price:** Free - **Tags:** demographics, name-trends, historical-data, social-statistics, birth-frequency ## Description Need context on naming conventions? This connector taps into the massive IBGE Names API, a dataset that tracks birth names in Brazil over nearly a century. It’s perfect for anyone building content, research tools, or genealogy platforms focused on Latin America. Instead of sifting through academic papers or outdated records, you can ask specific questions about name popularity and get structured data back. You'll track how the frequency of 'Maria,' for example, rose and fell over 60 years, see if a particular name is more common in Bahia than in São Paulo, or find the top names for any decade. Connect this MCP through Vinkius to bring deep demographic insight into your application, letting your agent handle the complex data queries you'd otherwise write hundreds of lines of code for. ## Tools ### get_nome_frequencia Retrieves the count of births for one or more names across different decades in Brazil. ### get_ranking_nomes Lists the most popular names in Brazil for a specific time frame or gender. ### get_nome_por_localidade Calculates name frequency, filtering results specifically by a Brazilian state's two-digit code (UF). ## Prompt Examples **Prompt:** ``` How popular was the name 'Maria' across decades in Brazil? ``` **Response:** ``` 👩 **Maria — Frequency by Decade** 1930s: 336,477 births 1950s: 1,487,301 births (peak! 🔝) 1970s: 1,225,640 births 1990s: 562,893 births 2010s: 145,301 births Maria was Brazil's #1 name for 60 years but has declined 90% since its 1950s peak. ``` **Prompt:** ``` What are the top 10 baby names in Brazil in the 2000s? ``` **Response:** ``` 🏆 **Top 10 Names — 2000s Decade** 1. Maria (562K) | 2. Ana (345K) 3. João (289K) | 4. Pedro (267K) 5. Lucas (254K) | 6. Gabriel (248K) 7. Mateus (210K) | 8. Guilherme (195K) 9. Rafael (188K) | 10. Mariana (172K) Biblical names dominate, with Lucas and Gabriel surging. ``` **Prompt:** ``` Is 'João' more popular in Bahia or in Rio Grande do Sul? ``` **Response:** ``` 📊 **João — Regional Comparison** 🏖️ Bahia: 892K total births (since 1930) 🍷 Rio Grande do Sul: 456K total births João is **nearly 2× more popular** in Bahia than in Rio Grande do Sul, reflecting the Northeast's stronger tradition of Biblical naming. ``` ## Capabilities ### Track name frequency by decade Determine how many people were born with a specific name in any given decade since 1930. ### Compare names across Brazilian states Isolate and compare the popularity of a name when filtered by specific Brazilian state codes (UF code). ### Retrieve national name rankings Get lists of the most popular names in Brazil, filtering results by gender or time period. ## Use Cases ### Researching cultural naming shifts A content strategist wants to write about changing tastes. They ask their agent: 'What were the top 10 names in the 2000s, and how did those names compare in popularity to the 1970s?' The agent runs `get_ranking_nomes` twice, then uses `get_nome_frequencia` on the difference to build a full article outline. ### Analyzing regional demographic differences A market researcher needs to understand naming patterns. They ask: 'How popular is 'João' in Rio Grande do Sul versus Bahia?' The agent calls `get_nome_por_localidade` with both state codes, delivering a clear comparative report. ### Building a genealogy tool A developer needs to show the lineage of names. They ask for the frequency of 'Maria' over the last 50 years. The agent uses `get_nome_frequencia` to provide a precise, decade-by-decade timeline, which they plug directly into their app. ## Benefits - You get full historical context. Instead of just knowing a name is popular now, you can use `get_nome_frequencia` to see if it was once the top choice in the 1950s. - Compare regions instantly. If you need to know if 'José' is more common in Bahia or São Paulo, running `get_nome_por_localidade` gives you a direct comparison for state-level insights. - Quickly identify trends. Use `get_ranking_nomes` to generate lists of the top 20 names by decade and sex, perfect for articles or marketing content. - Automate data collection. Your agent handles the multiple API calls needed—for instance, running a ranking check, then using that list to run frequency checks on key names. - Avoid manual research. You don't have to cross-reference academic papers; you just ask your AI client for the name data you need. ## How It Works The bottom line is that you get precise historical demographic data without writing a complex database query. 1. Start by telling your agent which name(s) and what context you need. For example, you might specify a list of names and the decades to check. 2. Your agent calls the relevant function in this MCP, passing required parameters like the state code or the gender filter. 3. The system returns structured data showing birth counts for the specified name(s) within those defined time periods. ## Frequently Asked Questions **How do I check a name's popularity across multiple decades in Brazil? (Using get_nome_frequencia)** Use `get_nome_frequencia` and provide the names you want to track. The tool returns the exact birth count for each name within every specified decade since 1930. **Can I compare name popularity between specific Brazilian states? (Using get_nome_por_localidade)** Yes, you can use `get_nome_por_localidade`. You must provide the IBGE UF code for each state you want to compare and list the names you are interested in. **What is the best way to find out the most popular baby names? (Using get_ranking_nomes)** Use `get_ranking_nomes`. You can filter this tool by a specific decade and by gender (M/F) to generate an accurate, ranked list of the top contenders. **When using get_nome_frequencia, how must I correctly format the input if I want to check three different names?** You must separate each name with a pipe symbol (|). For example, entering "Ana|João|Pedro" tells the system to analyze all three names across decades. This method allows for simultaneous comparison of multiple titles. **If I use an invalid or unrecognized IBGE UF code in get_nome_por_localidade, what error message should I expect?** The MCP will return a specific data validation error. This error confirms that the provided state code is not part of the recognized Brazilian database codes. Always verify your input against the official IBGE list. **For get_ranking_nomes, what are the historical boundaries for name popularity rankings?** The dataset includes data spanning from the 1930s through the most recent available decade. When using this tool, you must specify a valid decade and optionally select Male or Female (M/F) to narrow the results. **Are there rate limits when calling get_nome_por_localidade repeatedly for different Brazilian states?** The system supports high volume, but repeated calls exceeding 10 requests per minute might trigger temporary throttling. If you encounter a limit error, simply wait one minute and try your queries again. **What should I do if the name provided to get_nome_frequencia is misspelled or too obscure?** The tool will return a clear 'No Data Found' message for that specific input. You must check the spelling against known Brazilian spellings before running the query. **Where does this name data come from?** All name data comes from **official IBGE Census records** spanning over 90 years. The frequency counts represent actual birth registrations aggregated by decade since the 1930s.