IBGE Nomes MCP for AI. Track name history across Brazilian decades
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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.
What your AI can do
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).
Determine how many people were born with a specific name in any given decade since 1930.
Isolate and compare the popularity of a name when filtered by specific Brazilian state codes (UF code).
Get lists of the most popular names in Brazil, filtering results by gender or time period.
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IBGE Nomes — Nomes do Brasil (3 Tools)
Use these three tools to analyze Brazilian naming trends, retrieve historical data on name popularity by region, decade, or national ranking.
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Start using IBGE Nomes — Nomes do Brasil on VinkiusGet 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...
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 3 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually gathering name statistics is a nightmare of spreadsheets and academic articles.
If you're building a project on Brazilian culture or demographics, your current process involves pulling data from disparate sources: one sheet for national rankings, another for regional comparisons, and yet more to track decade-by-decade trends. You spend hours copy-pasting names into spreadsheets and manually cross-referencing dates.
With this MCP connected via Vinkius, you simply ask your agent a complex question—'Show me how 'João' changed in Bahia vs São Paulo since 1950.' The system handles the multi-step data retrieval using `get_nome_por_localidade` and delivers the clean comparison instantly.
Get precise name history with get_nome_frequencia.
Before this, tracking a single name's popularity across nearly 100 years meant piecing together data that didn't always exist in one place. You were limited by what the specific academic source contained.
Now, you can pinpoint exactly how many people were born with 'Maria' in the 1930s versus the 2010s using `get_nome_frequencia`. The data is structured and ready to use.
What your AI can actually do with this
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.
019d75b6-e0b2-71f8-af0f-49f815767883 Here's how it actually works
The bottom line is that you get precise historical demographic data without writing a complex database query.
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.
Your agent calls the relevant function in this MCP, passing required parameters like the state code or the gender filter.
The system returns structured data showing birth counts for the specified name(s) within those defined time periods.
Who is this actually for?
This connector is essential for researchers, content creators, and developers who need deep cultural or demographic context about Brazilian society. It’s for the genealogist stuck comparing family records across decades, or the data journalist needing to prove a trend.
Designs articles or campaigns that reference popular cultural topics, using historical name trends to ground their narrative.
Runs statistical reports comparing demographic data across regions, needing specific location-based name counts.
Investigates family history by tracking the relative popularity of names over multiple generations and decades.
What Changes When You Connect
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.
See it in action
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.
The honest tradeoffs
Treating all data as current.
Assuming that because 'Pedro' is popular now, it was also the most common name 80 years ago. This misses critical historical context.
To check past popularity, you must use get_nome_frequencia or run a targeted query through get_ranking_nomes specifying the correct decade.
Forgetting location matters.
Assuming that name trends are uniform across all of Brazil. A name popular in São Paulo might be rare in Bahia.
Always check regional variation by running a query using get_nome_por_localidade and inputting the specific IBGE UF codes for comparison.
Using one tool for everything.
Trying to get both historical trends and state-level comparisons with just get_ranking_nomes. That function doesn't support location filtering.
You need a two-step process: first, use get_nome_por_localidade for the region; then, if you need to track its history, run that result through get_nome_frequencia.
When It Fits, When It Doesn't
Use this MCP if your core problem is understanding historical demographic shifts or regional differences in naming conventions. If you only care about the top names right now and don't need to filter by gender, simply use get_ranking_nomes. However, don't rely on this for current data; it's a historical dataset. Never try to find a single endpoint that does everything, because name trends require specialized queries: location needs get_nome_por_localidade, pure history needs get_nome_frequencia, and general rankings use get_ranking_nomes. Use the right tool for the specific context you are analyzing.
Questions you might have
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.
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