Agify MCP for AI. Estimate age ranges just from a name.
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Agify gives you demographic intelligence by predicting a person's age based on their first name. You feed it names, and it returns an estimated age range along with statistical metrics about how confident it is in that number.
It handles both single lookups and batches of up to ten names, letting you enrich data without ever asking for a birth date.
What your AI can do
Predict age batch
Processes and predicts ages for multiple people using a list of first names up to ten entries long.
Predict age
Predicts a person's age from their first name, with an option to scope the prediction by country.
Send a first name and receive an estimated age for that individual.
Process up to ten names in one go, getting age estimates for the whole group at once.
Improve prediction accuracy by limiting the name analysis to a specific geographic region (ISO code).
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Agify: Two Tools for Demographic Analysis
These tools let you estimate age ranges by name, handling both single lookups and large batches of records.
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Start using Agify on VinkiusPredict Age Batch
Processes and predicts ages for multiple people using a list of first names up to ten entries long.
Predict Age
Predicts a person's age from their first name, with an option to scope the...
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Claude AI
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Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
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Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
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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 2 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Manually segmenting users by name is slow and guesswork prone.
Right now, if you get a list of names from an event or signup form, your team probably has to open multiple tabs. You copy the first name into Google, check for regional variations, then try to manually guess or approximate the age range and write it back into a spreadsheet. It's tedious, error-prone, and takes hours just on one list.
With Agify, you skip that whole process. Your agent handles the data enrichment automatically. You feed the names in, and instantly get structured results with estimated ages and confidence metrics. The output is clean; it’s ready for your database.
Agify allows you to predict age without `predict_age` or `predict_age_batch`.
The manual steps that disappear are the lookups, the cross-referencing with outdated internal sheets, and the guesswork. You don't have to maintain complex rule sets for regional name variations; the MCP handles the data mapping.
Now, your agent gives you clean, structured demographic profiles instantly. It changes how fast you can move from raw input data to actionable user intelligence.
What your AI can actually do with this
Dealing with user profiles often means collecting sensitive personal identifying information (PII). Instead, Agify lets your agent estimate key demographics using only the name and an optional country code. You send in a list of names, or just one name, and get back age predictions. The service doesn't guess; it uses large-scale demographic data to provide a statistical likelihood.
This capability is huge for anything from marketing segmentation to building smarter user onboarding flows. When you connect Agify through Vinkius, your AI client can access this intelligence alongside thousands of other tools in the catalog. You get rich data context right where you're working, instantly profiling users without compromising privacy.
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Here's how it actually works
The bottom line is, your AI client translates a simple name into actionable demographic data points automatically.
First, subscribe to this MCP and connect your preferred AI client through Vinkius.
Next, ask your agent to predict ages. You'll provide the name(s) and optionally include a country code for better accuracy.
Your agent calls the necessary tool, and you receive the estimated age and the statistical data count used for the prediction.
Who is this actually for?
This MCP is essential for anyone handling large datasets or building user-facing applications where collecting full birth dates isn't feasible. It targets roles that spend time segmenting, profiling, and enriching customer records with minimal input.
Enrich massive databases of client names by adding estimated age ranges for segmentation models.
Determine optimal onboarding flows or personalization features based on name-derived user profiles before collecting PII.
Segment lead lists for targeted ad campaigns by estimating the age group of contacts from a simple mailing list.
What Changes When You Connect
Build smarter personalization logic. You can use the predict_age tool to estimate user ages, allowing your application to tailor content even before sign-up.
Handle big data sets fast. The predict_age_batch tool lets you process up to ten names in a single call, making large-scale data enrichment efficient.
Maintain privacy compliance. You get demographic insights without needing to collect or store sensitive birth dates or PII.
Improve prediction accuracy. By adding country scope to the predict_age function, you narrow down possibilities and get more reliable age estimates.
Understand the confidence level. The results include data counts, so you always know how statistically sound the predicted age is.
See it in action
Auditing a stale lead list
A marketing manager has a spreadsheet of contacts from an old event. Instead of manually guessing or rejecting records, they ask their agent to run predict_age_batch on the entire list to see which age demographics are most represented.
Developing smart onboarding
A developer needs to build a new user flow. They use predict_age to check a potential user's name against their intended region, allowing the system to pre-select relevant features or content immediately upon login.
Market research profiling
A product team needs to test different messaging angles. They pipe names from an internal database into predict_age to quickly segment users into age groups and see which message resonates best with each demographic.
The honest tradeoffs
Guessing the scope
Calling predict_age without specifying a country when dealing with international names. The prediction might be inaccurate or generic.
Always use the optional country scoping feature on predict_age. This limits the pool of data, giving your agent a much more precise estimate.
Using single calls for lists
Running predict_age ten times in a loop to process 10 names. This is slow and inefficient.
Use the dedicated predict_age_batch tool instead. It's designed to handle multiple names in one request, saving time and resources.
Assuming exact age
Thinking the prediction is a guaranteed fact rather than a statistical estimate.
Always review the data count provided with the result. This metric tells you how many data points supported the prediction, so you know how confident the system is.
When It Fits, When It Doesn't
Use this MCP if your primary need is to infer demographic information (like age) from simple identifiers like names, especially when dealing with large datasets or international users. You should use it anytime PII collection is restricted but profiling data is needed.
Don't use this if you require exact dates of birth, highly specific demographics beyond age, or need to cross-reference multiple non-name fields (like job title + location). For those cases, look for a specialized identity verification tool in the Vinkius catalog.
Questions you might have
How does Agify predict age using `predict_age`? +
It predicts the estimated age by comparing the provided first name against a vast, statistical database of demographic records. This gives you an educated estimate, not a guaranteed fact.
Can I process more than 10 names with `predict_age_batch`? +
The current tool is limited to batches of up to ten first names in one request. If you have larger lists, you'll need to break the data into smaller chunks.
Does Agify require a country code for `predict_age`? +
No, it doesn't, but adding a country scope significantly improves accuracy. It tells the system which regional dataset to focus on.
What is the data count metric in Agify results? +
The data count shows how many records were used from the underlying database to make that specific age prediction. A higher number means a more reliable estimate.
Do I need an API key to use `predict_age`? +
No, you can start testing without one. However, for high-volume or production use of predict_age, you must register and provide your Agify API Key in the connection settings. This key authenticates your calls and ensures stable performance when running complex data enrichment pipelines.
What happens if I pass bad names to `predict_age`? +
If a name is unrecognizable, the tool returns an error specific to that input. The API doesn't fail entirely; it simply flags the problematic entries so your agent can skip them and process the rest of your list. Always validate inputs before calling predict_age.
Is there a rate limit I should know about when using `predict_age_batch`? +
While you can process up to 10 names per call, excessive requests in quick succession may trigger throttling. If your workflow sends too many batches too fast, the system will return a 429 error code. Implement exponential backoff logic in your client when dealing with large datasets.
How does `predict_age` handle missing country codes? +
If you don't scope by country, the tool uses global default data for prediction. While this works for general analysis, providing an ISO code dramatically increases accuracy and improves demographic specificity. Always try to include the country scope if your user base is regional.
Can I predict the age of multiple people at once? +
Yes! Use the predict_age_batch tool. You can provide an array of up to 10 names in a single request to get multiple age estimations efficiently.
How can I make the age prediction more accurate for a specific region? +
When using the predict_age tool, you can provide an optional country_id (ISO 3166-1 alpha-2 code). This scopes the data to that specific country, providing a more localized age estimate.
Is an API key required to use this server? +
An API key is optional for low-volume testing, but recommended for production use or higher rate limits. You can obtain one at Agify.io.
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