Agify MCP. Predict age and demographic profiles from names
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Agify: Predicts the estimated age of individuals or groups based on their first name. Connect your AI agent to this server to enrich data by generating demographic profiles without needing birth dates.
You can scope predictions by country and process up to 10 names in a single batch request. This provides powerful identity intelligence for data analysis and user profiling.
What your AI agents can do
Predict age
Predicts the age of a single person based on their first name, optionally scoping the search to a specific country.
Predict age batch
Predicts the ages of multiple people in one go, accepting a list of names for efficient data processing.
Predicts the age of one person when you provide their first name (and optionally, a country code for better accuracy).
Predicts the ages for a list of names simultaneously in one request, handling up to 10 names at once.
Narrows the prediction scope using ISO country codes (e.g., 'IT' for Italy), improving the accuracy of the age estimate.
Outputs the count of data points used to generate the age prediction, allowing you to assess the confidence level.
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Supported MCP Clients
Gemini Waiting for input…
019e5cf8predict age
Predicts the age of a single person based on their first name, optionally scoping the search to a specific country.
019e5cf8predict age batch
Predicts the ages of multiple people in one go, accepting a list of names for efficient data processing.
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What you can do with this MCP connector
Agify MCP Server - Predict Age from Names
Your AI client can predict a person's age just from their first name. You'll get this server connected to your agent on Vinkius. It gives you demographic data to enrich your records without needing birthdates. You can narrow predictions down to a specific country, and it handles up to ten names in a single request.
predict_age predicts the age for one person using just their first name, and you can optionally pass in a country code to make the guess more accurate. predict_age_batch predicts ages for a list of people at once, letting you process multiple names efficiently.
When you run these tools, the output includes the estimated age, the country scope used for the prediction, and a data count, so you know exactly how many data points it used to make the call.
How Agify MCP Works
- 1 Subscribe to the Agify MCP Server and input your API key into your AI client.
- 2 Call the appropriate tool (
predict_agefor singles orpredict_age_batchfor groups), providing the name(s) and optionally the country code. - 3 The server processes the request and returns the estimated age(s), along with the supporting data count.
The bottom line is, you send names, and the server sends back demographic estimates.
Who Is Agify MCP For?
Data analysts and marketing teams need this. If you're building a product that needs to segment users or enrich databases with demographic data, this is your tool. It solves the pain of needing age data when the user only provides a name.
Enriches large user databases with estimated age groups or demographic metrics using names as identifiers.
Segments lead lists based on name-derived age estimates to tailor ad copy or product recommendations.
Builds personalization engines or smarter user onboarding flows without requiring users to enter sensitive birth dates.
What Changes When You Connect
- Enrich user data instantly. Instead of just having a name, you get an estimated age and a data point count. This is critical for assessing data quality when using the
predict_agetool. - Process large lists quickly. Use
predict_age_batchto run age predictions on up to 10 names in a single request, saving time compared to calling the single prediction tool repeatedly. - Improve regional accuracy. By adding an ISO country code, you narrow the prediction scope. This makes the age estimate much more reliable than a general search, especially for common names.
- Build smarter flows. Integrate this tool into onboarding or personalization engines. You can guide users through a flow that requires a name but not a birth date.
- Segment leads effectively. Marketing teams use this to segment leads. You can categorize an audience by name-derived age estimates before sending out a campaign.
Real-World Use Cases
Profiling a list of new signups
A data analyst receives a list of 50 new user names. Instead of manually checking each one, they ask their agent to run predict_age_batch on the entire list. The agent processes all 50 names, returning a structured JSON output with estimated ages for every entry.
Targeting a specific market
A marketing manager is running a campaign in Japan. They feed the names of their leads into the agent and use the predict_age tool, specifying 'JP' as the country scope. This ensures the age prediction is accurate for Japanese demographics, not a global average.
Checking a single lead's profile
A backend developer needs to check a single lead's age for a profile card. They use the predict_age tool with just the name. The agent returns the estimated age and the supporting data count, which they can use to display a confidence score on the UI.
Validating data sources
An ML engineer needs to know if a name field is reliable. They use the tool and examine the output's data count metric. If the count is low, they know the prediction is weak, which helps them validate their data source quality.
The Tradeoffs
Over-relying on default predictions
Just running predict_age('Michael') without specifying a country. The result might be a global average that doesn't match the user's actual location or culture.
→
Always specify the country using the predict_age tool. For example, predict_age('Michael', country='US') provides a much tighter and more accurate age range than a generic call.
Using single calls for big jobs
Calling predict_age 10 times in a loop to process 10 names. This is slow and inefficient, and it generates unnecessary API calls and latency.
→
Use the predict_age_batch tool instead. It accepts multiple names in one request, making the process faster and more resource-efficient.
Ignoring data confidence
Accepting the first age number returned by the API without checking the supporting data count. This leads to trusting potentially inaccurate data points.
→ Always check the data metrics provided by the server. If the data count is low, use the prediction with caution or ask the user for more explicit information.
When It Fits, When It Doesn't
Use this server if your primary need is to enrich records with estimated demographic data based only on a person's name. You need to know the age without asking for a birth date. If your workflow requires complex cross-referencing with other external APIs (like IP geolocation or purchase history), this tool is only one piece of the puzzle. Don't use it if you need factual, verified age data—this is a prediction. If you need to predict a different demographic trait (e.g., income bracket or job title) from a name, you need a different service. If you only need to check if a name is valid, use a simple validation endpoint instead.
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Agify. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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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 server provides 2 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Available Capabilities
Manually figuring out a user's age from their name is a pain.
Today, if you get a list of names and need to know the age distribution for a segment, you're stuck in spreadsheets. You're manually looking up names, guessing demographics, or running slow, fragmented API calls just to get a general idea. It's tedious, and the data is never consistent.
With the Agify MCP Server, you just pass the names to your agent. It runs the prediction in one go, giving you estimated age ranges and the data sources it used. You get clean, structured data that you can use immediately.
Agify MCP Server: Predict Age from Name
You eliminate the need to run separate scripts for single names and separate jobs for batches. You just call one tool, providing the list or the name, and the agent handles the rest.
The difference is clean, predictable data that lets you segment users and build smart personalization flows without ever asking for a birth date.
Common Questions About Agify MCP
How accurate is the Agify MCP Server for age prediction? +
The prediction accuracy depends on the underlying data count. The server returns a data point count, so you can check the source metrics to judge how reliable the estimate is. The higher the count, the better the confidence.
Can I use Agify MCP Server to predict age for names in different countries? +
Yes. You can use the predict_age tool and pass an ISO country code (like 'IT' or 'JP') to scope the prediction to that region, making the age estimate much more accurate.
What's the difference between `predict_age` and `predict_age_batch`? +
predict_age predicts one age and supports optional country scoping. predict_age_batch predicts multiple ages at once, which is faster for processing large lists of names.
Does the Agify MCP Server require a specific API key? +
Yes, you'll need to subscribe to the server and provide an API key, though some limited testing might be available initially. Check the server documentation for current key requirements.
How does the `predict_age` tool handle missing country data? +
The predict_age tool defaults to a global model if you don't specify a country. However, providing an ISO code significantly improves the prediction's accuracy. You should always include the country code if you know it.
What format should I use for the input names in `predict_age_batch`? +
You should provide the names as a list or comma-separated string. The tool accepts up to 10 names per request. If you have more than 10, you'll need to break them into multiple calls.
Can I use the Agify MCP Server for non-name data predictions? +
No, the Agify MCP Server is strictly for name-derived demographic estimates. It requires a first name as input. You can't ask it to predict age from a birth date or location alone.
Are there any limits or rate limits when calling `predict_age`? +
The specific rate limits are determined by the underlying Agify.io API. For high-volume data enrichment, check the service's documentation. Otherwise, keep your requests spaced out.
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.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.
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