City Grounding Data for AI Agents
The tool
Ground agents in verifiable city data — population, country and coordinates across 150K+ places — so location answers rest on real records, not guesses.
Once your client is connected to the VerveContext MCP server, this appears in its tool list as CityGroundingDataforAIAgents. It is read-only and open-world: it fetches, it never mutates anything on your side, so most clients will call it without asking you to confirm.
{
"name": "CityGroundingDataforAIAgents",
"arguments": {
"city": "San Francisco"
}
}What that looks like in a conversation
You don't name the tool — the model picks it. Asking about San Francisco in the terms this source covers is enough for it to reach for CityGroundingDataforAIAgents on its own.
Connecting
One server URL covers every source in the catalog, including this one. Authorization is OAuth — the client opens a browser once and there is no key to paste into a config file.
{
"mcpServers": {
"vervecontext": {
"url": "https://api.vervecontext.com/v1/mcp"
}
}
}https://api.vervecontext.com/v1/mcpPer-client setup — Claude Desktop, Cursor, VS Code, ChatGPT — is on the MCP setup page.
Arguments
These are the properties on the tool's inputSchema, so a well-behaved client validates them before the call is made.
| Argument | Type | Example | Description |
|---|---|---|---|
cityRequired | string | San Francisco | The city name for which you want to get the data (e.g., New York) |
limitPremium | integer | 5 | Limit number of cities that match your search criteria |
What the model gets back
The result carries a structuredContent object matching the tool's declared outputSchema, so a client can read fields without parsing prose. status is "ok" on success and error is null; a null field means the value wasn't available for that input, not that the call failed.
{
"status": "ok",
"error": null,
"data": {
"search": "San Francisco",
"foundCities": [
{
"name": "San Francisco",
"altName": "",
"country": "US",
"countryName": "United States",
"featureCode": "PPLA2",
"population": 874961,
"populationCategory": "major",
"loc": {
"type": "Point",
"coordinates": [
-122.4194,
37.7749
]
}
}
]
}
}Fields
| Field | Type | Example | Description |
|---|---|---|---|
search | string | The search query used to find cities | |
foundCities | array[1] | Array of city objects matching the search criteria | |
└ foundCities.0.name | string | Official city name | |
└ foundCities.0.altName | string | Alternative city name or alias if available | |
└ foundCities.0.country | string | Two-letter ISO country code (e.g., US) | |
└ foundCities.0.countryName | string | Full country name (e.g., United States) | |
└ foundCities.0.featureCode | string | Geonames feature code for city classification type | |
└ foundCities.0.population | number | City population count derived from latest census | |
└ foundCities.0.populationCategory | string | City size classification based on population ranges | |
└ foundCities.0.loc | object | ||
└ foundCities.0.loc.type | string | GeoJSON geometry type (always Point) | |
└ foundCities.0.loc.coordinatesPremium | array | GeoJSON coordinates as [longitude, latitude] pair |
Why ground on it
A model can produce something that looks like this answer from its training data, and be confidently out of date or simply wrong. This source returns the current value with a shape you can check, which is the difference between an answer you can cite and one you have to hedge.
Point an evaluation at search: it is the field most worth pinning a claim to, and it is either present and current or absent — never plausibly invented.
Failure modes
Errors come back as tool errors with a sentence the model can act on, not a bare status code.
| Status | What it means |
|---|---|
400 / 422 | The arguments didn't validate. The message names the offending one. |
401 | The OAuth session is invalid or expired — reconnect the server. |
403 | Out of credits. Not a bad key — the month's allowance is spent. |
404 | This source isn't part of VerveContext. Check the catalog. |
429 | Brief rate limit. Retrying after a moment succeeds. |
Other ways to use City Grounding Data for AI Agents
Set up City Grounding Data for AI Agents on VerveContext, or reach the same source a different way. Your VerveContext account and credits work on all of them — one key, one balance.
Frequently asked questions
Do I have to tell the agent to use CityGroundingDataforAIAgents?
No. The tool's name and description are in the model's context once the server is connected, so it selects the tool when the question calls for it. Naming it explicitly works too and is useful when you want to force the call.
Does connecting the server expose every tool at once?
Yes — one connection lists the whole VerveContext catalog. Clients with a tool budget can usually filter the list; the credit cost is per call, so an unused tool costs nothing.
What does a call cost?
1 credit each time the tool actually runs. A model that reasons about the tool without calling it costs nothing.
Is there a REST version of this?
Yes — the same source is available as a plain HTTPS endpoint on APIVerve, linked above. Same data, same credits, same account.