Overview
Meteorites works by retrieving meteorite data from various reliable sources and providing it in a structured format. You can expect accurate and up-to-date information.
Live Test Meteorite Grounding Data for AI Agents Source →
The tool
Once your client is connected to the VerveContext server, this appears in its tool list as MeteoriteGroundingDataforAIAgents. It is read-only and open-world — it fetches and never mutates anything on your side — so most clients call it without asking you to confirm.
{
"name": "MeteoriteGroundingDataforAIAgents",
"arguments": {
"name": "Allende"
}
}You do not name the tool yourself; the model picks it. Asking about Allende in the terms this source covers is enough for it to reach for MeteoriteGroundingDataforAIAgents on its own — naming it explicitly also works, and is the way to force the call.
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, 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. Premium arguments are accepted on every plan but only take effect on plans that include them.
| Argument | Type | Description |
|---|---|---|
nameRequired | string | The name of the meteorite you want to search for |
massOptional | number | Minimum mass of the meteorite in grams |
yearOptionalPremium | number | The year the meteorite fell to Earth |
What the model gets back
The result carries a structuredContent object matching the tool's declared outputSchema, so a client reads fields without parsing prose. status is "ok" and error is null on success; a null field means the value was not available for that input, not that the call failed.
{
"status": "ok",
"error": null,
"data": {
"count": 1,
"filteredOn": [
"name"
],
"meteors": [
{
"name": "Allende",
"recclass": "CV3",
"mass": "2000000",
"year": "1969",
"geolocation": {
"type": "Point",
"coordinates": [
-105.31667,
26.96667
]
}
}
]
}
}
Response fields
Paths are relative to data. Premium fields are absent rather than zeroed on plans that do not include them, so check for presence instead of comparing to 0.
| Field | Type | Example | Description |
|---|---|---|---|
count | number | 1 | Number of meteorites returned in meteors (capped at 30) |
filteredOn | array | ["name"] | Array of field names used to filter the meteorite results |
meteors | array[1] | Array of meteorite objects matching the search query | |
meteors.0.name | string | Allende | Official name of the meteorite specimen |
meteors.0.recclass | string | CV3 | Meteorite classification code (composition and type) |
meteors.0.mass | string | 2000000 | Mass of the meteorite in grams as string |
meteors.0.year | string | 1969 | Year the meteorite fell to Earth as string |
meteors.0.geolocationPremium | object | {…} | GeoJSON object with landing site coordinates |
meteors.0.geolocation.typePremium | string | Point | |
meteors.0.geolocation.coordinatesPremium | array | [-105.31667,26.96667] |
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 in 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 count: 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 carrying a sentence the model can act on, not a bare status code. Error handling covers the full list.
| Status | What it means |
|---|---|
400 / 422 | The arguments did not validate. The message names the offending one. |
401 | The OAuth session is invalid or expired — reconnect the server. |
403 | Blocked by a key restriction or an IP allow-list. Never a bad identity. |
404 | This source is not part of VerveContext. Check the catalog. |
429 | Out of credits, or a brief rate limit. The message tells them apart. |
A call costs 2 credits each time the tool actually runs; a model that reasons about the tool without calling it costs nothing.
Use cases
- Planetarium Exhibit Displays
- Museum curators query recorded falls by specimen name to present visitors with the classification, weight, and discovery year of famous space rocks.
- Science Coursework Modules
- When designing astronomy curricula, educators filter cataloged specimens by mass to illustrate how meteor size distributions vary across recorded recovery events.
- Geology Sample Cataloging
- To catalog collected specimens, geology labs match field finds against recorded meteorite classifications and known mass thresholds.
- Astronomy Reference Portals
- Stargazing databases look up historical strike records by name to provide readers with the confirmed weight, type, and fall date of recovered specimens.
Other ways to use Meteorite Grounding Data for AI Agents
Set up Meteorite 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.
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