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submit_query
Submit Query
What it answers
Submit a heavy/long-running query to run OFF the request path and get a job_id back immediately, instead of blocking (and risking a timeout) on a statewide/cross-hospital scan. Arguments are validated against the target tool's own input model AT ACCEPT TIME, so a malformed submit is refused immediately (reason=invalid_arguments) instead of after you waited for the job. A POINT-LOOKUP CALL IS REFUSED, not queued: the refusal carries the direct call to make instead, with your own arguments already in it. That includes a bounded compare_hospital_rates read (one billing_code + one state), which the precomputed window answers in about a second. Pass force=true only when the SYNC door sent you here (too_large_for_sync, tool_timeout) — that route is always accepted. Poll with poll_query(job_id) and read the result with fetch_query(job_id). A refusal names the async-eligible targets and their current serving state, so you always know what this lane can run today.
Inputs
toolargumentsforceCall it
From an agent: connect the Vlada MCP once (one click for Claude, ChatGPT, Cursor, VS Code) and ask in plain English; the agent selects submit_query when the question fits. From code: the same tool over REST with an API key. The schema endpoint needs no key.
curl -X POST https://api.vladahealth.com/v1/tools/submit_query \
-H "Authorization: Bearer $VLADA_API_KEY" \
-H "Content-Type: application/json" \
-d '{"tool":"<tool>"}'curl https://api.vladahealth.com/v1/tools/submit_query/schema # the JSON schema, no auth # MCP endpoint (Streamable HTTP): https://mcp.vladahealth.com/mcp
What comes back
Typed rows plus provenance on every answer: the public source file, its vintage, the methodology, and a response_hash you can replay. Prove and replay tools turn any number into a re-runnable receipt. A number the data cannot support comes back as “not in the data”, never as zero.
Output schema
{
"$defs": {
"Quality": {
"description": "Row-level quality signal. All flags precomputed at build time\n(stored on the gold mart) and passed through here.",
"properties": {
"is_outlier": {
"default": false,
"title": "Is Outlier",
"type": "boolean"
},
"is_ghost_candidate": {
"default": false,
"title": "Is Ghost Candidate",
"type": "boolean"
},
"n_similar_rates": {
"default": 0,
"title": "N Similar Rates",
"type": "integer"
},
"confidence": {
"default": 1,
"title": "Confidence",
"type": "number"
}
},
"title": "Quality",
"type": "object"
},
"Source": {
"description": "Cell-level provenance. Every served value traces back here.\n\n`snapshot_id` is the Iceberg snapshot the tool read from. Two calls\nat the same snapshot MUST produce byte-identical responses —\nthat's the content-addressed caching guarantee.",
"properties": {
"table": {
"title": "Table",
"type": "string"
},
"rate_id": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Rate Id"
},
"source_files": {
"items": {
"type": "string"
},
"title": "Source Files",
"type": "array"
},
"snapshot_id": {
"anyOf": [
{
"type": "integer"
},
{
"type": "null"
}
],
"default": null,
"title": "Snapshot Id"
}
},
"required": [
"table"
],
"title": "Source",
"type": "object"
}
},
"properties": {
"value": {
"default": null,
"title": "Value"
},
"unit": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Unit"
},
"vintage": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Vintage"
},
"source": {
"$ref": "#/$defs/Source"
},
"methodology": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Methodology"
},
"quality": {
"$ref": "#/$defs/Quality"
},
"invariants_applied": {
"items": {
"type": "string"
},
"title": "Invariants Applied",
"type": "array"
},
"caveats": {
"items": {
"type": "string"
},
"title": "Caveats",
"type": "array"
},
"response_hash": {
"default": "",
"title": "Response Hash",
"type": "string"
},
"semantic_version": {
"default": 1,
"title": "Semantic Version",
"type": "integer"
},
"tool_version": {
"default": "unknown",
"title": "Tool Version",
"type": "string"
},
"explanation": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "Explanation"
},
"status": {
"default": "complete",
"title": "Status",
"type": "string"
},
"refusal": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Refusal"
},
"failure": {
"anyOf": [
{
"additionalProperties": true,
"type": "object"
},
{
"type": "null"
}
],
"default": null,
"title": "Failure"
},
"as_of": {
"anyOf": [
{
"type": "string"
},
{
"type": "null"
}
],
"default": null,
"title": "As Of"
},
"freshness_state": {
"default": "unknown",
"title": "Freshness State",
"type": "string"
}
},
"title": "AsyncJobResponse",
"type": "object"
}Sources behind it
Known limits
No gaps recorded for this tool. Absence of a recorded gap is not a claim of complete coverage; the answer itself says what it covers.
As of the 2026-09-19 build of the served surface · machine-readable catalog · the live server may run a different version; the schema endpoint above is authoritative for what is deployed.