Provided by OpenAlex
Search the Research Topic Vocabulary
Search the OpenAlex controlled topic vocabulary by text over topic names and descriptions, and get back each topic with its id, description, the subfield, field and domain above it, keywords, total works count and total citation count. Use this to turn a research area in your own words into the topic id that the paper and researcher endpoints filter and group on.
Contract snapshot: 2026-09-29. Inspect in Glasser for current terms.
{
"type": "object",
"$schema": "http://json-schema.org/draft-07/schema#",
"required": [
"search"
],
"properties": {
"page": {
"type": "integer",
"maximum": 100,
"minimum": 1
},
"sort": {
"enum": [
"relevance_score:desc",
"works_count:desc",
"cited_by_count:desc"
],
"type": "string"
},
"cursor": {
"type": "string",
"maxLength": 500,
"minLength": 1
},
"search": {
"type": "string",
"maxLength": 200,
"minLength": 2
},
"per_page": {
"type": "integer",
"maximum": 100,
"minimum": 1
}
},
"additionalProperties": false
}{
"search": "machine learning",
"per_page": 10
}This Endpoint publishes no output schema. You get the Provider's own response body, unchanged — we do not warrant a shape we cannot guarantee.
How to run it
Two fields identify it: the Provider and the endpoint key. The key looks like a path because it usually mirrors the Provider's own, but it is an opaque identifier — not a URL you can open. The same provider:endpoint pair is what your Policy lists.
Once: paste this into your agent's chat. The Skill installs the CLI and logs in by itself. For an agent that cannot use a shell, connect it over MCP instead.
set up https://glasser.ai/SKILL.mdThen ask for this Endpoint:
Run the OpenAlex Endpoint /topics?search=true on Glasser with input {"search":"machine learning","per_page":10}glasser run -p openalex -e '/topics?search=true' -i '{"search":"machine learning","per_page":10}' --endpoint-version 1Pinned to version 1, the one this page shows: if a newer version is published first, the Run is refused instead of charged under new terms. Install and log in first on Get Started.
Once per Run:
IDEMPOTENCY_KEY=$(uuidgen)Run it, and copy this same block to send it again:
curl -X POST https://api.glasser.ai/v1/runs \
-H "Authorization: Bearer gl_..." \
-H "Idempotency-Key: $IDEMPOTENCY_KEY" \
-H "Content-Type: application/json" \
-d '{"provider":"openalex","endpoint":"/topics?search=true","input":{"search":"machine learning","per_page":10},"endpoint_version":1}'Reuse the same Idempotency-Key to send it again: the second send is a read of the first Run. A new key is a new Run and a new Charge. Pinned to version 1 for the same reason as the CLI.
What each outcome charges
| No result | The Provider answered: nothing matched. | $0.0011 |
|---|---|---|
| Provider error | The Provider failed to answer. | $0.00 |
| Timed out | The deadline won. | $0.00 |
| Internal failure | Our defect. | $0.00 |