get_concept
Pack: openalex · Endpoint: https://gateway.pipeworx.io/openalex/mcp
No MCP client? Call it directly: GET https://gateway.pipeworx.io/v1/tools/get_concept for the schema, then POST the same URL with its arguments for the data.
Look up research fields or topics by name. Returns concept description, publication count, related concepts, and parent concepts in the academic hierarchy.
Parameters
| Name | Type | Required | Description |
|---|---|---|---|
query | string | yes | Concept name to look up (e.g., “deep learning”) |
_apiKey | string | no | OpenAlex API key. Optional — Pipeworx supplies one; pass your own to bill your account instead. |
Example call
Arguments
{
"query": "deep learning"
}
curl
curl -X POST https://gateway.pipeworx.io/openalex/mcp \
-H 'Content-Type: application/json' \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/call","params":{"name":"get_concept","arguments":{"query":"deep learning"}}}'
TypeScript (@pipeworx/sdk)
import { Pipeworx } from '@pipeworx/sdk';
const pipeworx = new Pipeworx();
const result = await pipeworx.call('get_concept', {
"query": "deep learning"
});
Response shape
Always returns: query, found
| Field | Type | Description |
|---|---|---|
query | string | The concept query searched |
found | boolean | Whether a matching concept was found |
id | string | OpenAlex concept ID |
display_name | string | Concept display name |
level | number | Concept hierarchy level |
description | string | null | Concept description |
works_count | number | Number of works in this field |
cited_by_count | number | Total citations in this field |
ancestors | array | Parent concepts in hierarchy |
related_concepts | array | Related concepts (up to 10) |
Full JSON Schema
{
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The concept query searched"
},
"found": {
"type": "boolean",
"description": "Whether a matching concept was found"
},
"id": {
"type": "string",
"description": "OpenAlex concept ID"
},
"display_name": {
"type": "string",
"description": "Concept display name"
},
"level": {
"type": "number",
"description": "Concept hierarchy level"
},
"description": {
"type": [
"string",
"null"
],
"description": "Concept description"
},
"works_count": {
"type": "number",
"description": "Number of works in this field"
},
"cited_by_count": {
"type": "number",
"description": "Total citations in this field"
},
"ancestors": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Ancestor concept name"
},
"level": {
"type": "number",
"description": "Ancestor hierarchy level"
}
},
"required": [
"name",
"level"
]
},
"description": "Parent concepts in hierarchy"
},
"related_concepts": {
"type": "array",
"items": {
"type": "object",
"properties": {
"name": {
"type": "string",
"description": "Related concept name"
},
"level": {
"type": "number",
"description": "Related concept hierarchy level"
},
"score": {
"type": "number",
"description": "Relatedness score"
}
},
"required": [
"name",
"level",
"score"
]
},
"description": "Related concepts (up to 10)"
}
},
"required": [
"query",
"found"
],
"dependentRequired": {
"found": [
"id",
"display_name",
"level",
"description",
"works_count",
"cited_by_count",
"ancestors",
"related_concepts"
]
}
}
Connect
Add this to your MCP client config, or use one-click install buttons:
{
"mcpServers": {
"openalex": {
"url": "https://gateway.pipeworx.io/openalex/mcp"
}
}
}
See Getting Started for client-specific install steps.