# NLM MeSH Term Normalizer — Descriptor & Qualifier API (`quartz_apple_dnx/nlm-mesh-term-normalizer`) Actor

Normalize exact biomedical terms to canonical NLM MeSH descriptors with descriptor IDs, preferred and entry terms, allowable qualifiers, and related descriptors.

- **URL**: https://apify.com/quartz_apple_dnx/nlm-mesh-term-normalizer.md
- **Developed by:** [Piven Core](https://apify.com/quartz_apple_dnx) (community)
- **Categories:** Developer tools, Business
- **Stats:** 2 total users, 1 monthly users, 93.3% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

$0.25 / 1,000 resolved mesh descriptors

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#pay-per-event

## What's an Apify Actor?

An Actor is a serverless cloud program that runs on the Apify platform. It has two run modes.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.

Apify vocabulary and the platform model are defined once, in the agent quickstart at https://apify.com/agents.md.

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.

Do not guess an integration path. Every one of them is in the agent quickstart at https://apify.com/agents.md: the Apify MCP server, Agent Skills with the Apify CLI, the JavaScript and Python clients, the REST API, and the account-free path for an agent with no human to sign in. It also carries the rule on stating cost before the first paid run.

For examples already wired to this Actor's own input schema, see the [API](#api) section below.

Each client library has reference documentation the quickstart does not restate: [JavaScript/TypeScript](https://docs.apify.com/api/client/js/docs.md) (`npm install apify-client`) and [Python](https://docs.apify.com/api/client/python/docs.md) (`pip install apify-client`).

# README

## NLM MeSH Term Normalizer — Descriptor & Qualifier API

Normalize biomedical terms to canonical **Medical Subject Headings (MeSH)** descriptors using the official NLM MeSH RDF Lookup API.

Use it for **biomedical terminology normalization, literature-search preparation, indexing workflows, research-data cleanup, descriptor resolution, and automated MeSH pipelines**.

**Independent tool. Not affiliated with or endorsed by NLM, NIH, or HHS. This is terminology data, not medical advice.**

### Common use cases

- Normalize biomedical terms to canonical MeSH descriptors
- Resolve entry terms to preferred descriptor labels
- Prepare standardized terminology for literature-search workflows
- Collect descriptor IDs, terms, qualifiers, and related descriptors
- Feed normalized MeSH identifiers into downstream research systems

### What you get

Each successful row contains MeSH descriptor ID and URI, canonical/preferred label, matched submitted label, preferred and entry terms, allowable qualifiers when requested, see-also descriptors when requested, source URL, and capture timestamp.

### Try it

```json
{
  "terms": ["Infection Control", "Ofloxacin", "Public Health"]
}
```

### Source

The NLM MeSH RDF Lookup API is an official JSON GET service for finding MeSH descriptors and retrieving descriptor details, including terms, qualifiers, and related descriptors.

### Important limitations

Unmatched terms return a diagnostic row and are not billed.

This Actor normalizes terminology. It does not determine diagnoses, treatment relationships, medical relevance, evidence quality, or clinical meaning beyond the source terminology records.

### Pricing

Only successfully normalized descriptor rows are billed. Not-found diagnostics, zero-result summaries, and budget-limit summaries are free.

Candidate target: **$0.25 per 1,000 resolved descriptors**, subject to the live economics gate.

### About Piven Core

Built and maintained by **Piven Core** — focused data APIs and automation utilities for reliable, production-ready workflows.

Learn more: https://pivencore.com

# Actor input Schema

## `terms` (type: `array`):

1 to 100 exact MeSH descriptor or entry-term labels. Examples: Infection Control, Ofloxacin, Public Health.

## `includeQualifiers` (type: `boolean`):

Return the MeSH qualifiers allowed for each matched descriptor.

## `includeSeeAlso` (type: `boolean`):

Return related MeSH descriptors exposed by the official lookup details response.

## `maxConcurrency` (type: `integer`):

Bounded concurrency for the public NLM MeSH RDF service.

## `maxRequestRetries` (type: `integer`):

Retries transient NLM/network/server failures with bounded backoff.

## Actor input object example

```json
{
  "terms": [
    "Infection Control",
    "Ofloxacin",
    "Public Health"
  ],
  "includeQualifiers": true,
  "includeSeeAlso": true,
  "maxConcurrency": 2,
  "maxRequestRetries": 2
}
```

# Actor output Schema

## `dataset` (type: `string`):

No description

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "terms": [
        "Infection Control",
        "Ofloxacin",
        "Public Health"
    ]
};

// Run the Actor and wait for it to finish
const run = await client.actor("quartz_apple_dnx/nlm-mesh-term-normalizer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = { "terms": [
        "Infection Control",
        "Ofloxacin",
        "Public Health",
    ] }

# Run the Actor and wait for it to finish
run = client.actor("quartz_apple_dnx/nlm-mesh-term-normalizer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print(f"💾 Check your data here: https://console.apify.com/storage/datasets/{run.default_dataset_id}")
for item in client.dataset(run.default_dataset_id).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "terms": [
    "Infection Control",
    "Ofloxacin",
    "Public Health"
  ]
}' |
apify call quartz_apple_dnx/nlm-mesh-term-normalizer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,quartz_apple_dnx/nlm-mesh-term-normalizer"
        }
    }
}
```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/Sn5jjNPH2teLVMOiA/builds/RnN6rdSmJLHaNxjQf/openapi.json