openfda-database

---
name: openfda-database
description: 'Query, search, and download data from the openFDA API for drugs, devices,
  foods, tobacco, cosmetics, animal and veterinary products, substances, and transparency
  data. Use for FDA adverse events, recalls, labeling, approvals, shortages, 510(k)
  clearances, NDC lookups, and any FDA safety or regulatory data query across all
  28 API endpoints.

  '
metadata:
  category: data
  legacy_category: databases
  taxonomy_version: enzyme-agent/taxonomy/v1
  capability_ids:
  - data.scientific_database.retrieval
---

# openFDA Search and Query

## Prerequisites

1.  **Python**: Run scripts directly with `python`; do not require `uv` unless the script explicitly fails without it.
2.  **User Notification**: If LICENSE_NOTIFICATION.txt does not already exist in
    this skill directory then (1) prominently notify the user to check the terms
    at https://open.fda.gov/apis/ and https://open.fda.gov/license, then (2)
    create the file recording the notification text and timestamp.
3.  **`.env` file**: Make sure the `.env` file exists in your home directory.
    Create one if it does not exist.
4.  **`FDA_API_KEY`** (optional but recommended): Raises the daily request limit
    from 1,000 to 120,000. The skill works without it, but an agent can easily
    exhaust the keyless limit in a single session. The user can register for a
    free key at https://open.fda.gov/apis/authentication/. If the variable is
    missing from `.env`, do NOT ask the user to paste it into the chat (this
    would leak the key into the agent's context). Instead, give the user this
    command — **substituting `ENV_FILE` with the resolved literal path to the
    `.env` file**:

    ```bash
    printf "Enter openFDA API key (typing hidden): " && read -s key && echo && echo "FDA_API_KEY=$key" >> "ENV_FILE" && echo "Saved."
    ```

    The scripts load credentials automatically via `dotenv`. **NEVER** read,
    print, or inspect the `.env` file or its variables (e.g. no `cat`, `grep`,
    `echo`, `printenv`, or `os.environ.get` on keys). Credentials must stay
    out of the agent's context.

## Core Rules

-   **Use the Wrapper**: ALWAYS execute the provided helper scripts to query the
    database rather than accessing the database directly. The scripts
    automatically enforce the required rate limit gracefully.

-   **Rate Limiting**: Respect openFDA rate limits. Without API key: 240
    requests/min, 1,000 requests/day per IP. With API key: 240 requests/min,
    120,000 requests/day per key. Always set an API key before running
    multi-query workflows.

> **Warning**: An automated agent can easily exhaust the 1,000-request daily
> limit in a single research session. Always set an API key before running
> multi-query workflows.

> Instruct the user to register for a free key at
> https://open.fda.gov/apis/authentication/ and follow the prerequisite
> instructions above to add `FDA_API_KEY` to the `.env` file. The script will
> emit a warning to stderr if no API key is detected.

-   **Always Use `--output`**: All subcommands require `--output <file>` to
    write results to a file. This prevents large output becoming overwhelming.
    Use jq or code to read the output file.

-   **Notification**: If this skill is used, ensure this is mentioned in the
    output.

## Utility Script

**Single script for all operations:**

```bash
python scripts/openfda_query.py {search,count,download} --output <file> [options]
```

### 1. Search

Search any of the 28 endpoints and save JSON results to a file.

```bash
python scripts/openfda_query.py search \
  --category drug --endpoint event \
  --search "patient.drug.medicinalproduct:aspirin" \
  --limit 5 --output /tmp/fda_results.json
```

Stdout prints a compact summary:

```json
{"status": "success", "output": "/tmp/fda_results.json", "results_in_file": 5, "total_matching": 601477}
```

*Options:*

-   `--output`: Output file for full JSON results (required).
-   `--category`: API category — `drug`, `device`, `food`, `tobacco`, `other`,
    `animalandveterinary`, `cosmetic`, `transparency`.
-   `--endpoint`: Endpoint within the category (e.g., `event`, `label`, `510k`).
    See [references/api_endpoints.md](references/api_endpoints.md) for full
    list.
-   `--search`: Query string (e.g.,
    `patient.drug.medicinalproduct:aspirin+AND+serious:1`).
-   `--sort`: Sort field and order (e.g., `receivedate:desc`).
-   `--limit`: Max results (default 10, max 1000).
-   `--skip`: Pagination offset (default 0).
-   `--api_key`: API key (also reads `FDA_API_KEY` env var).

### 2. Count

Count unique values of a field within matching results.

```bash
python scripts/openfda_query.py count \
  --category drug --endpoint event \
  --search "patient.drug.medicinalproduct:aspirin" \
  --count_field "patient.reaction.reactionmeddrapt.exact" \
  --summary 10 --output /tmp/aspirin_reactions.json
```

Stdout prints a summary with the top 5 terms. Full data is in the output file.

*Additional options:*

-   `--count_field`: Field to count (append `.exact` for whole-phrase counting).
-   `--summary N`: Return only the top N most frequent terms. Use this to avoid
    flooding the context with hundreds of infrequent terms.

### 3. Download

Download multiple pages of results to a file.

```bash
python scripts/openfda_query.py download \
  --category drug --endpoint event \
  --search "patient.drug.medicinalproduct:aspirin" \
  --limit 100 --max_pages 5 \
  --output /tmp/aspirin_events.json
```

*Additional options:*

-   `--max_pages`: Maximum pages to fetch (default 10).
-   `--all_results`: Automatically paginate to fetch all matching results.
    Safety cap of 25,000 records maximum per download to prevent runaway
    downloads and prevent excessive API usage.

    > **Tip**: Common drugs can have excessive reports. Use a date range (e.g.,
    > `receivedate:[20250101+TO+20250131]`) to limit the volume of download.

## Entity Resolution: Using .exact for Precision

When searching for specific product names, drug names, or categorical terms,
always use the `.exact` suffix on the field to get exact-match results. Without
it, the API tokenizes multi-word values and returns noisy partial matches.

```bash
# Precise: matches only "ADVIL"
python scripts/openfda_query.py search --category drug --endpoint label \
  --search 'openfda.brand_name.exact:"ADVIL"' \
  --limit 5 --output /tmp/advil_label.json
```

> **Note**: Many brand names in the FDA database include variant suffixes (e.g.,
> "TYLENOL Extra Strength" rather than just "TYLENOL"). If an `.exact` search
> returns 0 results, try without `.exact` to see the available brand name
> variants, then re-query with the full exact name.

The `.exact` suffix is also required when using `--count_field` to aggregate
whole phrases instead of individual words.

## MedDRA Term Resolution

openFDA adverse event data uses MedDRA (Medical Dictionary for Regulatory
Activities) terms for reactions. The API reports **Preferred Terms (PTs)** but
does not provide the MedDRA hierarchy (System Organ Class, High Level Terms,
etc.).

> **Note**: MedDRA is a proprietary ontology and is **not indexed** in the
> EMBL-EBI OLS. To approximate MedDRA hierarchy lookups, use the **Human
> Phenotype Ontology (HP)** or **NCI Thesaurus (NCIT)** as proxy ontologies —
> they cross-reference MedDRA IDs and provide parent/ancestor relationships.

```bash
# Step 1: Get top reactions from openFDA
python scripts/openfda_query.py count \
  --category drug --endpoint event \
  --search "patient.drug.medicinalproduct:metformin" \
  --count_field "patient.reaction.reactionmeddrapt.exact" \
  --summary 5 --output /tmp/metformin_reactions.json

# Step 2: Look up the top reaction term using a biomedical ontology service
# skill (e.g. embl-ebi-ols skill).
# MedDRA is not available in OLS; use the Human Phenotype Ontology (HP) or
# NCI Thesaurus (NCIT) as a proxy to find the hierarchical classification of
# the reaction term.
```

## Available Endpoints (28 total)

Category to endpoint mapping:

-   `drug`: event, label, ndc, enforcement, drugsfda, shortages
-   `device`: 510k, classification, enforcement, event, pma, recall,
    registrationlisting, udi, covid19serology
-   `food`: enforcement, event
-   `tobacco`: problem, researchpreventionads, researchdigitalads,
    researchsmokefree
-   `other`: historicaldocument, nsde, substance, unii
-   `animalandveterinary`: event
-   `cosmetic`: event
-   `transparency`: crl

## Reference

-   **Query syntax and all endpoints**: See
    [references/api_endpoints.md](references/api_endpoints.md) for field names,
    search syntax, date ranges, and boolean operators.

## Recipes

Common query patterns for drugs, devices, foods, tobacco, cosmetics, animal and
veterinary products, substances, transparency data, adverse events, recalls,
labeling, approvals, shortages, 510(k) clearances, NDC lookups, any FDA safety
or regulatory data query, and more. See
[references/recipes.md](references/recipes.md) for the full recipes.

## Workflow

1.  Search for records using `search` with `--output`. Read the output file.
2.  Use `count` with `--summary 10 --output` to summarize field distributions.
3.  Use `download` (with `--all_results` for exhaustive pulls) to fetch larger
    datasets.
4.  Read and analyze the output file using standard tools.
5.  For MedDRA term hierarchy questions, use a biomedical ontology service skill
    (e.g. EMBL-EBI OLS skill with the HP or NCIT ontology) to look up the term.

• SKILL.md 9 KB
• execution_profile.json 1 KB
• scientific_contract.yaml 1 KB
• agents/openai.yaml 1 KB
• references/api_endpoints.md 6 KB
• references/recipes.md 4 KB
• scripts/openfda_query.py 14 KB