> ## Documentation Index
> Fetch the complete documentation index at: https://docs.particle.pro/llms.txt
> Use this file to discover all available pages before exploring further.
# Brand Suitability
> Industry-standard content risk and advertiser-fit assessment for podcasts: per-category prevalence and treatment, evidence excerpts, and a deterministic trend across analyses.
Particle API assesses every well-classified podcast against the 12 categories from the [IAB Tech Lab Content Taxonomy 3.x Brand Safety & Suitability Framework](https://iabtechlab.com/standards/content-taxonomy/) — the industry-standard taxonomy that advertisers, agencies, DSPs, and ad-verification vendors (IAS, DoubleVerify, Spotify, Hive, Sounder, Barometer) train against. The framework was originally published by GARM (Global Alliance for Responsible Media) in 2022; IAB Tech Lab assumed stewardship after GARM was discontinued in 2024 and continues to publish and version it. Each assessment covers a contiguous, non-overlapping window of recent episodes, refreshed on a risk-tiered cadence.
Available to MCP agents through [`particle_podcast_resolve`](/mcp/tools/podcasts/podcast-resolve): every result carries the high-level `suitability_tier` enum unconditionally, and passing `include: ["suitability"]` attaches the per-category breakdown alongside the podcast. Corpus-wide publisher views are [`particle_podcast_get_suitability_leaderboard`](/mcp/tools/podcast_suitability/podcast-get-suitability-leaderboard) and [`particle_podcast_list_suitability_category_publishers`](/mcp/tools/podcast_suitability/podcast-list-suitability-category-publishers) (opt-in `podcast_suitability` category).
## What the assessment gives you
A buy-or-skip decision needs more than a flat 0–1 risk score per category. A true-crime documentary about Bundy is heavy on crime content but advertiser-friendly; a host endorsing violent vigilantism is light on crime content but advertiser-toxic. The Particle assessment captures that distinction along five dimensions:
* **Per-category prevalence** — `NONE`, `INCIDENTAL`, `OCCASIONAL`, `FREQUENT`, `PERVASIVE`. How often the category appears across the analyzed window.
* **Per-category treatment** — `ABSENT`, `DOCUMENTARY`, `EDITORIAL`, `PROMOTIONAL`, `GLAMORIZING`. How the content is framed when it appears. Treatment is the entire reason a news show covering terrorism stays advertiser-friendly while a podcast endorsing it does not.
* **Per-category risk level** — derived deterministically from `(prevalence, treatment, code)`. `NONE`, `LOW`, `MEDIUM`, `HIGH`, or `FLOOR` (violates the Brand Safety Floor).
* **Per-category evidence** — every category with prevalence above `NONE` carries one or more cited episode excerpts so you can audit the verdict and defend planner decisions to clients.
* **Methodology transparency** — sample size, confidence, and the time window of episodes that produced the verdict, all on every response.
## Tiers
The overall verdict maps to the framework's four risk levels:
| Tier | Framework risk level | Advertiser fit |
| ----------- | -------------------- | ------------------------------------------------------------------------------------ |
| `SAFE` | Low Risk | Suitable for all advertisers, including kid-directed and family brands. |
| `LIMITED` | Medium Risk | Suitable for most advertisers; family brands may apply caution. |
| `SENSITIVE` | High Risk | Many brands skip; suitable for general-audience and adult-product advertisers. |
| `UNSAFE` | Brand Safety Floor | No monetization — content violates the Brand Safety Floor for at least one category. |
## Categories
All 12 brand-safety categories are evaluated on every assessment, even when prevalence is `NONE`, so the response records that the category was actively considered.
| Code | Description |
| -------------------------------- | -------------------------------------------------------------------------------------------------------------------------- |
| `adult_sexual` | Adult or sexually explicit content. |
| `arms_ammunition` | Arms, ammunition, and weapon-related content. |
| `crime_harmful_acts` | Crime, harmful acts to individuals, and human-rights violations. |
| `death_injury_military_conflict` | Death, injury, or military conflict. |
| `online_piracy` | Online piracy and copyright infringement. |
| `hate_speech_aggression` | Hate speech and acts of aggression directed at protected groups. |
| `obscenity_profanity` | Obscenity and profanity, including gestures. |
| `illegal_drugs_alcohol_tobacco` | Illegal drugs, tobacco, e-cigarettes, vaping, or alcohol. |
| `spam_harmful` | Spam or harmful content (malware, phishing, scams). |
| `terrorism` | Terrorism, including its promotion or glorification. |
| `debated_social_issues` | Debated sensitive social issues with insensitive or harmful treatment. |
| `misinformation` | Factually incorrect or deliberately misleading content presented as fact (added in the September 2023 framework revision). |
## Get a podcast's suitability
```bash curl theme={"dark"}
curl "https://api.particle.pro/v1/podcasts/the-jane-doe-show/suitability" \
-H "X-API-Key: $PARTICLE_API_KEY"
```
```js JavaScript theme={"dark"}
const res = await fetch(
"https://api.particle.pro/v1/podcasts/the-jane-doe-show/suitability",
{ headers: { "X-API-Key": process.env.PARTICLE_API_KEY } },
);
const assessment = await res.json();
```
```python Python theme={"dark"}
res = httpx.get(
"https://api.particle.pro/v1/podcasts/the-jane-doe-show/suitability",
headers={"X-API-Key": os.environ["PARTICLE_API_KEY"]},
)
assessment = res.json()
```
```jsonc Response (truncated) theme={"dark"}
{
"overall_tier": "LIMITED",
"confidence": "HIGH",
"summary": "True-crime show with documentary treatment of historical American cases. No glorification; sponsor base is mainstream consumer brands.",
"methodology": "Reviewed segments for 12 episodes from a 6-month window; read full transcripts for 5. Cross-checked sponsors for drug- and predatory-finance categories.",
"episodes_analyzed": 25,
"sample_window_start_at": "2025-11-04T00:00:00Z",
"sample_window_end_at": "2026-04-29T00:00:00Z",
"evaluated_at": "2026-05-02T18:14:21Z",
"categories": [
{
"code": "crime_harmful_acts",
"description": "Crime, harmful acts to individuals, and human-rights violations.",
"prevalence": "FREQUENT",
"treatment": "DOCUMENTARY",
"risk_level": "MEDIUM",
"reasoning": "Hosts narrate established cases without endorsement, citing primary sources and court records.",
"evidence": [
{
"episode_id": "78cgekLUjCJBUZbj3s5K8Y",
"episode_title": "The Jane Doe Case",
"episode_slug": "the-jane-doe-case",
"excerpt": "We're going to walk through the timeline based on the trial transcripts..."
}
]
},
{
"code": "obscenity_profanity",
"description": "Obscenity and profanity, including gestures.",
"prevalence": "OCCASIONAL",
"treatment": "EDITORIAL",
"risk_level": "LOW",
"reasoning": "Profanity used for emphasis, never directed at protected groups.",
"evidence": [/* … */]
}
// 10 more entries — every brand-safety category appears, even when prevalence=NONE.
]
}
```
## Optional sections
Pass `include=trend`, `include=history`, or `include=trend,history` to expand the response.
```bash theme={"dark"}
curl "https://api.particle.pro/v1/podcasts/the-jane-doe-show/suitability?include=trend,history" \
-H "X-API-Key: $PARTICLE_API_KEY"
```
`trend` is computed deterministically from the diff between the two most recent assessments — the agent never compares across windows. Each analysis covers a contiguous, non-overlapping window of episodes, so the comparison is genuinely "what was true in the prior window vs. what's true now."
```jsonc Response (truncated) theme={"dark"}
{
"overall_tier": "LIMITED",
// ... main body unchanged ...
"trend": {
"direction": "STABLE",
"prior_evaluated_at": "2025-11-12T03:55:08Z",
"changed_categories": [
{
"code": "obscenity_profanity",
"prior_prevalence": "INCIDENTAL",
"new_prevalence": "OCCASIONAL",
"prior_treatment": "EDITORIAL",
"new_treatment": "EDITORIAL",
"prior_risk_level": "LOW",
"new_risk_level": "LOW"
}
]
},
"history": [
{
"overall_tier": "LIMITED",
"confidence": "HIGH",
"episodes_analyzed": 25,
"sample_window_start_at": "2025-05-01T00:00:00Z",
"sample_window_end_at": "2025-11-04T00:00:00Z",
"evaluated_at": "2025-11-12T03:55:08Z"
}
// …
]
}
```
`direction` is one of `STABLE`, `IMPROVING`, `DECLINING`, or `INSUFFICIENT_HISTORY` (when fewer than two assessments exist).
## Filter the catalog by tier
`GET /v1/podcasts` accepts a `suitability_tier` query parameter. Returns only podcasts whose most recent assessment landed at that tier — useful for buyer-side catalog scans:
```bash theme={"dark"}
curl "https://api.particle.pro/v1/podcasts?suitability_tier=SAFE&limit=20" \
-H "X-API-Key: $PARTICLE_API_KEY"
```
The high-level `suitability_tier` enum is also surfaced on every podcast object in list and detail responses; the per-category breakdown, evidence, and methodology are gated to the dedicated suitability endpoint.
## Refresh cadence
Each assessment covers a contiguous, non-overlapping window of recent episodes — the most-recent \~25, scoped above the prior assessment's window so no episode is ever re-analyzed across runs. Refresh thresholds depend on the prior tier:
| Prior tier | Re-evaluates after this many new episodes | Max wall-clock staleness |
| ----------- | ----------------------------------------- | ------------------------ |
| `SAFE` | 25 new episodes since last window | 24 months |
| `LIMITED` | 20 new episodes | 24 months |
| `SENSITIVE` | 15 new episodes | 12 months |
| `UNSAFE` | 10 new episodes | 9 months |
A daily show producing \~30 episodes per month therefore refreshes roughly monthly when SAFE and weekly when UNSAFE. A weekly show refreshes roughly every 5–6 months when SAFE.
## Limitations
* **Minimum coverage.** A first assessment requires at least 5 episodes with classified topics. Podcasts below this threshold return a 404 from this endpoint.
* **English-language bias.** Treatment classification is most reliable for English-language content; languages with limited transcript coverage may produce lower-confidence assessments.
* **Sample-based.** Each assessment is a snapshot of a contiguous window, not the full catalog. Across many assessments over a podcast's lifetime, the union of windows covers the publishing history; for catalog-wide claims combine `include=history` with the per-window `sample_window_start_at` / `sample_window_end_at` boundaries.
## Related
* [Bias analysis](/podcasts/overview#bias-analysis) — political bias rating, the closest analog (per-podcast LLM-derived assessment with evidence and methodology).
* [Episodes](/podcasts/episodes) — drill into the episodes that produced an assessment.
* [Concepts → Pricing weight](/concepts#pricing-weight) — suitability calls are priced higher per call.