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Get Started Free →Clinical trial registry database search API
.claude/skills/brycewang-stanford-clinicaltrials-api/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 45% | 0% |
| case-04 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-05 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-15 | ✗→✓ | ▲ Improved | 73% | 0% |
| case-17 | ✗→✓ | ▲ Improved | 17% | 0% |
ClinicalTrials.gov is a registry and results database of publicly and privately supported clinical studies conducted around the world, maintained by the U.S. National Library of Medicine (NLM) at the National Institutes of Health (NIH). It contains registration records for over 500,000 clinical trials from more than 220 countries, making it the largest and most comprehensive clinical trial registry in the world.
The ClinicalTrials.gov API provides programmatic access to this vast repository of clinical trial data. Researchers can search for trials by condition, intervention, sponsor, location, phase, status, and many other criteria. The API returns detailed structured data including study design, eligibility criteria, outcome measures, enrollment information, study contacts, and results when available.
Clinical researchers, pharmaceutical companies, systematic reviewers, epidemiologists, public health officials, patient advocacy groups, and health policy analysts use the ClinicalTrials.gov API to monitor the clinical trial landscape, identify recruiting studies, conduct meta-analyses, analyze research trends, and ensure comprehensive evidence coverage in systematic reviews. The database is a critical resource for evidence-based medicine and regulatory compliance.
No authentication required. The ClinicalTrials.gov API is freely accessible without any API key, token, or registration. All endpoints are publicly available. Users are expected to comply with the NCBI usage policies and make requests at a reasonable rate.
Search the ClinicalTrials.gov database and retrieve full study records with comprehensive metadata about trial design, eligibility, interventions, and outcomes.
GET https://clinicaltrials.gov/api/v2/studies| Parameter | Type | Required | Description | |----------------|--------|----------|------------------------------------------------------------| | query.term | string | No | Free-text search query across all fields | | query.cond | string | No | Condition or disease filter | | query.intr | string | No | Intervention or treatment filter | | query.spons | string | No | Sponsor or collaborator filter | | filter.overallStatus | string | No | Status filter: RECRUITING, COMPLETED, ACTIVE_NOT_RECRUITING | | filter.phase | string | No | Phase filter: PHASE1, PHASE2, PHASE3, PHASE4 | | filter.geo | string | No | Geographic filter (distance:lat,lng) | | sort | string | No | Sort field and direction | | pageSize | int | No | Results per page (default 10, max 1000) | | pageToken | string | No | Token for next page of results | | format | string | No | Response format: json (default) or csv |
bash# Search for recruiting cancer immunotherapy trials curl "https://clinicaltrials.gov/api/v2/studies?query.cond=cancer&query.intr=immunotherapy&filter.overallStatus=RECRUITING&pageSize=5" # Search by sponsor curl "https://clinicaltrials.gov/api/v2/studies?query.spons=NIH&filter.phase=PHASE3&pageSize=10"
totalCount, nextPageToken, and studies array. Each study contains protocolSection with identificationModule (NCT ID, title, organization), statusModule (overall status, start/completion dates), descriptionModule (brief summary, detailed description), conditionsModule (conditions and keywords), designModule (study type, phases, allocation, intervention model), armsInterventionsModule, eligibilityModule (criteria, gender, age range), contactsLocationsModule, and outcomesModule.Retrieve a specific clinical trial by its NCT identifier.
GET https://clinicaltrials.gov/api/v2/studies/{nctId}| Parameter | Type | Required | Description | |-----------|--------|----------|--------------------------------------------| | nctId | string | Yes | NCT identifier (e.g., NCT04280705) |
bashcurl "https://clinicaltrials.gov/api/v2/studies/NCT04280705"
No formal rate limits are documented for the ClinicalTrials.gov API. However, the service follows NCBI usage guidelines which recommend no more than 3 requests per second without an API key, and up to 10 requests per second with an NCBI API key. For bulk data access, ClinicalTrials.gov provides downloadable data files at https://clinicaltrials.gov/AllPublicXML.zip and via the AACT (Aggregate Analysis of ClinicalTrials.gov) database at https://aact.ctti-clinicaltrials.org/.
Track actively recruiting trials for a specific disease or condition:
pythonimport requests params = { "query.cond": "Alzheimer's Disease", "filter.overallStatus": "RECRUITING", "filter.phase": "PHASE3", "pageSize": 20 } resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) data = resp.json() print(f"Found {data['totalCount']} recruiting Phase 3 Alzheimer's trials\n") for study in data["studies"]: proto = study["protocolSection"] ident = proto["identificationModule"] status = proto["statusModule"] print(f"{ident['nctId']}: {ident['briefTitle']}") print(f" Status: {status['overallStatus']}") print(f" Start: {status.get('startDateStruct', {}).get('date', 'N/A')}") print()
Perform a comprehensive search for systematic review inclusion screening:
pythonimport requests all_studies = [] page_token = None while True: params = { "query.cond": "type 2 diabetes", "query.intr": "metformin", "filter.overallStatus": "COMPLETED", "pageSize": 100 } if page_token: params["pageToken"] = page_token resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) data = resp.json() all_studies.extend(data["studies"]) page_token = data.get("nextPageToken") if not page_token: break print(f"Total completed metformin trials for T2D: {len(all_studies)}")
Extract and analyze study design features for research landscape mapping:
pythonimport requests from collections import Counter params = { "query.cond": "COVID-19", "filter.phase": "PHASE3", "pageSize": 100 } resp = requests.get("https://clinicaltrials.gov/api/v2/studies", params=params) data = resp.json() sponsors = Counter() for study in data["studies"]: org = study["protocolSection"]["identificationModule"].get("organization", {}) sponsors[org.get("fullName", "Unknown")] += 1 print("Top sponsors of Phase 3 COVID-19 trials:") for sponsor, count in sponsors.most_common(10): print(f" {sponsor}: {count} trials")
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | pass→pass | 8,834 | 8,870 | +0% | 1 | 1 | 0% | 1,561 | 3,696 | +137% | 0 | 0 | — |
case-03 | pass→pass | 10,476 | 9,484 | -9% | 1 | 1 | 0% | 1,697 | 3,440 | +103% | 0 | 0 | — |
case-01 | fail→pass | 15,053 | 11,543 | -23% | 1 | 1 | 0% | 2,679 | 3,885 | +45% | 0 | 0 | — |
case-04 | fail→pass | 12,129 | 5,384 | -56% | 1 | 1 | 0% | 1,869 | 2,749 | +47% | 0 | 0 | — |
case-05 | fail→pass | 19,702 | 16,236 | -18% | 1 | 1 | 0% | 3,283 | 5,062 | +54% | 0 | 0 | — |
case-06 | pass→pass | 6,600 | 4,203 | -36% | 1 | 1 | 0% | 1,153 | 2,706 | +135% | 0 | 0 | — |
case-07 | pass→pass | 6,581 | 4,248 | -35% | 1 | 1 | 0% | 1,110 | 2,700 | +143% | 0 | 0 | — |
case-08 | pass→pass | 8,324 | 3,956 | -52% | 1 | 1 | 0% | 1,378 | 2,633 | +91% | 0 | 0 | — |
case-09 | pass→pass | 8,213 | 5,523 | -33% | 1 | 1 | 0% | 1,499 | 2,927 | +95% | 0 | 0 | — |
case-10 | pass→pass | 3,635 | 2,832 | -22% | 1 | 1 | 0% | 662 | 2,394 | +262% | 0 | 0 | — |
case-11 | pass→pass | 8,628 | 6,208 | -28% | 1 | 1 | 0% | 1,520 | 3,121 | +105% | 0 | 0 | — |
case-12 | pass→pass | 6,638 | 4,157 | -37% | 1 | 1 | 0% | 1,258 | 2,705 | +115% | 0 | 0 | — |
case-13 | pass→pass | 9,643 | 4,607 | -52% | 1 | 1 | 0% | 1,732 | 2,673 | +54% | 0 | 0 | — |
case-14 | pass→pass | 8,156 | 3,811 | -53% | 1 | 1 | 0% | 1,430 | 2,576 | +80% | 0 | 0 | — |
case-15 | fail→pass | 13,225 | 8,063 | -39% | 1 | 1 | 0% | 1,904 | 3,302 | +73% | 0 | 0 | — |
case-16 | pass→pass | 12,695 | 6,722 | -47% | 1 | 1 | 0% | 2,096 | 3,160 | +51% | 0 | 0 | — |
case-17 | fail→pass | 14,770 | 3,224 | -78% | 1 | 1 | 0% | 2,124 | 2,483 | +17% | 0 | 0 | — |
case-18 | pass→pass | 9,269 | 4,318 | -53% | 1 | 1 | 0% | 1,583 | 2,721 | +72% | 0 | 0 | — |
case-19 | fail→pass | 6,446 | 2,984 | -54% | 1 | 1 | 0% | 1,190 | 2,422 | +104% | 0 | 0 | — |
case-20 | fail→pass | 8,004 | 7,020 | -12% | 1 | 1 | 0% | 1,400 | 3,125 | +123% | 0 | 0 | — |
case-21 | pass→pass | 10,656 | 6,300 | -41% | 1 | 1 | 0% | 2,195 | 3,193 | +45% | 0 | 0 | — |
case-22 | pass→pass | 11,013 | 7,399 | -33% | 1 | 1 | 0% | 2,127 | 3,413 | +60% | 0 | 0 | — |
case-23 | fail→pass | 5,227 | 4,557 | -13% | 1 | 1 | 0% | 962 | 2,828 | +194% | 0 | 0 | — |
case-24 | pass→pass | 4,726 | 4,674 | -1% | 1 | 1 | 0% | 883 | 2,699 | +206% | 0 | 0 | — |
case-25 | pass→pass | 11,063 | 8,763 | -21% | 1 | 1 | 0% | 1,919 | 3,641 | +90% | 0 | 0 | — |
case-26 | pass→pass | 7,356 | 1,995 | -73% | 1 | 1 | 0% | 1,286 | 2,234 | +74% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 26 cases were attempted. The headline lift of +31 percentage points is the difference between those two pass rates over the 26 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.