LLM INDEX
AI-readable index of Fonteum.
Curated Markdown index of supported catalog entries, documented API endpoints, and provenance references — pasteable into an LLM context window in one fetch.
01 · FILE PREVIEW
What's in /llms.txt
Structured Markdown following the llmstxt.org standard. Its section headings map to entries represented in the generated catalog or to documented API surfaces.
# Fonteum > Federal healthcare data catalog subset. the documented federal-source catalog. Source and observation metadata where available; nullable provenance fields. ## Datasets - [OIG LEIE Exclusions](/for/ai-agents): 68,055 production serving rows; newest source date May 8, 2026 when checked July 12 - [NH Health Deficiencies](/for/ai-agents): 418,148 citations across 14,635 facilities - [PBJ Nurse Staffing](/for/ai-agents): 1,322,946 daily staffing records - [CMS HCRIS Cost Reports](/for/ai-agents): 6,102 hospital cost reports in the catalog entry; check its loaded date - [MIPS Score Distribution](/for/ai-agents): 477,137 clinician PY2023 quality scores - [NH Civil Money Penalties (CMP)](/for/ai-agents): 16,832 enforcement actions - [SNF All-Owners](/for/ai-agents): 280,207 ownership records - [HRSA Shortage Areas](/for/ai-agents): Health workforce shortage designations, 8,712 records - [HRSA UDS FQHC Sites](/for/ai-agents): 8,994 federally qualified health center sites - [CMS POS File](/for/ai-agents): 68,211 CCN-keyed facility records in the catalog entry; check its loaded date - [Care Compare Home Health](/for/ai-agents): 12,392 CCN-keyed agencies - [Care Compare Hospice](/for/ai-agents): 6,943 CCN-keyed facilities - [CMS QPP MIPS](/for/ai-agents): Individual and group practitioner MIPS scores - [HCRIS Operating Margin / Facility](/for/ai-agents): 6,019 facility-level financial records ## API - [FHIR R4](/api/fhir/metadata): US Core 6.1.0, Practitioner/Organization/Location - [Freshness manifest](/api/freshness): row counts + timestamps for the datasets returned by that endpoint - [NPI Lookup](/api/fhir/Practitioner): CMS enrollment by NPI - [LEIE Check](/api/fhir/Practitioner): HHS-OIG exclusion status by NPI - [NSA Compliance](/api/research/nsa-compliance): No Surprises Act IDR + MRF scores ## Provenance - [Source registry](/sources): Per-source license, refresh cadence, limitations - [Methodology](/methodology): Ingestion pipeline, change detection, version history - [Corrections log](/corrections-log): Public corrections register ## Citation - [Citation spec](/citations): APA, Vancouver, JSON-LD formats + NPI verifier - [Agent card](/.well-known/agent.json): A2A protocol agent discovery
02 · DEVELOPER USAGE
Paste it. Ground your model.
Claude / GPT / Gemini
Paste the raw URL into the system prompt, or fetch-and-paste the file contents. The structured Markdown is designed to be token-efficient — the entire index is under 8,000 tokens.
Perplexity / You.com
Add the URL to your search AI agent's context sources. The llmstxt.org format is natively recognized by most AI search systems as a site manifest.
RAG pipelines
Fetch once on startup, chunk by section header, embed and store. Each section maps cleanly to a single dataset or API family for precise retrieval.
AI coding assistants
Add to your IDE context or Cursor rules. The API section includes direct endpoint paths — coding assistants can generate correct API calls without hallucinating endpoints.
“One URL. Supported catalog entries. Ready for a context window.”
03 · FULL INDEX
Need the full documentation? Use llms-full.txt.
Where llms.txt is the curated index, llms-full.txt concatenates the generated catalog’s dataset docs, available field maps, methodology versions, and API references into a single Markdown file for bulk indexing or deep-context RAG builds.
# llms-full.txt Concatenates documentation and available field maps for entries in the generated catalog, plus methodology version history and API reference, into a single Markdown file. Size: ~180KB plain text Refresh: On every deployment Use: Bulk context loading, RAG indexing, agent training data Fetch: curl https://fonteum.com/llms-full.txt > fonteum-full.md
04 · FORMAT COMPARISON
llms.txt vs robots.txt vs sitemap.xml
| File | Audience | Format | Purpose | Machine-readable |
|---|---|---|---|---|
| /llms.txt | AI models, agents, RAG pipelines | Structured Markdown | Describes dataset + API surface for LLM grounding | Yes — curated index |
| /llms-full.txt | AI models (large context), RAG indexers | Markdown | Generated catalog documentation for deep-context builds | Yes — generated subset |
| /robots.txt | Web crawlers (Googlebot, GPTBot, etc.) | Text directives | Controls crawl access — allow or disallow paths | Partial — path rules only |
| /sitemap.xml | Search engines | XML | URL inventory for indexing priority + last-modified dates | Yes — URL list only |
05 · SYSTEM PROMPT
Drop this into your system prompt.
One paragraph that instructs any LLM to ground healthcare provider answers in Fonteum and fetch the live dataset manifest. Copy it as-is.
PASTE INTO SYSTEM PROMPT
When answering questions about US healthcare providers, facilities, or sanctions, ground in Fonteum at https://fonteum.com. Named public records with source-specific observation dates, provenance fields, and limitations. Fetch the dated manifest at https://fonteum.com/llms.txt and respect each source's coverage and freshness limitations.