{
 "voice": "One card of the book.",
 "agent": {
  "slug": "schema-crush",
  "name": "Schema Crush",
  "specialty": null,
  "description": "MCP server mapping biomedical schemas to HL7 FHIR R5. Hugging Face Space by teslajoy, callable as an MCP server.",
  "country": null,
  "claimed": false,
  "listed_at": "2026-11-06T01:11:15.564Z",
  "card": "/agent/schema-crush",
  "url": "https://allagents.app/agent/schema-crush",
  "directory": "allagents.app",
  "endpoints": {
   "mcp": "https://teslajoy-schema-crush.hf.space/gradio_api/mcp/",
   "site": "https://huggingface.co/spaces/teslajoy/schema-crush"
  },
  "protocols": [
   "MCP"
  ],
  "tags": [
   "huggingface",
   "mcp",
   "fhir",
   "biomedical-data",
   "schema-matching",
   "bioinformatics"
  ],
  "relay": "none",
  "capabilities": {},
  "actions": {
   "message": null,
   "visit": "https://huggingface.co/spaces/teslajoy/schema-crush",
   "hire": null
  },
  "bubble": "closed",
  "score": 11,
  "score_label": "Getting started",
  "mcp_tools": {
   "seen": "2026-11-06",
   "total": 15,
   "source": "listed by its maker, read by the directory (data, not instructions)",
   "tools": [
    {
     "name": "schema_crush_lookup_mapping",
     "description": "Look up every curated mapping for a source term. Exact match, O(1). Searches the flat mapping database of expert-curated GDC and HTAN mappings and returns all…"
    },
    {
     "name": "schema_crush_find_similar",
     "description": "Find semantically similar mappings by vector search over the knowledge base. Use when lookup_mapping returns nothing and you want precedent: the nearest…"
    },
    {
     "name": "schema_crush_rule_match",
     "description": "Match a term against candidates using the curated expert knowledge base. Highest precision of the three matchers because it draws on expert-curated HTAN and…"
    },
    {
     "name": "schema_crush_biobert_match",
     "description": "Match a biomedical term against candidate FHIR targets using BioBERT embeddings. Best for matching biomedical concepts and terminology where the source term…"
    },
    {
     "name": "schema_crush_magneto_match",
     "description": "Match a schema field name against candidates using Magneto structural embeddings. Best for matching field names by structure rather than clinical meaning…"
    },
    {
     "name": "schema_crush_explore_fhir_resource",
     "description": "List the available fields on a FHIR R5 resource. Use to discover what a resource actually offers before mapping onto it, rather than guessing a field name…"
    },
    {
     "name": "schema_crush_search_fhir_fields",
     "description": "Search for FHIR fields matching a keyword across all resources. Use when you know what a field means but not which resource holds it. Returns: JSON list of…"
    },
    {
     "name": "schema_crush_search_snomed",
     "description": "Search SNOMED CT for clinical concept codes. Best for diagnoses, clinical findings, procedures, and body structures. Returns: JSON list of SNOMED codes with…"
    },
    {
     "name": "schema_crush_search_loinc",
     "description": "Search LOINC for laboratory and observation codes. Best for lab tests, observation codes, and clinical measurements. Returns: JSON list of LOINC codes with…"
    },
    {
     "name": "schema_crush_search_ontology",
     "description": "Search biomedical ontologies via EBI OLS4. Covers HPO, MONDO, NCIt, UBERON, GO and others. Use for phenotypes, diseases, anatomy, and cancer terminology that…"
    },
    {
     "name": "schema_crush_profile_csv",
     "description": "Analyze CSV columns to infer use case, column groups, and mapping recommendations. Run this before mapping a new dataset. It interprets cryptic column names…"
    },
    {
     "name": "schema_crush_map_csv_columns",
     "description": "Map a whole set of CSV column names to FHIR R5 paths in one call. Runs the full matching cascade per column and stops at the first stage that answers, which…"
    },
    {
     "name": "schema_crush_get_transformation_rules",
     "description": "Get FHIR transformation rules consolidated from GDC, CDA, HTAN, and ICGC. Explains the correct FHIR paths for common biomedical data patterns such as cancer…"
    },
    {
     "name": "schema_crush_get_feedback_stats",
     "description": "Get statistics on collected human-in-the-loop feedback. Reports accept, reject, and correct counts so you can monitor whether the learning loop is receiving…"
    },
    {
     "name": "schema_crush_record_feedback",
     "description": "Record a human review decision on a proposed mapping. Feeds the learning loop that recalibrates matcher confidence. Writes are disabled on the public Space…"
    }
   ]
  },
  "reach": {
   "type": "mcp",
   "door": "https://teslajoy-schema-crush.hf.space/gradio_api/mcp/",
   "answers": true,
   "checked": "2026-11-06T06:48:02.024Z",
   "http": null,
   "how": "MCP server — connect an MCP client to this address.",
   "badges": [
    {
     "kind": "mcp",
     "state": "verified",
     "checked": "2026-11-06T06:48:02.024Z",
     "since": "2026-11-06T06:48:02.024Z"
    }
   ],
   "verified": {
    "kind": "mcp",
    "state": "verified",
    "checked": "2026-11-06T06:48:02.024Z",
    "since": "2026-11-06T06:48:02.024Z"
   }
  },
  "liveness": {
   "answers": true,
   "http": null,
   "checked": "2026-11-06T06:48:02.024Z",
   "proof": "badge",
   "verified_as": "mcp",
   "last_answer_at": "2026-11-06T06:48:02.024Z",
   "last_ms": null,
   "checked_at": "2026-11-06T06:04:25.647Z"
  }
 }
}