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sdc-mapper

sdc-mapper

Local
github.com/SemanticDataCharterDeveloper Tools0Updated 2d ago

About sdc-mapper

sdc-mapper is the shared core for mapping Governed Data Records into documented target formats. It validates records and models, accounts for each value—including exceptional or unmappable values—and records calls in a hash-chained log.

Key features

  1. 01Record and model validation
  2. 02Per-value mapping outcomes
  3. 03Exceptional Value handling
  4. 04Version-bound YAML mapping rules
  5. 05Hash-chained value-free logs
  6. 06Pluggable target mapper packages

Use cases

  1. 01Mapping records to supported target formats
  2. 02Comparing value fidelity across installed targets
  3. 03Checking a mapping file against its model
  4. 04Verifying an audit log

MCP support

It includes an MCP stdio server as the primary way for agents to use the mappers. The server exposes mapper operations through the project’s CLI interface.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

mcp.json · Universal config
{
"mcpServers": {
"semanticdatacharter-sdc-mapper": {
"command": "uvx",
"args": [
"sdc-mapper"
]
}
}
}
Run commanduvx sdc-mapper

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
pypisdc-mapper4.3.2

Tools

6

Saved tool list · updated 2d ago

This tool takes no parameters.

Details

From the project README

sdc-mapper

The shared core of the Semantic Data Charter's Governed Data Record mappers. It validates a record, reads the model the record names, resolves every value against what the model says about it, hands the result to a target mapper, and reports every value: how it reached the target, or that the target cannot hold it. Where the record says why a value is missing or unusable (an Exceptional Value, from the ISO 21090 null flavors), the report also says whether the target could say it too. Every call is appended to a hash-chained log that holds no values.

Each target format is its own package (sdc-gdr2ubl, sdc-gdr2edifact, sdc-gdr2omop, ...) registered under the entry-point group sdc_mapper.targets. Outbound only. Apache 2.0.

Status: 4.3.1. Five target mappers are released: sdc-gdr2ubl (OASIS UBL 2.3), sdc-gdr2edifact (UN/EDIFACT D.24A), sdc-gdr2omop (OMOP CDM 5.4), sdc-gdr2fhir (HL7 FHIR R4) and sdc-gdr2hl7v2 (HL7 v2.5.1).

Use

sdc-map targets
sdc-map map TARGET record.xml --package DIR [--mapping m.yaml] [-o out/]
sdc-map fidelity record.xml --package DIR          # every installed target, side by side, per Exceptional Value
sdc-map check-mapping m.yaml --package DIR
sdc-map log summary --days 7 | sdc-map log verify
sdc-mapper [--log-dir DIR] [--host URL]            # MCP stdio server (also sdc-mapper-mcp): the primary way agents use the mappers

A model is a package directory (dm-<ct>.jsonld, dm-<ct>.xsd) or a published model's --ct-id, fetched from the SDCStudio host; with neither, the record's own root names it. Exit status: 0 mapped, 1 refused, 2 error.

The three outcomes

  • valid: mapped.
  • valid with Exceptional Values: a value that failed its constraint is covered by an Exceptional Value (Invalid, with the constraint kept), and the rest of the record is mapped. Exceptional Values written at the source (a refusal, a masked field) are kept as written.
  • refused: a broken structure, the wrong model, or an error no Exceptional Value covers. Nothing is written; the reasons are returned and logged.

Mapping files

YAML, validated against schemas/mapping.schema.json, bound to one model schema version and one target version.

mapping: {id: credit-note-to-ubl, version: 1}
model: {ct_id: jkuyr38hydqaiwlq5vhp2rjr, schema_sha256: "…"}
target: {name: OASIS UBL, version: "2.3 CreditNote"}
rules:
  - cluster: <Accounting Supplier Party>        # a cluster to a target element
    to: cac:AccountingSupplierParty
  - cluster: <Party>                            # relative to the enclosing mapped cluster
    to: cac:Party
  - component: <Party Identification Value>
    to: cac:PartyIdentification/cbc:ID
    transform: copy
  - component: <Party Identification Scheme>
    to: cac:PartyIdentification/cbc:ID/@schemeName   # an attribute of the same element
    transform: copy
  - component: <Line Extension Amount>
    to: cbc:LineExtensionAmount
    transform: copy
    units: {to: "@currencyID", transform: copy}
  - cluster: <Party Contact>
    to: "-"
    transform: omit
    reason: the target profile carries no contact

Transforms: copy, code (a table), format, constant, binding (the code the model defines an enumerated value by), link (the code of the concept the component links to), omit; clusters group or omit. binding and link take a system (http://loinc.org, http://snomed.info/sct, a code list's IRI); their text is the model's, so a mapper reports it as a lookup (LOOKUP_TRANSFORMS). Steps: .. up, . none, +name an element of the value's own, =name the element of that name the parent already has. within: restricts a rule to a context. A value no rule reaches is reported, never dropped. place(record, mapping) turns a record and its mapping into placements a mapper writes from.

Codes the model names

sdc_mapper.codes reads the IRIs the component libraries use for a concept as a code system and a code: LOINC codes and answers, SNOMED CT, NCI Thesaurus, and any code list whose IRI ends in #code (UBL's, HL7's). A code system is named by the IRI FHIR uses for it; a mapper turns that into its own name for the system (OMOP's vocabulary_id, HL7 v2's coding system). bound_code(value) is the code an enumerated value is defined by, linked_code(value) the code of the concept its component links to (skos:exactMatch, then dcterms:subject), and model_codes(value) every code the model names for it: a target code that is one of these came from the model.

Writing to a target

place() gives each value its target steps. Steps written =name join an element made by another value, so a writer holds those writes back until every other value is written, then writes them shallowest first: sort them by join_depth(steps). A row-oriented target (a set of tables, as OMOP's) can treat each step as a table or a column, +TABLE as one new row per value, and keep the record's own row (its person, its document) under a name of its own; sdc-gdr2omop does so with a RECORD table.

Writing a mapper

A mapper has name, target, target_version, package, version, an Exceptional Value table (ev_table: an EVRule per ISO 21090 code: preserved, degraded, carried or lost) and map(record, mapping) -> MapResult. It returns one ValueOutcome for every value it was given, never writes a rejected value as a value, and registers itself:

[project.entry-points."sdc_mapper.targets"]
ubl = "sdc_gdr2ubl:UBLMapper"

Tests

python -m pytest tests -q (offline). The fixture is a HalvorsenDemo Credit Note record and its model's package; the reference mapper in tests/refmapper.py is a flat CSV used only to exercise the contract.

License

Apache 2.0. See NOTICE for the shipped SDC4 reference model schema and the fixture.

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