About sdcreader
sdcreader is a Python reader and read-only MCP server for published Semantic Data Charter model packages. It serves projection developers and agents with consistent access to package metadata, record structure, codes, and schema integrity checks.
Key features
- 01Fetches models from the public catalog
- 02Reads package and schema metadata
- 03Lists record leaves and constraints
- 04Exposes enumerated codes and definitions
- 05Checks package integrity and schema version
Use cases
- 01Inspect a model before projecting it into another standard
- 02Let an agent search models and inspect their fields
- 03Verify that a local package matches its published schema
MCP support
The read-only MCP server exposes tools to search models, describe a model, list leaves and codes, and check a package. It uses a fixed catalog host or a local package directory.
Configuration
Local server config is the same for every client. Copy it into your client’s MCP config file.
Tools
5Saved tool list · updated 4d ago
| Parameter | Type | Required | Description |
|---|---|---|---|
| page | integer | No | |
| search | string | No | Text matched against title and description. |
| project | string | No | A public project's exact name, for example NIH_CDE. |
| page_size | integer | No |
Details
From the project READMEsdcreader
Read a published Semantic Data Charter model's package for projection into other standards.
A projection describes a model's governed data records in another standard's terms: CDIF, DCAT-US 3.0, DCAT-AP
3.0.1, HealthDCAT-AP, and whatever comes next. What every projection describes is the same evidence, the model's
package, and reads it into the same shape. sdcreader is that evidence and that shape, once, with nothing about
any projection in it. The four projection writers (sdc_cdif, sdc_dcat3_us, sdc_dcat3_ap, sdc_healthdcat_ap)
carried it as identical copies until this package.
pip install sdcreader # the reader, with HTTP for fetching from the public catalog (since 4.1.0 no extra is needed)
from sdcreader import fetch_package, load_package, read_model
pkg = load_package("samples/nhanes-participant") # a package on disk
pkg = fetch_package("xy8upneajsb8vdcmnve01g6g", save_to="pkg/") # or from SDCStudio's public catalog, no account
model = read_model(pkg)
pkg.schema_url_pinned # https://sdcstudio.axius-sdc.com/dmlib/dm-xy8u...xsd?sha256=0df4... the exact bytes the records are validated against
model.title, model.leaves # 'NHANES Participant', 153 leaves in document order, each with its path of clusters
model.codes[ct_id] # the enumerated values of a component with the IRI, definition and code the schema annotates them with
model.dc("publisher") # what the modeler wrote, None where SDCStudio's default sits
What a package is
SDCStudio generates a package for every published model and serves it publicly for models in public projects:
GET /api/v1/catalog/dm/<ct>/ | the catalog record: title, description, project, the artifact list (catalog.json) |
GET /api/v1/catalog/dm/<ct>/<artifact>/ | a storage pointer, {"download_url": ..., "filename": ..., "storage": "gcs"}, for jsonld, xsd and the rest; the reader follows it |
dm-<ct>.jsonld | the model and its components: type, label, description, cardinality, constraints (enumerations, units, ranges, patterns), semantic links (dcterms:identifier the library slot, dcterms:source, dcterms:publisher, skos:exactMatch, rdfs:seeAlso, prov:wasRevisionOf), contains |
GET /dmlib/dm-<ct>.xsd | the schema bytes: the Dublin Core header for the model, and for each enumerated value its definition and the code it is defined by (dm-<ct>.xsd) |
GET /dmlib/dm-<ct>.versions.json | every published version of the schema by SHA-256, with the current one (versions.json) |
A package directory holds those four files. The reader refuses a directory without the JSON-LD and the schema, a JSON-LD whose identifier is not the directory's model, and a schema whose bytes are not the published current version: a projection cites the schema by URL and SHA-256, so the bytes have to be the ones that URL serves.
Two gaps in the package are on SDCStudio's issue tracker: the JSON-LD metadata drops fields the schema header
carries (#748), and the header omits dc:source (#749). The reader takes the model's Dublin Core from the header,
which is complete.
What the reader gives
ModelPackage: the four files, the SHA-256, the schema URL and its pinned form (?sha256=), the public catalog URL, andcheck().Model:title,description,root(the governed record's cluster),componentsby identifier,leavesin document order (each aLeafwith itscomponent, itspathof clusters and aslugof that path, unique in the record),codes(the enumerated values per component asCode: value, IRI, definition, the code it is defined by),metadata(the JSON-LD's) andheader(the schema's Dublin Core).Model.dc(name): what the modeler wrote, the header first and the JSON-LD second, with SDCStudio's field defaults as unset: "Universal" for coverage, "None" for relation, "SDC Data Model (DM)" for type, blanks. A projection never publishes a default as a fact.contributors,subjects(the semicolon list),rights_urlandrights_statementderive from it.Component:sdc_type,label,description,data_type,constraints,linksby predicate (link(p)for the first),contains,iri.- Constants: the leaf, numeric and quantified types; the predicate IRIs
IDENTIFIER,SOURCE,PUBLISHER,EXACT,CLOSE,SEE_ALSO,HAS_UNIT;DC_DEFAULTS.
MCP server
sdcreader is also an MCP server, registered as io.github.SemanticDataCharter/sdcreader, so an assistant can read a
published model the way sdcvalidator, sdcgovernance and sdcreceipt let it validate, govern and settle a record.
stdio, no SDK, no account, read-only.
uvx sdcreader # or: sdcreader-mcp, or python -m sdcreader.mcp_server
uvx sdcreader --host https://your-sdcstudio.example # a sovereign SDCStudio's public catalog
| Tool | Does |
|---|---|
find_models | search the public catalog by text, or list a public project's models by name |
describe_model | one model: title, project, root record, leaf and component counts by type, Dublin Core with SDCStudio's defaults as null, the schema URL pinned to its SHA-256 |
list_leaves | the leaves in document order with path, type, constraints, code count and identity links, paged |
list_codes | one component's enumerated values with IRI, definition and code system |
check_package | the reader's integrity check on a package directory |
Each tool takes a ct_id (fetched from the host the server was started with) or a package directory on disk.
No tool takes a URL: the host is fixed at start, so a caller cannot point the server elsewhere. Every tool calls the
same functions as the library; the two interfaces describe one model the same way.
Tests
pip install -e ".[dev]"
python -m pytest tests -q # includes one fetch against production
python -m pytest tests -q -m "not network" # offline, as CI runs it
The fixture is the NHANES Participant package of the FAIR Data Demo, the record every projection is proven on.
Versioning
The leading 4 is the SDC4 reference model, as with every Semantic Data Charter package. An addition to what the reader exposes is a minor version, a fix a patch; the major changes only when the reference model does.
License
Apache-2.0 (see LICENSE, NOTICE).
Comments
New to MCP?
From what the protocol is to installing your first server, these answers cover most first questions.
An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.