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Sdcreader

by SemanticDataCharterversion 4.4.1i★ –iRuns on your computer
Registry nameio.github.SemanticDataCharter/sdcreader

Install

Overview

Description from the maker (original English)

Read published Semantic Data Charter models: catalog search, record structure, codes, pinned schema.

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

  • Connection: Runs on your computer
  • Transport: stdio
  • Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

  • Basic checks: 1 of 1 passed
  • Runs on your computer with your user’s permissions (files, network, apps).
  • The registry lists no key to fill in.

You need

  • PyPIPython’s uv installed (for the uvx command)

Which app do you use?

Pick your app above. We remember it, so next time the button says “Install in …”.

Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json

{
  "mcpServers": {
    "sdcreader": {
      "command": "uvx",
      "args": [
        "sdcreader"
      ]
    }
  }
}

Run this in your terminal

claude mcp add sdcreader -- uvx sdcreader

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "sdcreader": {
      "command": "uvx",
      "args": [
        "sdcreader"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "sdcreader": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "sdcreader"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026

  • ✓ Pass

    Maker matches the repo

    The registry name was verified with the GitHub account SemanticDataCharter, which owns the repo

  • … Not checked yet

    Still maintained

    We have not read this repo from GitHub yet

  • … Not checked yet

    Has a licence

    We have not read this repo from GitHub yet

  • – Not applicable

    Endpoint responds

    This one runs on your computer; there is no internet endpoint

  • – Not applicable

    Sign-in with OAuth

    Runs on your computer and uses local settings instead

  • … Not checked yet

    No hidden instructions in tool descriptions

    Runs on your computer; we can only read tool lists from internet endpoints

Compared with others in Search & AI memoryi

  • More stars than 0% of the other 7231 servers in this category
  • Published to the registry more recently than 75% of the other 7231 servers in this category
  • Basic checks equal to or better than 100% of the other 7231 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is Sdcreader safe?

It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed –, and the latest version (4.4.1) reached the registry 12 days ago.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.

What can I use instead?

In the same category: Context7, Codebase Memory, Exa

Alternatives

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Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

Context7

by upstash

Feeds AI the latest docs for libraries and frameworks before it answers, so code it writes does not use outdated, removed functions.

Computer or cloud★ 64,224MITUpdated 3 days ago
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Codebase Memory

by DeusData

Codebase knowledge graph for AI agents — 162 languages, sub-ms queries, 99% fewer tokens.

Runs on your computer★ 48,148MITUpdated 20 days ago
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Exa

by exa-labs

Connect AI agents to Exa for web search, content fetching, and multi-step research.

Runs in the cloud★ 5,213MITUpdated 44 days ago
Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

sdcreader

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/ /the catalog record: title, description, project, the artifact list (catalog.json)
GET /api/v1/catalog/dm/ / /a storage pointer, {"download_url": ..., "filename": ..., "storage": "gcs"}, for jsonld, xsd and the rest; the reader follows it
dm- .jsonldthe 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- .xsdthe schema bytes: the Dublin Core header for the model, and for each enumerated value its definition and the code it is defined by (dm- .xsd)
GET /dmlib/dm- .versions.jsonevery 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, and check().
  • `Model`: title, description, root (the governed record's cluster), components by identifier, leaves in document order (each a Leaf with its component, its path of clusters and a slug of that path, unique in the record), codes (the enumerated values per component as Code: value, IRI, definition, the code it is defined by), metadata (the JSON-LD's) and header (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_url and rights_statement derive from it.
  • `Component`: sdc_type, label, description, data_type, constraints, links by 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.

Reading a record (4.3.0)

read_record(source, model) reads a Governed Data Record, the XML instance, and joins each value to the component that defines it. source is the record's bytes, its text or a path; a record of another model is refused (RecordError), as is anything that is not an sdc4:dm- record.

  • `Record`: attributes (instance_id, instance_version, creation_timestamp, current-state, ...), values in document order, and exceptional, the values that carry an Exceptional Value.
  • `RecordValue`: the component, its path of clusters (root first), instances parallel to the path (which occurrence each cluster is among the same cluster's repeats under the same parent; instance_key pairs them with the clusters' identifiers, one key per cluster instance), an occurrence index for a component repeated within its cluster instance, its position, its elements as the record holds them (a quantity's units nested: {"xdquantity-value": "1", "xdquantity-units": {"label": "Trade Units", "xdstring-value": "EA"}}), and its context (act, vtb, vte, tr, modified, latitude, longitude).
  • `ExceptionalValue`: the ISO 21090 null flavor (code, name) and its origin: source when it stands in place of the value, recovery when sdcvalidator's recovery inserted it beside a value that failed its constraint. For recovery, RecordValue.rejected is that value, kept so a person can see it.

Cluster members sit inside adapter elements that are not components of the model; the reader passes through them. It does not type, validate or interpret values: that is the caller's work (a mapper, an exporter, a checker).

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

ToolDoes
find_modelssearch the public catalog by text, or list a public project's models by name
describe_modelone 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_leavesthe leaves in document order with path, type, constraints, code count and identity links, paged
list_codesone component's enumerated values with IRI, definition and code system
check_packagethe reader's integrity check on a package directory
read_recorda Governed Data Record on disk read against its model: attributes, then each value with its component, path, elements, context and Exceptional Value, paged; the model is the one the record names unless a source is given

Each model 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).

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