Model Context Protocol
Connect AI assistants to the published glossary through four bounded, citation-aware, read-only tools. Public remote Streamable HTTP; no credentials required.
Set up MCP →Query curated concepts, navigate semantic relationships, and ground AI applications in source-attributed knowledge. One public glossary, five interoperable interfaces.
Start with your application, not an export format.
Connect AI assistants to the published glossary through four bounded, citation-aware, read-only tools. Public remote Streamable HTTP; no credentials required.
Set up MCP →Search concepts, retrieve definitions, traverse relationships, and request compact AI context bundles.
Explore REST →Use canonical concept URIs and machine-readable JSON-LD or Turtle representations.
Explore Linked Data →Query the published RDF graph using familiar semantic-web query patterns.
Explore SPARQL →Inspect concepts, sources, and relationships visually before building a query or integration.
Open Explorer →MCP clients supporting remote Streamable HTTP can discover and invoke the glossary's tools. The endpoint is POST-only; opening it in a browser returns HTTP 405 by design.
Add a remote MCP server in your AI client's settings, select Streamable HTTP, and use no authentication.
https://id.i2idl.org/mcpget_context for learning-engineering.Each tool retrieves only published reference material and declares itself read-only.
{"name":"get_context","arguments":{"id":"learning-engineering","depth":1,"maxRelated":3}}MCP Registry: io.github.blakeplock/i2idl-glossary · Registry version 1.0.0 · Negotiated MCP protocol 2025-06-18
Use the public versioned API to retrieve a bounded search result.
GET /api/v1/concepts?q=learning&limit=10Get a single concept's definition, evidence, neighbors, and provenance.
GET /api/v1/concepts/learning-engineering/context?depth=1&maxRelated=3The canonical graph uses JSON-LD and RDF vocabularies including SKOS, Dublin Core Terms, Schema.org, and PROV-O. Concepts resolve via stable HTTPS identifiers.
| Resource | Example | Use |
|---|---|---|
| Canonical identifier | /concepts/learning-engineering | Persistent concept URI |
| JSON-LD | /concepts/learning-engineering.jsonld | Machine-readable concept graph |
| Turtle | /concepts/learning-engineering.ttl | RDF serialisation |
| Full graph | /glossary.jsonld | Complete publication |
The hosted endpoint supports bounded, read-only SELECT, ASK, CONSTRUCT, and DESCRIBE queries.
https://id.i2idl.org/sparqlPREFIX skos: <http://www.w3.org/2004/02/skos/core#>
SELECT ?concept ?label WHERE {
?concept a skos:Concept ;
skos:prefLabel ?label .
} LIMIT 10Read operations only. Query execution is bounded by the service.
The Knowledge Graph Explorer lets you inspect semantic relationships, compare concepts, study source coverage, and discover scholarship linked to glossary topics. Research discovery results are separate from editorially curated graph assertions.
I2IDL concept records identify published definitions, relationships, citations, and source evidence. AI-generated explanations should distinguish explicit graph assertions from model interpretation. The public MCP service is unauthenticated and read-only; request bounds, upstream timeouts, and edge rate limiting protect service availability.