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Krusgraph
Krusgraph turns the tables in a Microsoft Fabric lakehouse, warehouse or SQL database into an explainable graph and an API for GraphQL item. It is a remote MCP server at https://krusgraph.com/mcp: you add it to Claude as a custom connector and sign in with a Microsoft Entra work account, and every call runs with that account's own Fabric permissions. The hosted service supports Claude, the client it has been tested with. Other MCP clients should work when you host Krusgraph yourself (not tested
- Authority
- 45 reach 0 · trust 5 · quality 25 · freshness 15
- Tier
- listed
- llms.txt
- https://krusgraph.com/llms.txt
- llms-full.txt
- https://krusgraph.com/llms-full.txt
- Sections
- 4
- Links
- 11
- Size
- 9417 bytes
- Registered
- 2026-10-22
- Country
- Sweden Hosting inference
- Hosting country
- Sweden Hosting inference
- Country evidence
- No published address evidence collected yet
- Categories
- Data and analytics
- For agents
- Documentation: developer or product docs worth reading, nothing to call
- First seen
- 2026-10-27T14:12:18.623Z
- Last checked
- 2026-10-27T14:12:19.083Z
- Badge
Their llms.txt
The beginning of the file, from our validated copy saved 2026-10-27T14:12:18.627Z. Cached copy · Source on krusgraph.com
# Krusgraph
> Krusgraph turns the tables in a Microsoft Fabric lakehouse, warehouse or SQL database into an explainable graph and an API for GraphQL item. It is a remote MCP server at https://krusgraph.com/mcp: you add it to Claude as a custom connector and sign in with a Microsoft Entra work account, and every call runs with that account's own Fabric permissions. The hosted service supports Claude, the client it has been tested with. Other MCP clients should work when you host Krusgraph yourself (not tested yet).
It reads table and column names, infers keys and relationships from names (declared keys and foreign keys win), and shows the rule behind each one (K0–K5 for keys, R0–R4 for relationships). Ambiguous columns are reported, never guessed. Sources: lakehouses (tested end to end), and, experimental until a live run, warehouses, Fabric SQL databases, Snowflake database items, mirrored databases (Snowflake, Azure SQL, Cosmos DB, SAP), mirrored Azure Databricks catalogs and mirrored catalogs; Snowflake and Databricks schemas can also be planned where they are with the open-source library, CLI or local stdio server. Thirty-five tools: `list_workspaces`, `list_sources`, `list_tables`, `plan_backend` (nothing is deployed; also suggests composite keys), `sample_rows` (a few rows of one table), `validate_plan` (checks every key for uniqueness and NULLs and every relationship for orphans against the rows; returns counts), `create_backend` (creates or updates one API for GraphQL item; never deletes; no dry run, and it does not run `validate_plan`), `describe_backend` (types, fields and relationships of an existing API, read from its definition), `query_graphql` (read-only; mutations are refused), `rayfin_connector`, `ontology_draft` (the plan as a Fabric IQ ontology (preview) draft; nothing deployed); from the same plan without creating anything: `semantic_model_draft` (Power BI model in TMDL, Direct Lake), `join_path` (how two tables connect, with T-SQL), `data_dictionary` (hand-over Markdown), `dbt_schema` (dbt sources with tests), `plan_drift` (plan against an existing API), `analyze_schema` (table roles, central tables, subject areas, cycles) and `suggest_relationships` (missing links checked against the data); creating with a dry run first, all from the same plan: `create_semantic_model`, `create_ontology` (a Fabric IQ ontology item), `create_views` (wide T-SQL views in a schema of their own) and `create_data_agent`, of which only `create_views` can create with the permissions the hosted app has today; `app_starter` and `app_readiness` for an app on a created API; `lineage` for which APIs, semantic models, views and data agents build on a source's tables; from understanding to building: `silver_notebook_draft` and `create_silver_notebook` (a PySpark notebook that writes de-duplicated silver tables with every reference checked and a quarantine table, never over the source), `drift_monitor_draft` and `create_drift_monitor` (a nightly notebook an
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