How do I install orionbelt-analytics?

Its own README gives this command: docker run -it --rm -p 8000:8000 ralforion/orionbelt-analytics. Run it where your MCP client can launch the server, and check the README for any settings it asks for.

Is orionbelt-analytics official and is it maintained?

We found nothing naming it as a vendor’s official server. Its repository has 54 GitHub stars and its state in our directory is listed. Its last commit we recorded was on 2026-10-27.

What licence does orionbelt-analytics use?

The repository declares the NOASSERTION licence.

InfratoApp / Resources / MCP servers / orionbelt-analytics

orionbelt-analytics

orionbelt-analytics is an MCP server, listed under databases. It reaches postgresql and snowflake. Its repository has 54 GitHub stars and is licensed under NOASSERTION; we last read it on 2026-10-27.

Analyzes database schemas from PostgreSQL, Snowflake, ClickHouse, and Dremio, generating RDF/OWL ontologies with SQL mappings.

Install

Read from orionbelt-analytics’s own README, close to verbatim -- not written by us, and not guessed.

docker run -it --rm -p 8000:8000 ralforion/orionbelt-analytics

View in orionbelt-analytics’s own README ↗

What it reaches, and what it needs

Reaches

postgresql, snowflake

Credential

○ none found

Maintenance

Stars

State

listed

Licence

NOASSERTION

From the repository

Ontology-based MCP server that analyzes database schemas (PostgreSQL, Snowflake, ClickHouse, Dremio) and generates RDF/OWL ontologies with SQL mappings for fan-trap-free Text-to-SQL.

View on GitHub ↗2026-10-27

What orionbelt-analytics can do

27 tools documented in its README. An AI client calls these by name.

connect_database: Connect to any supported database using `.env` credentials
list_databases: List the databases configured on the server, by name and description
list_schemas: List available schemas in the connected database
reset_cache: Clear cached schema and ontology data for the current session
discover_schema: Analyze schema structure with automatic GraphRAG + ontology generation
get_table_details: Get detailed column, key, and constraint info for a specific table
cleanup_workspace: Delete all workspace files for the current connection and start fresh
generate_ontology: Generate RDF/OWL ontology from schema with SQL mapping annotations
suggest_semantic_names: Detect abbreviations and cryptic names for business-friendly renaming
apply_semantic_names: Apply LLM-suggested semantic names and descriptions to ontology
load_my_ontology: Load a custom `.ttl` ontology file from an import folder
validate_relationship: Check a relationship against the data and record the verdict in the ontology
download_artifact: Download ontology or R2RML mapping as a Turtle file
sample_table_data: Preview table data with row limit and injection protection
execute_sql_query: Execute SQL with OBQC validation, security checks, and fan-trap detection
generate_chart: Generate Plotly charts (bar, line, scatter, heatmap) with MCP-UI rendering
graphrag_search: Semantic search + schema overview (auto-initialized by `discover_schema`)
graphrag_query_context: Get optimized context for SQL generation (85-95% token reduction)
graphrag_find_join_path: Discover join paths between tables via graph traversal
reachable_from: Dimension-capable tables for an anchor grain (many-to-one closure)
measurable_from: Measure-capable tables for an anchor grain (one-to-many closure)
plan_composite_query: Advise a fan-trap-safe Composite Fact Layer (UNION ALL) decomposition
store_ontology_in_rdf: Persist ontology in Oxigraph for SPARQL access
query_sparql: Execute SPARQL queries (SELECT, ASK, CONSTRUCT — auto-detected)
add_rdf_knowledge: Add custom metadata triples to the RDF store
save_semantic_model: Save a semantic model (e.g., OBML YAML) to the workspace
get_semantic_model: Retrieve a semantic model (e.g., OBML YAML) from the workspace

Read from the README; names are kept only if the README contains them.