Orionbelt Semantic Layer MCP server
API-first semantic layer MCP server compiling YAML models into dialect-specific SQL
98 stars
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If you have run it, two minutes of your experience saves the next person an afternoon.
Orionbelt Semantic Layer tools
No tool declarations could be read from the package source. They show once the server is installed.
Public scan report
scanner v0.1.10 · 2026-09-28 · same rubric, same numbers if you re-run it
- Code scan280 source files scanned25/25
- –Live reliabilityno gateway calls yet and no remote to proben/a
- –Tool poisoningtools not inspected (local package is not executed); not countedn/a
- Auth qualitylocal package, no credentials required12/15
- Maintenancelast push 2 days ago15/15
- Maintainer identityregistry namespace matches repository owner; GitHub account older than a year9/10
Install Orionbelt Semantic Layer in Claude Code, Cursor or VS Code
claude mcp add orionbelt-semantic-layer -- uvx orionbelt-semantic-layer
What the publisher says
From the Orionbelt Semantic Layer repository's README, as published. We do not edit it. Read it on GitHub
OrionBelt® Semantic and Context Layer, Rule Engine, and Semantic Sidecar
Define your metrics once in YAML. Let agents and BI tools query them without ever touching your schema.
A semantic sidecar: it rides alongside the systems you already run instead of replacing them.
Ask an LLM to write SQL against a raw star schema and sooner or later it joins two fact tables and hands you a revenue number inflated by a factor of eight. It looks right. Nobody catches it.
OrionBelt is a semantic and context layer with a rule engine, and it runs as a semantic sidecar. You declare dimensions, measures, metrics, and joins in version-controlled YAML. OrionBelt compiles them into dialect-specific SQL through a real AST, and routes multi-fact queries through a Composite Fact Layer planner that blocks the join paths that produce fan traps. Agents and BI tools ask for "Total Revenue" by "Country". They never see a table name.
No BI tool in the middle. No runtime lock-in. Point it at what you already have.
Four ways in
The same model serves every surface you already use. Four of them, and one model behind all four:
Shortened. The full README is on GitHub.
Nothing above is checked by us. What we check is on the safety report.
Orionbelt Semantic Layer: common questions
- Is Orionbelt Semantic Layer MCP server safe?
- Yes, by our scan: it is graded A (94/100). Read the Orionbelt Semantic Layer safety report
- How do I install Orionbelt Semantic Layer?
- It runs on your machine. Copy the Claude Code, Cursor, VS Code or Claude Desktop config from the install section.
- Does Orionbelt Semantic Layer need an API key?
- Not as far as the registry entry and our scan can tell: no credentials are declared or required.
- Is Orionbelt Semantic Layer maintained?
- The last commit was 17 days ago (2026-09-26). The latest release is v0.3.0.
- What can I use instead of Orionbelt Semantic Layer?
- Servers from other publishers that do the same job: Sqlsure MCP server, ZenBrain Memory MCP server and Memory Vault MCP server. Compare all Orionbelt Semantic Layer alternatives.
Alternatives to Orionbelt Semantic Layer
Same job from other publishers: the closest match first, then the best rated.
- SqlsureSemantic SQL inspector: catches double-counting, wrong joins, PII exposure before execution.not reviewedGrowingA
ZenBrain MemorySeven-layer agent memory: episodic, semantic, procedural and core. Local SQLite, no account.not reviewedGrowingA- Memory VaultLocal-first AI memory layer with hybrid search. Postgres + pgvector. Self-hosted, MIT.not reviewedGrowingB
- DBHubToken-efficient database MCP server for PostgreSQL, MySQL, MariaDB, SQL Server, Oracle, SQLitenot reviewedWidely usedA
KeboolaConnect your AI assistants to Keboola and expose your data, transformations, SQL queries, ...not reviewedEstablishedA
