Is the sidemantic MCP server actively maintained?

The sidemantic MCP server was last committed 1mo ago and has 3 contributors. Issue health: 92% of issues resolved. AgentRank freshness score reflects real-time maintenance activity.

sidemantic MCP Server

Name: sidequery/sidemantic
Rating: 4.4 (1 reviews)
Author: sidequery

sidequery/sidemantic

Score: 84.4 → 0% Rank #1025 Database

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The universal metrics layer. Compatible with 15+ formats: Cube, MetricFlow, LookML, Omni, BSL, LDM, Cortex, Malloy, OSI, SML, TML, Hex, Rill, Superset

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Overview

sidequery/sidemantic is a Python tool licensed under AGPL-3.0. The universal metrics layer. Compatible with 15+ formats: Cube, MetricFlow, LookML, Omni, BSL, LDM, Cortex, Malloy, OSI, SML, TML, Hex, Rill, Superset Topics: analytics-engineering, duckdb, semantic-layer, sql, ai, analytics, bigquery, clickhouse, data-engineering, postgresql, snowflake, spark, visualization, mcp.

Ranked #1025 out of 24846 indexed tools.

In the top 5% of all indexed tools.

Ecosystem

Python AGPL-3.0

analytics-engineeringduckdbsemantic-layersqlaianalyticsbigqueryclickhousedata-engineeringpostgresqlsnowflakesparkvisualizationmcp

Score Breakdown

Stars 15% 130

130 stars → growing community

Freshness 25% 1mo ago

Last commit 1mo ago → recently updated

Issue Health 25% 92%

11/12 issues closed → responsive maintainer

Contributors 10% 3

3 contributors → small team

Dependents 25% 0

No dependents → no downstream usage

npm Downloads N/A

PyPI Downloads N/A

Forks 12

Description Detailed

License AGPL-3.0

Weights: Freshness 20% · Issue Health 20% · Dependents 22% · Stars 10% · Contributors 8% · How we score →

How to Improve

Dependents medium impact

No downstream dependents detected yet — adoption by other projects is the strongest trust signal

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Markdown

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Details

Stars: 130
Forks: 12
Open Issues: 1
Closed Issues: 11
Contributors: 3
Dependents: —
Language: Python
License: AGPL-3.0
Last Commit: 1mo ago

Matched Queries

topic:mcp

From the README

# Sidemantic

Sidemantic is an open-source semantic runtime. Define governed metrics once—or import the semantic models you already have—and query them consistently from SQL, the CLI, Python, HTTP, PostgreSQL clients, notebooks, BI tools, and AI agents.

- **Bring existing models:** Power BI TMDL/DAX, Cube, dbt MetricFlow, LookML, Hex, Rill, Superset, Omni, BSL, GoodData LDM, Snowflake Cortex, Malloy, Apache Ossie, AtScale SML, and ThoughtSpot TML
- **Or author natively:** concise YAML, semantic SQL DDL, or Python
- **Run on your warehouse:** DuckDB, MotherDuck, PostgreSQL, BigQuery, Snowflake, ClickHouse, Databricks, Spark SQL, and ADBC sources
- **Consume metrics anywhere:** semantic SQL, CLI, Python, HTTP/Arrow, PostgreSQL wire protocol, MCP, notebooks, TypeScript/WASM, and embedded analytics

[Documentation](https://sidemantic.com) | [GitHub](https://github.com/sidequery/sidemantic) | [Docker Hub](https://hub.docker.com/repository/docker/sidequery/sidemantic) | [Discord](https://di

Read full README on GitHub →