mcp server
Boyce
Deterministic SQL compiler for AI agents. Your agent stops guessing SQL.
Description as published by the maintainer. Source
- version 0.1.1
- slowing
slowing — Most recent push to the repository was 2026-04-28.
Signals
These are separate measurements of different things. They are deliberately not combined into one score, because a popularity number that mixes website traffic with saves and stars cannot be checked or acted on.
| Signal | Value | What it measures | Window | Observed | Source |
|---|---|---|---|---|---|
| GitHub stars | 1 | Number of GitHub accounts that bookmarked this repository since it was created. It is a bookmark count, not installs, not active users and not quality. | cumulative, all time | GitHub | |
| Last commit | 2026-04-28 | Date of the most recent push to any branch. This is the strongest cheap indicator of whether the project is still maintained. | point in time | GitHub | |
| Open issues | 0 | Open issues plus open pull requests, as GitHub counts them together. A high number can mean an active project or an abandoned one. | as of fetch | GitHub | |
| Latest published version | 0.1.1 | Latest version string the maintainer published to the registry. | as of fetch | Model Context Protocol | |
| Registry record last updated | 2026-04-22 | When the registry record was last updated by its maintainer. | point in time | Model Context Protocol | |
| License | MIT | Licence GitHub detected in the repository. Detection can be wrong; the LICENSE file is authoritative. | as of fetch | GitHub | |
| First listed in the MCP Registry | 2026-04-22 | Date this server was first published to the official MCP Registry. Not a usage or quality measure. | point in time | Model Context Protocol | |
| repository status | active | The repository exists on GitHub and is not archived. This says nothing about how recently it was worked on. | as of fetch | GitHub |
Where to get it
Related, by what their authors tagged them
-
Jitsu
— last commit 2026-08-06, shares bigquery, redshift
Manage Jitsu data pipelines: destinations, streams, connections, functions, live events.
-
io.github.alexpota/cloudscope
— last commit 2026-08-04, shares bigquery
Azure + GCP cost management: spending, forecasts, anomalies, budgets, idle resources, and tags.
-
SchemaBrain
— last commit 2026-08-03, shares postgresql, semantic-layer
The trust and intelligence layer between AI agents and your database.
-
Steep
— last commit 2026-06-29, shares semantic-layer
Query metrics, targets, entities, and team data in your Steep workspace via MCP.
-
com.falkordb/QueryWeaver
— last commit 2026-08-06, shares semantic-layer
An MCP server for Text2SQL: transforms natural language into SQL using graph schema understanding.
-
DataCharter
— last commit 2026-08-06, shares duckdb, sql
Local-first, contract-governed data explorer with read-only, PII-masked query tools for AI agents.
-
ai.aliengiraffe/spotdb
— last commit 2026-08-05, shares database, duckdb
Ephemeral data sandbox for AI workflows with guardrails and security
-
io.github.buttonmonkey/mcp-loom
— last commit 2026-07-06, shares duckdb
Stdio MCP proxy: intercepts oversized tool results into a queryable embedded DuckDB.
-
io.github.cbetz/ratebook
— last commit 2026-07-01, shares duckdb
Open MCP server for US electricity tariffs: real rate lookup, bill estimates, and when to charge.
-
io.github.cyanheads/faostat-mcp-server
— last commit 2026-07-30, shares duckdb
UN FAOSTAT global food & agriculture statistics over a local SQLite mirror, via MCP.
These share tags the maintainers applied themselves, such as bigquery, redshift, postgresql, semantic-layer. Common tags like "mcp" or "ai" are ignored for this: agreeing with six hundred other projects is not a similarity.
This is not a recommendation and not a test result. It is a map of what the authors said their work is about.
How the author describes it
Topics the maintainer set on GitHub: ai-agents, bigquery, database, deterministic, developer-tools, duckdb, llm-tools, mcp, mcp-server, mit-license, postgresql, python, redshift, semantic-layer, sql.
Bring your own setup
We take apart real AI setups every week and show what broke, what cost too much, and what the trace actually said. If you run agents on real work, that is where the useful conversation is.
Join ZBS AI Practice Lab