mnemiq MCP¶
Text-to-SQL you can tune to your database (/NEM-ik/ - the m is silent) - an open-source engine that answers natural-language questions over your own database, built so that every stage between the question and the SQL is a setting you can read, change and measure. Run it on your own environment, on models you choose.
Server type: Self-hosted (open-source, Python via uv) - stdio or HTTP serve
Auth: None built in - runs inside your environment with your database credentials
Install: uv run mnemiq enrich / build / ask / serve --http
Endpoint: http://127.0.0.1:8080 when served over HTTP
License: Open source (github.com/agenticfabriq/mnemiq)
Built by: Agentic Fabriq
Why This Matters for Operators¶
Generic BI tools answer canned questions; an LLM pointed at the raw database hallucinates table names. mnemiq splits the difference honestly: the pipeline - profile and describe the schema, index it for retrieval, generate grounded SQL, verify against the real database - is settings, not internals, and every answer shows the SQL it produced, the tables it read and how it grounded the response. Role-scoped queries (--roles analyst) mean the same engine answers finance questions and sales questions with the right guardrails. No accuracy number is claimed until it has been run on your database - which is exactly the right posture.
Tools & Capabilities¶
| Command | Purpose |
|---|---|
mnemiq enrich |
Profile and describe your schema (~30s on a 4-table demo DB) |
mnemiq build |
Index the schema for retrieval (~2s) |
mnemiq ask "..." |
Natural-language query with the SQL, tables read and grounding shown in the answer |
mnemiq serve --http |
Serve the engine over HTTP at 127.0.0.1:8080 for MCP clients |
roles (--roles analyst) |
Role-scoped query access |
The engine exposes the major pipeline stages as settings - query mode (instant / deep / thinking), grounding sources and role policies are adjustable rather than hidden.
Installation¶
git clone https://github.com/agenticfabriq/mnemiq.git
cd mnemiq
uv run python scripts/seed_demo.py # optional demo database
uv run mnemiq enrich # profile + describe the schema
uv run mnemiq build # index it for retrieval
uv run mnemiq ask "how many customers are there by country?" --roles analyst
uv run mnemiq serve --http # http://127.0.0.1:8080
Configuration¶
Point your MCP client at the served HTTP endpoint (or run over stdio). Database credentials stay in your environment - the engine reads your schema and query results, and the grounding documents (docs/grounding.md) describe how answers are anchored to the database rather than to the model's memory.
Integration with CorpusIQ¶
CorpusIQ is the connector layer that lets AI assistants read the systems a business already runs on - accounting, payments, CRM, email, files and analytics - and answer with the source named. It is read-only by design: it retrieves data and never writes to, or acts in, a connected system, which is what makes it safe to point at live accounts.
This page is part of the CorpusIQ directory, the reference we maintain for the MCP server and agent-skill ecosystem. Run alongside CorpusIQ, a third-party server like this one can be combined with a company's own business data in a single answer.