NVIDIA Auto Ontology's semantic layer and a personal knowledge base as an MCP server for Claude, ChatGPT and other MCP clients. No LLM API key needed.
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Auto Ontology MCP is an MCP server that NVIDIA Auto Ontology's semantic layer and a personal knowledge base as an MCP server for Claude, ChatGPT and other MCP clients. No LLM API key needed. Its tool list has not been published yet over stdio, sse and http, requires no API key, and scores 62/100 on MCPpedia's security, maintenance and efficiency rubric.
Config is the same across clients - only the file and path differ.
{
"mcpServers": {
"auto-ontology": {
"args": [
"--from",
"git+https://github.com/gogoymh/auto-ontology-mcp",
"auto-ontology-mcp"
],
"command": "uvx"
}
}
}Are you the author?
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Auto Ontology MCP (auto-ontology-mcp) brings NVIDIA Auto Ontology's semantic layer to any MCP client, for two jobs:
Run this in your terminal to verify the server starts. Then let us know if it worked - your result helps other developers.
uvx 'sqlite-utils' 2>&1 | head -1 && echo "✓ Server started successfully"
After testing, let us know if it worked:
Five weighted categories - click any category to see the underlying evidence.
No known CVEs.
Checked sqlite-utils against OSV.dev.
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Auto Ontology MCP (auto-ontology-mcp) brings NVIDIA Auto Ontology's semantic layer to any MCP client, for two jobs:
[!TIP] Use it in place of an LLM Wiki. In Karpathy's LLM Wiki pattern the model compiles your sources into markdown pages that you read in Obsidian. Auto Ontology MCP runs the same loop (ingest, query, lint, with an index and a log) on an ontology instead of loose pages: facts go into typed tables you can sum, filter and compare with SQL, ideas become concept pages with typed links, every record, page and link keeps its source and the quote behind it, and
export_wikiwrites it all out as an Obsidian vault with aliases,[[links]]and the graph view. How the two map
Auto Ontology runs its own LLMs (NVIDIA NIM) behind a Next.js, FastAPI and Postgres stack. This project hands every language step (reading, extracting, naming, judging, writing SQL) to the model in your MCP client, so there is no model endpoint to host and no API key to configure. The server keeps the deterministic work: schema extraction, profiling, key inference, validation, storage and read-only execution, in local SQLite files.
Text-to-SQL fails quietly. The query runs, a number comes back, and nothing says it is wrong.
| ❌ A model reading a raw schema | ✅ The same model with auto-ontology-mcp |
|---|---|
Guesses which of billing_city_id and delivery_city_id "the customer's state" means | Gets the join path from reviewed relationships, with the default role and the alternatives spelled out |
Joins customers.customer_id = orders.order_id because the integers happen to overlap | validate_sql rejects the join and names the one the ontology knows |
| Filters `state_n |