KG-SSOT

Name: KG-SSOT
Author: marciocar

by marciocarversion 4.48.0i★ 0iRuns on your computer

Registry nameio.github.marciocar/kg-ssot

Install

Overview

Description from the maker (original English)

Company-brain knowledge graphs: confirmed claims cite sources, and decisions get revisited

PopNx MCP’s three pillars

These three are never merged into one score: opinions, measurements and popularity are different things.

User voiceiNo reviews yetReviews from signed-in users. The average shows once there are reviews
Our basic checksi2 of 2 passedChecked from public information; code not scanned yet
UsageiEarly↓ 1.3K/wk · ★ 0

What you can do with it

We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.

Connection: Runs on your computer
Transport: stdio
Category: Search & AI memory

Install

Pick the AI app you use, then copy the settings.

Before you install

Basic checks: 2 of 2 passed
Runs on your computer with your user’s permissions (files, network, apps).
The registry lists no key to fill in.

You need

PyPIPython’s uv installed (for the uvx command)

Which app do you use?

ClaudeClaude CodeCursorVS CodeChatGPT

Pick your app above. We remember it, so next time the button says “Install in …”.

Download the maker’s .mcpb file

The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.

Run this in your terminal

claude mcp add kg-ssot -- uvx kg-ssot

Open in Cursor

Or copy the settings yourself

Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json

{
  "mcpServers": {
    "kg-ssot": {
      "command": "uvx",
      "args": [
        "kg-ssot"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

Open in VS Code

Or copy the settings yourself

Paste into .vscode/mcp.json in your project

{
  "servers": {
    "kg-ssot": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "kg-ssot"
      ]
    }
  }
}

Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.

This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.

Your choice stays on this device in a cookie; no account needed.

These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.

Which apps can use it

Based on how this server connects and on each app’s own docs, checked Nov 10, 2026.

Run on your computer in

Claude Goose LM Studio Msty Studio Cherry Studio Cursor VS Code + GitHub Copilot Visual Studio Windsurf (Devin Desktop)Zed Kiro Cline Continue JetBrains AI Assistant Claude Code Codex CLI Gemini CLI OpenCode nodecode Warp LibreChat Raycast

All apps, with pros and cons →

What it can do

Runs on your computer; the tool list appears in your AI app after you install it.

Basic checksi

We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 11, 2026

✓ Pass
Maker matches the repo
The registry name was verified with the GitHub account marciocar, which owns the repo
✓ Pass
Still maintained
Code last changed yesterday
… Not checked yet
Has a licence
We have not read this repo from GitHub yet
– Not applicable
Endpoint responds
This one runs on your computer; there is no internet endpoint
– Not applicable
Sign-in with OAuth
Runs on your computer and uses local settings instead
… Not checked yet
No hidden instructions in tool descriptions
Runs on your computer; we can only read tool lists from internet endpoints

Discussion

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User reviews

No reviews yet

Compared with others in Search & AI memoryi

More stars than 0% of the other 5868 servers in this category
Published to the registry more recently than 96% of the other 5868 servers in this category
Basic checks equal to or better than 100% of the other 5868 servers in this category

Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.

Common questions

Is KG-SSOT safe?

It passed 2 of 2 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.

What keys or codes does it need?

The registry does not list a required key, though some servers ask you to sign in on first use.

Does it run on my computer or in the cloud?

It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).

Is it still maintained?

The GitHub repo last changed yesterday, and the latest version (4.48.0) reached the registry yesterday.

Which apps can use it?

Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv), MCPB on your computer.

What can I use instead?

In the same category: Ruflo, Context7, Metabase

Alternatives

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Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi

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Context7

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by upstashWidely used↓ 1.2M/wk★ 64.4KUpdated yesterday

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Metabase

Lets AI clients search, explore, query, and visualize data in a Metabase instance.

by metabaseWidely used★ 50.3KUpdated 6 months ago

Runs in the cloud

Install

README from the maker

Open the README

Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.

KG-SSOT

The trust layer for a company brain: knowledge graphs in plain text where a confirmed claim must cite its source, a decision records what it replaced and what it rests on, and a decision comes back for review when a source it pinned changes.

English | Português (Brasil)

Documentation · Adopt it · The contract · Conformance suite · Changelog · For AI agents

The contract in 40 seconds: a claim without a source is refused, then fixed, queried and gated.

Why

A company brain is usually built by piling documents into RAG or GraphRAG. That keeps the data, but not the decisions: who decided what, on which source, and whether that source still holds. Teams, of people or of agents, pile up conclusions such as "the retry is idempotent" or "we decided on three attempts", and on the way they lose three things: the source, the date it was last verified and what depends on it. When the person who decided leaves, the reasoning leaves too.

KG-SSOT treats this as a format problem, not a discipline problem. Knowledge lives in a .kg.yaml graph, versioned next to the code, and a formal contract decides in CI what fails:

a confirmed or production node without provenance fails (a claim with no recoverable source is declared unverifiable instead);
a source that is only a placeholder (unknown, n/a, tbd) fails;
an edge that points nowhere fails, and so does a node with no edges;
a verification stamp with nothing to check against, a decision with no origin and a production claim backed only by testimony raise warnings.

What it does for a company brain

You need to know...	KG-SSOT gives you
Where a claim came from	A `provenance` on every confirmed claim: the file, the line or the node, and, if you pin it, a hash that `kg_trace.py check` verifies
Why a decision was made, and what it replaced	`kg_query --decision ID`: the whole `SUPERSEDES` line, what supports it and the status of each support
Which decisions to look at again	`kg_query --revisit`: the ones past their review cadence, contested, or whose approved document or pinned source changed
What is on each owner's plate	`kg_query --open --where owner=X --sort attention`
How many open decisions each graph holds, or any count by field	`kg_query --type decision --open --group-by file`
Which nodes are most supported, and by how many distinct sources	`kg_query --edges --where edge_type=SUPPORTS --group-by to.file --group-by to.id --count-distinct from.id`
Which decisions have no internal `SUPPORTS` edge in their own graph	`kg_query --type decision --where degree.in.SUPPORTS=0`; a `SUPPORTS` from another graph is not counted, so complete it with `kg_query --edges --where edge_type=SUPPORTS --where scope=external`
What is past its re-verification date, on any kind of node	`kg_query --due`, the most overdue first
Who cites this node in other repositories, before you delete it	`kg_refs cited-by repo:ID --repo name=folder ...`, with what was and was not read
What an e-mail, chat or wiki event said, and who sent it	An `evidence` node with the CloudEvents 1.0.2 names in `x_event`, pinned to the accepted inbox document (recipe)

Business, technical, compliance and design knowledge can live in the same format, in your repository. It plugs in as a Python package or an MCP server, with a JavaScript validator on npm, and the CI gate is one of its uses, not the point. The pins are checked when kg_query or kg_trace.py runs with --root.

Get started in 60 seconds

pip install kg-ssot

the conformance suite against the reference reader, from the installed package

kg-conformance | tail -2

one valid graph and one that fails

kg-validate valid.kg.yaml broken.kg.yaml

Or straight from the repository, with hash-checked dependencies:

git clone https://github.com/marciocar/kg-ssot && cd kg-ssot

python3 -m venv .venv && .venv/bin/pip install --require-hashes -r tools/requirements.txt .venv/bin/python3 -I tools/kg_validate.py \ spec/conformance/fixtures/latest/form/minimal/valid-minimal.kg.yaml \ spec/conformance/fixtures/latest/form/minimal/bad-node-missing-label.kg.yaml

PASS spec/conformance/fixtures/latest/form/minimal/valid-minimal.kg.yaml

FAIL spec/conformance/fixtures/latest/form/minimal/bad-node-missing-label.kg.yaml MUST form.required.node.label ×1 · 2 files; 1 pass the MUST; 0 with a SHOULD warning (kg-contract-v4.schema.json + kg-contract-v4.should.schema.json)

The tools print in English by default and in Brazilian Portuguese when your locale is pt_BR (or KG_SSOT_LANG=pt-BR). Codes, exit codes and JSON keys never change with the language; only the human text does.

Install

Channel	Command	What you get
PyPI	`pip install kg-ssot`	The commands `kg-validate`, `kg-conformance`, `kg-gate`, `kg-store`, `kg-query`, `kg-diff`, `kg-vocab`, `kg-criterion`, `kg-audit`, `kg-trace`, `kg-refs`, `kg-vector`, `kg-mcp` and `kg-prov-export`, with the contract and the suite inside; `pip install "kg-ssot[vector]"` adds the search by similarity
npm	`npm install kg-ssot`	The JavaScript reader with a precompiled validator, which also runs in React Native and Expo
Claude Desktop	the `.mcpb` file from the latest release	The MCP server as a one-click extension
MCP Registry	`io.github.marciocar/kg-ssot`	The same server for any MCP client
Container	`docker pull ghcr.io/marciocar/kg-ssot`	The MCP server as a service next to a client without Python, such as LibreChat
Your repository	`kg_vendor.py update --tag kg-ssot-v4.45.0`	The release vendored by tag, with a sha256 stamp, and the gate in your CI (manual, chapter 6)

A node, inside

- id: C_RETRY_SAFE

node_type: claim plane: PROD status: confirmed impact: 4 confidence: 0.8 label: The payment retry is idempotent. verified_at: "2026-10-01" verified_against: tests/payment_retry_test.py provenance: source: tests/payment_retry_test.py locator: test_retry_is_idempotent method: "measurement: integration test in CI"

The label is the claim, short. The provenance says where it came from and how: one of five method classes, measurement, reading, judges, derived or testimony. The stamp says when it was verified and against what. Since contract v4.16 these English names are the canonical form; the Portuguese medição, leitura, juízes, derivado and testemunho are still accepted as aliases, with no warning.

Use it from an AI agent

The reference MCP server gives a model the contract and a store: it validates a graph, writes only what passes, reads, lists, queries with every edge resolved to the label on the other end, and shows history. Read-only tools are annotated as such, so a client can skip confirmation for them.

kg-mcp --root ./graphs          # stdio, for Claude Desktop, Claude Code and other MCP clients

kg_schema_help returns the current contract, a minimal passing example and the fixes for the most common codes, so a model can write a passing graph on the first try. llms.txt indexes the documentation for agents, and llms-full.txt has all of it in one file.

The agent skill. skills/kg-ssot/ is an Agent Skills skill that teaches a coding agent to write, fix, validate and query graphs with these tools, in .kg.yaml and .kg.json. In a pre-registered test, an agent with the skill reached the same result with 45% of the output tokens and 54% of the tool calls.

npx skills add marciocar/kg-ssot -s kg-ssot     # Claude Code, Codex and other clients that read Agent Skills

In Claude Code, it also comes as a plugin: /plugin marketplace add marciocar/kg-ssot, then /plugin install kg-ssot@kg-ssot.

How it works

flowchart LR

subgraph product["Product (one tag kg-ssot-vX.Y.Z)"] C["Contract JSON Schema MUST + SHOULD

integrity rules"] S["Conformance suite

512 cases as plain data"] R["Reference reader kg_validate.py"] S -- "every reader passes" --> R C --> R end subgraph adopter["Adopter repository"] V["vendor/kg-ssot

sha256 stamp"] G["kg_gate.py in CI

per-graph ratchet"] K["the team's .kg.yaml"] V --> G K --> G end product -- "kg_vendor.py update --tag" --> V G -- "nothing gets worse: a new graph passes, debt only shrinks" --> PR["PR approved"]

The contract is JSON Schema 2020-12 at two levels: MUST fails, SHOULD warns. The rules a schema cannot express (unique ids, existing edge ends, orphan nodes) are part of it.
The suite is the compatibility proof: each case is a real YAML file with the expected verdict and codes, and a reader passes only if it emits exactly the same codes.
The kit brings the contract to a repository by tag, with a stamp that fails local edits, and runs the per-graph ratchet in CI: what already failed becomes named debt, a new graph has to pass, and the debt only shrinks.

Features


Two-level contract	MUST fails, SHOULD warns, with a ramp: what warns in one version can fail in the next, and the adopter sees it coming
Structured provenance	`source`, `locator` and `method`, with five evidence classes and the source locality (`repo`, `web`, `host`, `pessoa`, `privado`)
Verification stamp	`verified_at` and `verified_against`, with a warning for a stamp with no target and for a verification dated before the fact
Edges between graphs	`external_edges`, with the far end checked against the corpus
Stable codes	Every violation has a code such as `form.required.node.provenance`, and `--where` names the node
Structured verdict	`kg_validate.py --format json` returns the verdict with code, document path, line, message and fix hint, in a versioned format (`spec/verdict.schema.json`) for tools and MCP servers

| Two formats, one model | .kg.yaml to write and review in a diff, and .kg.json for runtime use (an app, a server), read by a JSON parser u

Only the beginning is shown; read the rest in the repo

KG-SSOT

Overview

PopNx MCP’s three pillars

What you can do with it

Install

Before you install

Which apps can use it

Related guides

What it can do

Basic checksi

Maker matches the repo

Still maintained

Has a licence

Endpoint responds

Sign-in with OAuth

No hidden instructions in tool descriptions

Discussion

User reviews

Compared with others in Search & AI memoryi

Common questions

Alternatives

Ruflo

Context7

Metabase

README from the maker

KG-SSOT

Why

What it does for a company brain

Get started in 60 seconds

the conformance suite against the reference reader, from the installed package

one valid graph and one that fails

Install

A node, inside

Use it from an AI agent

How it works

Features