wquestions-mcp 0.1.0

Model any domain in 7 questions — a WQuestions MCP server.

wquestions-mcp

Model any domain in 7 questions.

An MCP server that lets Claude Desktop (or any MCP client) build and query a knowledge model of anything — a spa, a barbershop, a clinic, a bank — using one fixed set of tools. No per-domain schema to write, ever.

The problem

Every domain today gets its own bespoke ontology or database schema: a CRM schema for sales, a clinical model for a clinic, a different one again for a bank or a taxi dispatcher. None of it transfers between domains, and none of it was designed for an LLM to reason over — each new domain means new modeling work before an AI can even start answering questions about it.

WQuestions replaces all of that with a single fixed index: 7 axes that any fact, in any domain, answers. Model a domain by asserting facts on those 7 axes; query it the same way no matter what the domain is.

The 7 axes

Axis	Question	Holds
Q	who	agents
O	what	objects, and reified situations (facts treated as things)
L	where	places
T	when	time points and intervals
N	how much	magnitudes with a unit
K	which / what kind	atemporal categories, types, states
M	how	the predicates that connect Q/O/L/T/N/K to each other — structural, not a value axis

Quickstart

Add this to your Claude Desktop config (claude_desktop_config.json) and restart Claude Desktop:

{
  "mcpServers": {
    "wquestions": {
      "command": "uvx",
      "args": ["wquestions-mcp"]
    }
  }
}

Prefer to run from source? Clone this repo and, from the repo root, pip install -e . into a virtualenv (the engine is bundled — no other package needed). Then point command/args at that venv's wquestions-mcp script (e.g. command: ".../.venv/bin/wquestions-mcp", args: []) instead of uvx.

Then ask Claude: "Load the spa example, then show me the model." See DEMO.md for the full 30-second walkthrough.

Tools

Tool	Does
list_axes	Describe the 7 axes
list_roles	List canonical roles (who/what/where/... connectors), typed by domain and range
add_entity	Create an individual on a value axis (Q, O, L, T, N, K)
define_verb	Register a situation type and its roles — optional, assert_situation auto-registers unknown verbs
assert_situation	Assert a fact: reify a situation and attach its roles
ask	Query by projection — fix some roles, ask for others, optionally as of a point in time
show_model	Dump the current universe: every entity and fact
load_example	Load a prebuilt demo universe (spa) to try queries instantly
reset	Clear the model and start a fresh universe

How it works

The LLM client does the natural-language-to-structure step: it reads "Diego cut Marco's hair at Barber Kings on 2025-06-11" and turns it into role-labeled arguments (agente: diego, paciente: marco, lugar_de: barber_kings). The server never parses English — it takes those roles, validates them against the 7-axis model, and runs ingest and query over the wq engine. Same engine, same 9 tools, whether the domain behind them is a spa or a barbershop.

Further reading

The 7-axis model — why it's fixed, what each axis actually commits to, and how it holds up once you push on it — is worked out in full in WQuestions, the book this project comes from (Spanish). Start with Chapter 8, El espacio multidimensional for the axis model itself, or the table of contents.