Most of the traffic evaluating a synthetic-data vendor today is still a person with a browser tab. Some of it is already an agent — a coding assistant scaffolding test fixtures, an AI advisor being graded on whether it gets a planning answer right. We built a discovery and evaluation layer for that second kind of visitor, not as an add-on to the marketing site, but as a first-class surface: a plaintext manifest, a callable MCP server, and a benchmark with a real answer key.
Everything below is free, read-only, and returns structured JSON (or plain text for llms.txt) — no login, no rate-limit surprises, no HTML to parse.
A plaintext discovery file for coding agents (Cursor, Claude Code, etc.) building wealthtech features — points straight at the catalog, the manifest, and per-archetype samples.
View llms.txtOne Streamable HTTP endpoint (JSON-RPC 2.0), 8 tools, no auth, no session state. Read-only by design — it's a discovery layer, not a metered API.
View the MCP endpointThe full archetype and bundle taxonomy in one JSON call, with a sample-household URL wired to every archetype — built for agents that would rather fetch than scrape.
View the manifestEvaluation tasks for AI financial-advisor agents, each with a deterministic, source-grounded answer. Blind mode withholds the answer key.
View the benchmarkPoint Claude, Cursor, or your own agent at a single MCP URL. No API key, no session setup — the server is stateless Streamable HTTP over JSON-RPC 2.0.
list_archetypesAll 71 synthetic household archetypes — id, name, life stage, wealth tier, household type.list_bundlesProduct bundles with use cases and one-time-purchase links.get_sample_householdOne deliberately-insufficient sample household per archetype — proves fidelity, not a usable corpus.describe_household_schemaThe top-level fields of a synthetic household record.get_purchase_linkThe purchase URL for a given bundle.list_reference_parametersThe 2026 U.S. federal financial-planning parameters, optionally filtered by domain.get_reference_parameterA source-verified parameter by key, with its primary-source citation.get_planning_benchmarkThe Planning Benchmark tasks below — pass blind=true to omit answers.// add the server once — Claude, Cursor, or your app { "mcpServers": { "wealthschema": { "url": "https://www.wealthschema.com/api/mcp" } } }
{
"persona": "household_type, state_of_residence, education, ...",
"cash_flow": "gross/net income, expense breakdown, savings_rate_pct, ...",
"assets": "checking, brokerage, retirement, real_estate, ...",
"longitudinal": "monthly[] 96-month trajectory + methodology",
// ...9 more fields, abridged for this page
}14 tasks across 12 categories — net worth, savings rate, RMDs, IRMAA tiers, marginal brackets, estate exemptions, state-level rules. Each task gives a household's facts and a planning question with one deterministic, source-grounded answer. Most require looking up a live 2026 reference parameter and applying it correctly — the same failure mode that makes a plausible-sounding advisor agent wrong in production.
Pass ?withhold_answers=true to fetch tasks without the answer key for blind evaluation — score the model's output yourself against the full benchmark.
{
"question": "What is this household's net worth?",
"household": {
"filing_status": "single",
"total_assets_usd": 850000,
"total_liabilities_usd": 320000
},
"expected": { "value": 530000, "unit": "USD" },
"scoring": { "method": "exact" }
}The full validated corpus is still a one-time purchase, delivered the same way it always has been — no metered API, no subscription for the data itself. What changes here is who can find and evaluate it before that purchase happens. A coding agent scaffolding a wealthtech feature can pull a real sample and the schema without leaving its editor. An AI advisor agent — or whoever is evaluating one — can be scored against a fixed, cited answer key instead of a vibe. Neither requires a human to have read a landing page first.
Browse the full catalog, or point an agent at the manifest and let it do the browsing.