Worthune Verified Financial Models

61 verified financial models and a household planning engine: 20 MCP tools, 6 keyless, specs cited.

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What it can do

    What data it sees

    Do you need an account

    No: the server works without sign-in

    61 verified financial models and a household planning engine: 20 MCP tools, 6 keyless, specs cited.

    How to connect

    How to connect Worthune Verified Financial Models to Claude

    The server address to paste into Claude settings:

    https://worthune.com/api/mcp/mcp

    1. Open Claude (claude.ai or the desktop app).
    2. Go to Settings → Connectors.
    3. Click Add custom connector.
    4. Paste the server address copied below into Remote MCP server URL and click Add.
    5. In a chat, click + → Connectors and switch the new connector on.

    Custom connectors are available on Free, Pro, Max, Team and Enterprise plans (Free is limited to one). On Team and Enterprise an organization Owner adds the connector first under Organization settings → Connectors.

    Authorization

    After you click Add, a sign-in window for the service opens. Sign in with your own account and approve access. Claude never sees your password.

    How to connect Worthune Verified Financial Models to ChatGPT

    The server address to paste into ChatGPT settings:

    https://worthune.com/api/mcp/mcp

    1. Open ChatGPT in a browser (chatgpt.com). A Plus, Pro, Business, Enterprise or Edu plan is required.
    2. Turn on developer mode once: Settings → Apps → Advanced settings → Developer mode.
    3. Open Settings → Connectors and click Create.
    4. Fill in the form: Name (anything), Description (one line about what the service does), MCP server URL (copy it below).
    5. Under Authentication choose OAuth if the service requires sign-in, otherwise None. Click Create.
    6. In a new chat open + → Apps/Connectors and enable the connector.

    OpenAI has renamed this section before (Connectors → Apps/Plugins). If the label differs, search settings for "developer mode". On Business/Enterprise workspaces an admin must allow custom connectors first.

    Authorization

    On first use ChatGPT opens the service's sign-in window. Sign in and approve access.

    How to connect Worthune Verified Financial Models to Cursor

    The server address to paste into Cursor settings:

    https://worthune.com/api/mcp/mcp

    Fastest: click Open in Cursor below and confirm the prompt.

    Manually:

    1. In Cursor open Settings → Cursor Settings → MCP and click Add new global MCP server.
    2. Paste the JSON copied below into ~/.cursor/mcp.json (per project: .cursor/mcp.json in the repo root).
    3. Save the file. The server appears in the MCP list; authorize it there if asked.

    Authorization

    If the service needs sign-in, an authorize button appears next to the server in the MCP list.

    Server tool list (20)

    Raw names from tools/list. Only developers need these.

    list_modelsList Worthune's Concordance-tested financial calculation models (an independent second implementation must agree with the engine, per the published spec). Returns model names and spec versions. Call get_model_contract before running a model.
    get_model_contractGet a model's machine-readable contract: required inputs with types and valid domains, cross-field constraints, sentinel-value meanings, and the government constants (with sources) the model uses. Set include_spec to also receive the full specification markdown (exact formulas, assumptions, exclusions, known issues).
    run_modelRun a Concordance-tested calculation model. Inputs must satisfy the contract from get_model_contract (all fields required, values inside their domains — out-of-domain requests are rejected, never clamped). The response echoes inputs, spec version, assumptions, triggered sentinels, and sourced constants; outputs are exactly what the engine computed, untouched. Share the matching interactive calculator with users: https://worthune.com/scenarios/<model> for scenario models.
    verify_claimVerify claims about a model's outputs against the engine before presenting them to a user. Pass the model inputs plus one or more claims ({path, op, value}); each claim comes back verified, violated, or out-of-scope, with the engine's computed value as proof and a decision-record hash tying the verdict to this spec version and these inputs. Out-of-scope means the model does not compute the claimed quantity — this tool never guesses. Use it to check a number you are about to state; use run_model when you want the full output envelope.
    create_householdCreate a persistent household resource from a household-schema document (docs/household-schema-spec.md: members, accounts by tax wrapper, liabilities, income and expense streams). Validation rejects with JSON-path errors — fix the payload in one pass. Returns the household id to use with get/replace/project_household.
    get_householdFetch a stored household document and its metadata by id.
    list_householdsList the organization's stored households — id, label, advisorRef, version, status, when it was last computed — without the documents (get_household returns one in full). Defaults to active households; GET /api/v1/households returns every status — pass status: "all" for the same listing.
    archive_householdArchive a stored household (never deleted: the row stays readable, leaves the active-household meter, and stops accepting writes or projections). The same operation as DELETE /api/v1/households/{id}.
    list_decisionsThe append-only history of coordination strategies run on a stored household, newest first — id, strategy, spec version, household version, record hash, when. Metadata only; get_decision returns one in full. Same as GET /api/v1/households/{id}/decisions.
    get_decisionOne stored decision in full: the decision object as it was produced, the household version it ran against, and its evidence record with the hashed inputs — enough to re-verify it with /api/v1/records/verify or the SDKs' verifyRecord. Decisions stored before their inputs were retained are returned hash-only and say so. Same as GET /api/v1/decisions/{id}.
    get_household_pictureOne household, one picture: the balance sheet by wrapper and owner, income and spending this year, how current the last computation is against the engine's spec (the same drift reasons the weekly scan delivers), the newest decisions, and a data-quality report naming what is missing and what supplying it would unlock. Sums only — no new number of consequence; nothing is stored. Same as GET /api/v1/households/{id}/picture. Pass as_of (YYYY-MM-DD) to pin the date; it is echoed back.
    replace_householdFull-document replace with optimistic concurrency: pass expectedVersion (from get_household) and a stale write loses cleanly with the current version instead of clobbering a concurrent change.
    patch_householdApply a collection-level delta to a stored household without resending the whole document: set scalars (filingStatus, state), upsert entries by id (replace or append), remove entries by id. expectedVersion (from get_household) is REQUIRED — a delta is only meaningful against a version you have read. The merged result is validated in full; a delta can never produce an invalid household.
    project_householdRun the deterministic multi-year projection on a stored household — income, RMDs, federal + covered state tax, expenses, amortization, cash flow, net worth per year — optionally with seeded Monte Carlo (same seed, same result). Assumptions: pass your own, pin a profile by id+version, or omit both for the labeled illustrative default; the response always names its assumptionsSource. Every simplification that fired is listed in projection.assumptionsApplied — show your work.
    list_example_householdsTwelve FICTIONAL households, each built to exercise a different part of the planning engine — accumulation, a Roth conversion window, drawdown on a low-basis portfolio, spousal Social Security coordination, running out of money, negative amortization, and more. Use one as a starting point for try_household_projection, or as a worked shape when helping someone describe their own household. Pass an id to get the full document back. No API key required.
    try_household_projectionRun the deterministic multi-year household projection on a document you pass in — income, RMDs, Roth conversions, federal and covered-state tax, expenses, amortization, the tax-grossed-up deficit draw, and net worth per year — optionally with a seeded Monte Carlo. NO API KEY, and NOTHING IS STORED: no household is created, no id issued, no record kept. The response carries every simplification that fired in projection.assumptionsApplied and an evidence record whose hash reproduces on an identical document, so you can show your work. Use list_example_households for a starting shape. For a household that persists — with an id, bulk import, webhooks and drift detection — see create_household, which takes a key.
    import_householdsMigrate a book (WS3.5): import up to 200 households in one call, with dryRun: true validating everything and writing NOTHING — iterate until the per-row, per-field report is clean, then run for real. Two shapes: households[] of { importKey, label?, household } documents, or template + records — a declarative mapping template (docs/data-migration.md) executed deterministically over raw exported records, so YOU draft the small reviewable template and the engine does the transcription. importKey is the caller's stable id: re-runs are idempotent (already-imported keys skip and report their householdId); imports never overwrite.
    draft_import_mappingWS3.5c: AI drafts the mapping; determinism executes it. Send up to 10 sample records from an export (column → string cell) and get back a DRAFT mapping template for import_households' template + records path. Three deterministic gates run before anything returns: strict JSON, the transcription guard (a numeric const anywhere in the draft is rejected — numbers bind to columns, never to values a model wrote), and the real import dry-run over your sample. Nothing is written. Review the template and the unmappedColumns honesty list, then run import_households yourself with dryRun: true over the full export.
    narrate_decisionThe Verified Narrative: client- or advisor-ready prose for a stored decision (decision_id) or an inline decision object. A model drafts the narrative FROM the decision; a deterministic gate then extracts every number in the draft and verifies each against the decision's own values — correctly-rounded restatements pass, invented numbers reject the draft whole (one retry, then refusal; a narrative is never patched). The response reports how many claims were checked.
    decide_householdRun a Household Coordination Engine strategy on a stored household and get back a ranked decision object with evidence records. Strategies: withdrawal-sequencing (no params); roth-ladder (params.candidates: [{annualAmountUsd, years}]); ss-claiming (params.candidateAges?: {memberId: [ages 62-70]}); asset-location (params.taxRates: {marginalOrdinary, qualifiedAndLtcg} plus params.characteristics or params.illustrativeCharacteristics: true — the illustrative set is labeled not-a-recommendation and is never applied silently); tax-loss-harvesting (params.ordinaryMarginalRatePct, optional longTermRatePct and realizedGains); annual-gifting (params.doneeCount, params.years); pension-election (params.ownerId, startAge, discountRatePct, options — EPVs from the NCHS life table; adults need sex). Alternatives are ranked best-first by a checkable score under a named objective; deltas name their baseline; every projection-backed candidate links its own verifiable record; exclusions are stated in the notes.