Kavara

Show Billing Balance

Return the caller's account_id, IU balance, USD equivalent at list, frozen flag, and recent ledger entries. Purpose: Surface the caller's current billing state — what they can spend, whether the account is frozen, and how recent entries landed. Use when: The caller wants to check available credit before committing to a large batch, or you are debugging a "why-was-I-charged" question. Do not use when: You just need per-call cost — the `_cost` envelope on every agent-driven tool result carries

Create Checkout Session

Create a Stripe Checkout Session URL for buying a credit pack (starter / scale / enterprise). Purpose: Hand the caller a self-serve URL to purchase IU credits. Use when: The caller's balance is low, or you want to route to a self-serve top-up flow before a larger validation batch. Do not use when: The caller is on an enterprise in-process deployment — those are invoiced directly, not via Checkout. Capability class(es): Meta (billing). Path fit: MCP only. Cost: 0 IU. Callable at balance=0.

Get Usage Summary

Return the caller's inference consumption over the last N days from the append-only Gate 2 events table. Purpose: Historical usage summary + per-tool breakdown for the caller's account. Use when: You need a usage report for the caller or an admin, or you are reconciling ledger debits against actual inference events. Do not use when: You need real-time cost — the `_cost` envelope on every agent-driven tool result covers that inline. Capability class(es): Meta (metering). Path fit: MCP only.

Get Bulk Scoring Client

Return a self-contained stdlib Python client for scoring at ZERO per-call LLM tokens. Purpose: Hand the caller an HTTP consumer that runs locally so bulk scoring doesn't burn LLM tokens per book. Use when: You need to score more than ~200 books, or `kirk_score_book_batch` returned `batch_too_large`, or the caller is running an autonomous bulk workload that would otherwise pay per-tool-call LLM tokens for every book. Do not use when: You are running a one-off interactive call — a direct `kirk_

Kirk Trading Demo (Free)

Runs a curated demonstration of Kirk on a trading example. Zero arguments. Returns real Kirk output against the same sealed engine that customer callers hit. Free, rate-limited. First-time users: call this to see what Kirk does before signing up. Purpose: Score n=30 jittered L2 snapshots per market regime (stationary vs stressed) through the sealed engine and surface the per-regime score-distribution statistics (mean, sd) plus the z-separation between the two distributions in pooled-sd units. A

Kirk UAV Demo (Free)

Runs a curated demonstration of Kirk on a UAV example. Zero arguments. Returns real Kirk output against the same sealed engine that customer callers hit. Free, rate-limited. First-time users: call this to see what Kirk does before signing up. Purpose: Score n=30 jittered 50-element spectra per acoustic class (drone / bird / helicopter) through the sealed engine and surface per-class score-distribution statistics plus z-separations for the three class pairs. Demonstrates that the same sealed eng

Verify Kirk Engine Identity

Verify sealed engine identity — returns the sha256 of the running scoring binary. Also serves as a liveness probe against the sealed backend. Purpose: Attest which Kirk build is currently serving scoring calls. Response carries the HOST DEFAULT engine sha (kirk_version). That is the legacy default and is NOT necessarily the engine that will stamp a given kirk_score_* result: each model row in kirk_list_models identifies its own engine, and every scoring response restates it under engine.sha. Fo

List Kirk Models

Enumerate the model_ids the sealed engine exposes, with the engine sha stamped in-response. Purpose: Discover the model catalog and record the sealed engine sha alongside your inference results. Use when: You are wiring a client for the first time and need model_id values for kirk_score_book / kirk_score_book_batch calls, or you want a machine-readable catalog with attestation. Do not use when: You need per-model hyperparameter detail — those are intentionally not exposed on the customer surf

Score Legacy Feature Vector

Score a 50-value feature vector against the legacy /v1/infer route on the sealed engine. Purpose: Backwards-compatible scoring surface for callers that were already targeting the legacy path. Use when: You have an existing client wired to /v1/infer and need continued MCP access without refactoring. Do not use when: You are on a fresh integration — prefer kirk_score_book (single-layer, cascade-shaped path). Also do not use in a tight loop against a large corpus: the MCP round-trip is milliseco

Score Single Order Book

Score one L2 order-book snapshot through the sealed single-layer path and return a scalar entropy plus engine attestation. Purpose: Score one snapshot end-to-end through the sealed engine and surface the result plus the engine sha that produced it. Use when: You are validating Kirk on your own data before committing to a production path, or you are scoring a single snapshot inside an interactive workflow (rate-limited at 60 req/min per account). Do not use when: You need throughput above inte

Score L2 Order Books (v2 contract)

Score a sequence of full L2 order books as one chain and return per-book entropies plus engine attestation. Purpose: Score complete books — prices, sizes and order counts — rather than prices alone. This is the v2 book contract; it carries information the price-only contract cannot. STATE POLICY, and it matters: the books are scored IN ORDER as a single fresh chain. The model starts from its locked initial state at the first book and carries state forward across the rest, so a book's value dep

Score Batch of Order Books

Score up to 500 L2 order-book snapshots in one MCP call — returns an entropies list plus engine attestation. Purpose: Batch-score up to 500 snapshots through the sealed engine in a single MCP dispatch. Use when: You are validating batch behaviour, comparing entropy distributions across small book sets, or running interactive experiments up to 500 books at a time. Do not use when: You have more than 500 books, or you are looping this tool from an LLM. Batches >500 raise a structured `batch_too

Render Order Book Tensor

Render an L2 order-book snapshot into the 20×20 complex128 thermometer tensor WITHOUT invoking the sealed engine. Purpose: Local tensor prep and inspection — see what shape the sealed engine will receive without paying for a scoring call. Use when: You want to sanity-check bid/ask level convention against the model's canonical input convention, inspect the non-zero cell pattern for a snapshot, or debug an unexpected entropy value by first confirming the tensor is well-formed. Do not use when:

Score Random Synthetic Books

Synthesize N realistic-geometry L2 book snapshots and score them — convenience wrapper on kirk_score_book_batch. Purpose: Produce a live entropy series with no external data — the fastest way to confirm a new integration is wired end-to-end. Use when: You want a wiring-check, a first-integration walk-through, or a quick reference for the response shape without needing to supply your own market data. Do not use when: You are scoring anything real — feed your own data through kirk_score_book_ba