kirk_billing_checkout | 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. |
kirk_billing_showread-only | 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 that inline
without a separate call.
Capability class(es): Meta (account state), not a capability of
the scoring engine.
Path fit: MCP only. Enterprise in-process deployments have
their own billing surface (invoiced separately).
Cost: 0 IU. Callable at balance=0 so a customer with zero credit
can still self-serve to top up. |
kirk_billing_usageread-only | 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.
Cost: 0 IU. |
kirk_bulk_howtoread-only | 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_score_book` invocation is simpler; don't route
through the client for a single book.
Capability class(es): Cost-steering / delivery-path tool. Hands
the caller a runner that exercises the same C2 / C5 / C6
capabilities as the MCP scoring tools, but at zero per-call LLM
token cost.
Path fit: The returned client is an HTTP consumer of the same
MCP endpoint. Production integrations run in-process under
sealed-engine attestation — same binary sha as this endpoint.
Contact Kavara for deployment options.
Cost: 0 IU. Free tool. Once running locally, the returned
client bills against the same tools it drives: single-book
calls at 1 IU each, and batch calls at 1 IU per 50 books
(minimum 1 IU per call). A full 500-book batch → 10 IU. No
LLM tokens on top.
Cost comparison (2.7M-book validation rerun via 500-book
batches — ~5400 batches, 54000 IU billed either way):
MCP via Sonnet 5: $1,968 LLM + $540 IU + ~15 days wall clock
MCP via Haiku 4.5: $656 LLM + $540 IU + ~10 days
Python client (this tool): $0 LLM + $540 IU + ~55 min
Return structure:
{
"language": "python",
"filename": "kirk_online_client.py",
"requirements": str,
"usage": str,
"code": str (the client source, ~500 LOC),
"example": str (2-line copy-paste demo)
} |
kirk_demo_tradingread-only | 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.
Also carries a representative canonical book pair so callers
see two concrete scores alongside the distributions.
Use when: You are a first-time caller exploring what Kirk does.
You want a zero-friction "what does the output look like"
experience against real sealed-engine attestation.
Do not use when: You are scoring your own data — use
``kirk_score_book`` or ``kirk_score_book_batch``. This tool's
input is a fixed synthetic representative pair, not a market
feed.
Capability class(es): C2 (variable-universe cross-section entropy
scoring) demonstrated end-to-end against the sealed engine.
Path fit: MCP demonstration surface only.
Cost: 0 IU. Rate-limited 3/hour per IP.
Returns:
Dict with per-regime ``stationary`` and ``stressed`` blocks
(each: ``mean``, ``sd``, ``n``, ``kirk_version``),
``z_separation`` (pooled-sd distance between the two
regime distributions), ``representative_pair`` (canonical
un-jittered ``stationary_score`` / ``stressed_score`` plus
``book_summaries``), ``interpretation_hint``, ``provenance``,
and ``synthetic_representative`` flag. |
kirk_demo_uavread-only | 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 engine sha handles market microstructure and
acoustic spectra with the same primitive.
Use when: You want to see Kirk's cross-domain generalization
without needing your own audio dataset.
Do not use when: You have real feature vectors to score — use
``kirk_infer_legacy`` directly (arg: list of 50 floats). This
tool's inputs are fixed synthetic spectra baked into the demo.
Capability class(es): Demonstrates domain-agnostic mathematical
primitive — the same engine sha handles kirk_score_book (L2)
and kirk_infer_legacy (arbitrary 50-vector).
Path fit: MCP demonstration surface only.
Cost: 0 IU. Rate-limited 3/hour per IP.
Returns:
Dict with per-class ``drone`` / ``bird`` / ``helicopter``
blocks (each: ``mean``, ``sd``, ``n``, ``kirk_version``),
``z_separation`` (dict of drone_vs_bird / drone_vs_helicopter
/ bird_vs_helicopter in pooled-sd units),
``representative_scores`` (the three single-sample scores
from the canonical un-jittered spectra),
``interpretation_hint``, ``provenance``, and
``synthetic_spectral`` flag. |
kirk_infer_legacyread-only | 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 millisecond-scale, and
the LLM tool-call cost accrues per book for agent-driven callers.
For bulk work, call kirk_bulk_howto first.
Capability class(es): C2 (cross-section entropy scoring), legacy
interface.
Path fit: Validation via MCP (this tool). Production integrations
run in-process under sealed-engine attestation — same binary sha as
this endpoint. Contact Kavara for deployment options.
Cost: 1 IU per call. For agent-driven callers, per-call LLM tokens
accrue on top; the response _cost envelope surfaces both. |
kirk_list_modelsread-only | 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 surface.
Capability class(es): C5 (engine sha attested on every response).
Path fit: Validation via MCP (this tool). Production integrations
run in-process under sealed-engine attestation — same binary sha as
this endpoint. Contact Kavara for deployment options.
Cost: 0 IU. Free tool. |
kirk_render_bookread-only | 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: You need an entropy score — this tool is prep-only.
Call kirk_score_book to score.
Capability class(es): Local prep for the C2 (variable-universe
cross-section entropy) workflow. No sealed-engine interaction; no
capability class is exercised beyond the input-shape convention.
Path fit: Validation via MCP (this tool). The same tensor shape is
what production in-process integrations consume under sealed-engine
attestation.
Cost: 0 IU. Free tool. |
kirk_score_bookread-only | 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 interactive scale, or you
are in a per-book loop from an LLM. MCP round-trip is millisecond-scale
and inappropriate for latency-critical work. For >200 books, call
kirk_bulk_howto first — the returned stdlib Python client scores at
zero LLM tokens per iteration.
Capability class(es):
- C2 (variable-universe cross-section entropy scoring — same model
handles any N without retraining).
- C5 (sealed engine sha stamped on every response).
- C6 (bit-exact reproducibility across substrates; validated by the
FY24 252-day reproduction, byte-identical on repeat runs).
Path fit: Validation via MCP (this tool). Production integrations
run in-process under sealed-engine attestation — same binary sha as
this endpoint. Contact Kavara for deployment options. MCP is a
validation and discovery surface, not a latency-critical production
path.
Cost: 1 IU per call. LLM tokens accrue on top for agent-driven callers. |
kirk_score_book_batchread-only | 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_large` before any ledger debit. For sustained bulk work,
call kirk_bulk_howto — the stdlib Python client scores at zero LLM
tokens per iteration.
Capability class(es):
- C2 (variable-universe cross-section entropy — heterogeneous batch
shapes are handled by one model without retraining).
- C5 (sealed engine sha stamped on every response).
- C6 (bit-exact reproducibility across substrates and runs).
Path fit: Validation via MCP (this tool). Production bulk workloads
run in-process under sealed-engine attestation — same binary sha as
this endpoint. Contact Kavara for deployment options. The MCP
round-trip is inappropriate for high-throughput consumption.
Cost: 1 IU per 50 books (minimum 1 IU per call). n≤50 → 1 IU;
n=51..100 → 2 IU; a full 500-book batch → 10 IU. Validation tier —
validation-scale limits. LLM-agent-scoped cap at 500 books; use
kirk_bulk_howto for anything larger. |
kirk_score_l2_bookread-only | 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 depends
on the books before it. State is never carried between calls. Sending the
same books in a different order is a different measurement and will return
different values; scoring N books one-per-call is NOT equivalent to one
call of N books.
Use when: You are validating Kirk on full L2 snapshots. For the price-only
v1 contract use kirk_score_book — the two are different envelopes and are
not interchangeable.
Do not use when: You are looping this tool from an LLM. Call kirk_bulk_howto
for bulk work; its v2 mode wraps this same call.
Cost: 1 IU per call. |
kirk_score_randomread-only | 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_batch. Synthetic bids/asks are not benchmark
input and should not appear in customer-visible results.
Capability class(es): C2 (uses the same variable-universe cross-
section entropy path as kirk_score_book_batch, on synthetic input).
Path fit: Validation via MCP (this tool). Not a production surface.
Cost: 1 IU per invocation. Internally routes through
kirk_score_book_batch — one metered dispatch, no double-metering. |
kirk_verify_engineread-only | 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. For a
model bound to a non-default engine (kirk-market-orderbook-v1) these
differ. Record the per-model value for provenance, not this one.
Secondary role: a cheap liveness probe when wiring up MCP.
Use when: You want to record engine sha in your own provenance log
before capturing scoring output, or you want a cheap liveness check
ahead of a larger validation batch.
Do not use when: You want a scoring result — this returns
identity/liveness only, no entropies.
Capability class(es): C5 (cryptographic attestation of engine identity).
Path fit: Validation via MCP (this tool). Production integrations
run in-process under sealed-engine attestation — same binary sha as
this endpoint. Contact Kavara for deployment options.
Cost: 0 IU. Free tool. For agent-driven callers, the _cost envelope
still reports iu_this_call=0 and the running session totals.
Returns:
Dict with `status`, `engine`, `env`, and `kirk_version` (the
sealed .so sha). A non-2xx response raises; caller sees a
clean MCP tool error. |
protogrid