Kirk — Unsupervised Structural Change Detection MCP Server

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.

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.

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.

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) }

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.

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.

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.

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.

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.

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.

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.

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.

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 sealed engine sha (kirk_version) that will stamp any subsequent kirk_score_* result. Secondary role: a cheap liveness probe for callers wiring up MCP for the first time. 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.