kirk_score_book_batch

io.github.UlyssesModel/kirk-mcp · Verify this server

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.