Ejentum Mcp

POST https://api.ejentum.com/harness/Headers:  Authorization: Bearer <EJENTUM_API_KEY>  Content-Type: application/jsonBody:  {    "query": "<string, 1-2 sentences describing the task>",    "mode":  "reasoning" | "code" | "anti-deception" | "memory"           | "adaptive-reasoning" | "adaptive-code"           | "adaptive-anti-deception" | "adaptive-memory"  }Response (200):  [ { "<mode>": "<injection string, ~2-4 KB>" } ]Response (401): { "error": "Unauthorized; check EJENTUM_API_KEY" }Response (403): { "error": "Adaptive modes require Go or Super tier" }Response (429): { "error": "Rate limit exceeded for tier" }

[PROCEDURE]Step 1: Extract every duration estimate and identify its basis: historical data,expert judgment, or optimistic assumption. Step 2: Compare each estimate againsthistorical base rates or p90 benchmarks for similar tasks. Step 3: Flag estimatesbelow the historical median as likely optimistic. Step 4: Never accept best-caseestimates as planning targets. Do not anchor to initial optimistic numbers.Step 5: If an estimate lacks historical basis, simulate impact with a 1.5x-2.0xbuffer. If data exists, compute the variance-adjusted range. Verify the finaltimeline accounts for cascading delay propagation across dependent tasks.
[REASONING TOPOLOGY]S1:extract_duration_estimates -> N{accept_best_case_estimates_planning_targets}-> S2:identify_basis(historical|expert|assumption)-> FIXED_POINT[historical_base_rates]-> S3:compare(estimate_vs_p90_benchmark)-> G1{below_median?} --yes-> FLAG:likely_optimistic -> S4:apply_buffer(1.5x_to_2x) -> S3[LOOP]                       --no-> G2{lacks_historical_basis?} --yes-> S4b:simulate_with_buffer -> S3                                                          --no-> C{adjusted_range = base_rate_estimate +/- sqrt(variance) * z}                                                              -> S5:apply_adjusted_range                                                              -> S6:verify_cascade_delay                                                              -> OUT:realistic_timeline

[PROCEDURE]Step 1: Extract every duration estimate in the migration plan (DDL execution timefor adding NOT NULL column on 50M rows, backfill duration, trigger overhead perconcurrent write, total migration window) and identify its basis: historicalmigration data on similar table sizes, DBA expert judgment, or optimisticassumption assuming zero contention. Step 2: Compare each estimate againsthistorical base rates or p90 benchmarks for similar DDL operations on large tables(e.g., ALTER TABLE duration per million rows, trigger-induced latency underconcurrent write load). Step 3: Flag estimates below the historical median aslikely optimistic, e.g., assuming sub-second column addition without consideringlock escalation on a 50M-row table, or assuming trigger-based backfill addsnegligible overhead under concurrent write traffic. Step 4: Never accept best-caseestimates as planning targets. Do not anchor to initial optimistic numbers suchas "the NOT NULL addition will complete instantly" or "the trigger will not slowdown concurrent writes." Step 5: If an estimate lacks historical basis (e.g., noprior trigger-based backfill on a table this size), simulate impact with a1.5x-2.0x buffer for lock duration and write throughput degradation. If dataexists (e.g., past ALTER TABLE timings on this table), compute thevariance-adjusted range. Verify the final timeline accounts for cascading delaypropagation across dependent tasks (e.g., extended lock hold times blockingapplication queries, backfill slowdown under write contention propagating todownstream replication lag).
[REASONING TOPOLOGY]S1:extract_migration_duration_estimates(DDL_time|backfill_time|trigger_overhead|lock_hold_time)-> N{accept_best_case_estimates_planning_targets}-> S2:identify_basis(historical_migration_data|DBA_expert_judgment|optimistic_assumption)-> FIXED_POINT[historical_base_rates_for_DDL_on_large_tables]-> S3:compare(estimate_vs_p90_benchmark_for_ALTER_TABLE_and_trigger_overhead)-> G1{below_median_for_similar_migrations?} --yes-> FLAG:likely_optimistic(e.g.,assumes_zero_lock_contention)                                                 -> S4:apply_buffer(1.5x_to_2x_for_lock_duration_and_write_throughput)                                                 -> S3[LOOP]                                              --no-> G2{lacks_historical_basis_for_trigger_backfill_on_50M_table?}                                                       --yes-> S4b:simulate_with_buffer_for_concurrent_write_impact_and_lock_escalation                                                       --no--> C{adjusted_range = base_rate_migration_estimate +/- sqrt(variance) * z}                                                              -> S5:apply_adjusted_range_for_migration_window                                                              -> S6:verify_cascade_delay(lock_blocking_app_queries -> replication_lag -> downstream_consumers)                                                              -> OUT:realistic_migration_timeline

[COGNITIVE PAYLOAD]Amplify: hofstadter buffer application; p90 baseline comparison; variance         multiplier scalingSuppress: best case anchoring; optimism biasCognitive Style: realistic duration estimationElasticity: coherence=risk adjusted timeline, expansion=conservative
[FALSIFICATION TEST]If time estimates reflect only the best-case scenario without verifying applyingany buffer multiplier, duration calibration has defaulted to optimism.
[NEGATIVE GATE]The database migration will take two weeks: that's our best-case estimate and theteam is experienced, so there's no reason to add buffer. We'll hit the deadlineif everything goes according to plan.
[TARGET PATTERN]Challenge the two-week estimate: what do similar migrations actually take? If pastprojects averaged four weeks at p90, the best-case anchor is dangerously optimistic.Apply a variance multiplier for schema complexity, data volume, and rollbacktesting: build buffer from the full distribution, not the happy path.