io.github.collapseindex/ci1t-mcp

Check episodes against configurable thresholds and return triggered alerts — no API call, no auth, no credits. Takes an episode array from evaluate or fleet responses. Response: { status: 'ok'|'warn'|'critical', total_alerts, critical, warnings, episodes_checked, thresholds, alerts: [{ episode, type, value, threshold, severity }] }. Alert types: ci_exceeded, ema_exceeded, authority_elevated, ghost_detected, fault.

Compare two windows of episodes to detect drift or degradation — no API call, no auth, no credits. Takes baseline and recent episode arrays from evaluate or fleet_session_round responses. Response: { comparison: { baseline: stats, recent: stats }, delta: { ci_mean, ema_mean, al_mean, ghost_delta, warn_delta, fault_delta }, trend: 'improving'|'stable'|'degrading', degraded: bool, severity_factors: [...] }. Use after multiple evaluate calls to track model health over time.

Convert between probability floats (0.0–1.0) and Q0.16 fixed-point integers (0–65535) — no API call, no auth, no credits. Response: { direction, count, converted: [{ input, q16|float }] }. Use to_q16 before evaluate, from_q16 to make CI outputs human-readable.

Create a new CI-1T API key. Response: { api_key, masked_key, scope, record }. IMPORTANT: Save the returned api_key — it cannot be retrieved again after creation.

Delete an API key by its PocketBase record ID (from list_api_keys). Response: { deleted: true }.

Evaluate prediction stability. Sends scores to the CI-1T engine and returns per-episode stability metrics. Accepts floats (0.0–1.0) or Q0.16 integers (0–65535) — auto-converts. Response: { episodes: [{ ci_out, ci_ema_out, al_out, warn, fault, ghost_confirmed, ghost_suspect_streak, ... }], credits_used, credits_remaining }. CI values are Q0.16 (0–65535; divide by 65535 for %). Classification: ≤0.15=Stable, ≤0.45=Drift, ≤0.70=Flip, >0.70=Collapse. Chain results → visualize (chart), alert_check (threshold alerts), compare_windows (drift detection), or interpret_scores (stats).

Evaluate a fleet of model nodes for prediction stability. Each node provides a score stream. Returns per-node episodes and aggregate fleet stats. Accepts floats (0.0–1.0) or Q0.16 integers (0–65535) — auto-converts per node. Response: { nodes: [{ node_id, episodes: [{ ci_out, ci_ema_out, al_out, warn, fault, ghost_confirmed, ... }] }], fleet_summary, credits_used, credits_remaining }. Chain per-node episodes → visualize, alert_check, or compare_windows. For persistent multi-round fleet monitoring, use fleet_session_create instead.

Create a new persistent fleet monitoring session. Returns a session ID for subsequent rounds. Max 16 nodes. Response: { session_id, node_count, node_names, created_at }. Workflow: fleet_session_create → fleet_session_round (repeat) → fleet_session_state (check) → fleet_session_delete (cleanup).

Delete a fleet session by ID. Response: { deleted: true, session_id }. Call when monitoring is complete to free server resources.

List all active fleet sessions. Response: { sessions: [{ session_id, node_count, round_count, created_at }] }.

Submit a scoring round to an existing fleet session. Each node's scores array is evaluated and the cumulative fleet snapshot is returned. Response: { round, nodes: [{ episodes: [...] }], fleet_summary }. Episodes in the response can be passed to visualize, alert_check, or compare_windows.

Get the current state of a fleet session without submitting new scores. Response: { session_id, round_count, nodes: [{ node_id, node_name, episodes: [...] }], fleet_summary }. Use to inspect accumulated results between rounds.

Generate CI-1T integration boilerplate for a specific framework or language — no API call, no auth, no credits. Response: { framework, use_case, api_base, endpoints, score_format, auth, cost, instruction }. The instruction field tells you how to produce complete, production-ready integration code for the user's stack.

Get billing history (Stripe invoices). Response: { invoices: [{ amount, credits, date, status }], has_more, cursor }. Pass cursor to paginate.

Check CI-1T engine health. Response: { status, version, latency_ms }. Call before evaluate/fleet operations to verify the engine is reachable.

Analyze raw prediction scores with statistical breakdown — no API call, no auth, no credits. Response: { count, mean, std, min, max, breakdown: [{ index, raw, normalized }] }. Accepts floats (0.0–1.0) or Q0.16 integers (0–65535) — auto-detects. For full stability classification (Stable/Drift/Flip/Collapse), pass scores to the evaluate tool instead.

List all API keys for the authenticated user. Response: { keys: [{ id, name, masked_key, scope, enabled, created }] }. Use the record id with delete_api_key to revoke.

Welcome guide for new users. Returns setup instructions, available tools, pricing, and links. No auth required. Call this when someone is new to CI-1T or asks how to get started.

Probe an LLM for prediction instability. Sends the same prompt 3 times and compares responses using the specified similarity method. Two modes: (1) Default — routes through CI-1T backend (costs 1 credit, uses Grok). (2) BYOM (Bring Your Own Model) — provide base_url + model_api_key + model to probe any OpenAI-compatible API directly (no credits, no CI-1T auth needed). Response: { scores: [u16, u16, u16], normalized: [f64, f64, f64], responses: [str, str, str], method, mode }. The returned scores array can be passed directly to evaluate for full stability classification.

Generate an interactive HTML visualization of CI-1T evaluate results — no API call, no auth, no credits. Takes an episode array from evaluate or fleet responses. Returns a file path to a self-contained HTML chart with sidebar KPIs, color-coded CI bars, EMA trend, authority levels, and hover tooltips. Response: { visualization: filepath, episodes, title, instruction }. Open the file in a browser or VS Code Simple Browser.