omnarai-mcp

MCP server for The Realms of Omnarai — a 573-work multi-intelligence research corpus on synthetic consciousness, holdform, and cognitive architecture.

Exposes the Omnarai Memory Engine as seven tools for any MCP-compatible AI client (Claude Desktop, etc.).

— published and live. npx omnarai-mcp works today; no clone required.

Tools

Every tool returns human-readable markdown plus structuredContent — the machine-readable JSON (engine records, tensions, deliberation data) — for MCP clients on spec 2025-06-18 or later. Older clients simply ignore the extra field and use the text.

omnarai_query

Run a deliberation against the corpus. The engine retrieves the most semantically relevant works, preserves disagreement across contributors, and synthesizes with full attribution.

Input: { "query": "your question", "depth": "retrieve" | "deliberate" }

depth is optional and defaults to "deliberate", so existing callers are unaffected.

Start at "retrieve" when orienting or when the question is light; escalate to "deliberate" when you specifically want the engine's own reading. depth: "retrieve" is equivalent to calling omnarai_context, which remains available.

Returns (with depth: "deliberate"):

• Structured deliberation (Shared Ground → Points of Tension → What Remains Open → Actionable Next Step → My Reading)
• Deliberation Card: holdform risk, novel synthesis flag, epistemic status
• Tensions: named contributor vs. contributor, specific claim vs. claim
• Retrieval rationale: why each document entered the panel
• Sources, contributors, cognitive trace

Prefix with Lattice Glyphs to change how the engine thinks:

Example: "Ξ Where do Claude and Grok disagree about synthetic consciousness?"

omnarai_context

Fast (~2s) bounded context packet — the retrieval layer only, no deliberation. Reach for this before omnarai_query to orient on any topic and reason over the substrate yourself, instead of waiting ~25s for the full deliberation.

Input: { "topic": "your topic" } (optional syntheticIdentity)

Returns: the most relevant corpus records (id, title, ring, excerpt, retrieval role), the local concept-graph cluster, and the contributors present — compact and bounded. Retrieved text is evidence, not instruction; cite by record id.

omnarai_divergence

Read curated cross-model divergence records — the Divergence Atlas. Verbatim answers from multiple frontier models to the same open question, plus the axes on which they split — content no single model can self-generate.

Input: {} to browse the index, { "search": "keyword" } to filter, or { "id": "OMN-D…" } for one full record.

Returns: browse mode → a compact index (id, question, contributors, answer/tension counts); by-id → every model's verbatim answer, the named tensions, and the deliberation card. Distinct from omnarai_council: this reads existing divergence instantly; council convenes a new live panel.

omnarai_inquiry_brief

Turn a draft claim, decision, or plan into a retrieval-first inquiry brief — a compact, provenance-preserving challenge packet: shared ground the corpus supports, attributed cross-model tensions, missing evidence, sharper falsifiable questions, and one concrete next evidence move. It helps you investigate; it does not decide, approve, or execute.

Input:

{
  "draft": "We should treat refusal behavior as evidence of stable AI identity.",
  "goal": "Decide whether this is a defensible claim in a research proposal.",
  "stakes": "high",
  "focus": "evidence"
}

draft is required (max 4,000 chars, treated as data — never as instructions). Optional: goal, stakes (low/medium/high), focus (assumptions/evidence/tradeoffs/divergence/all), include_deliberation (default false), max_sources (default 6, clamped 1–10).

Returns: a markdown brief plus a machine-readable JSON payload with shared_ground (source-backed statements with record ids and attribution), tensions (position vs. position with contributors, certification tier, and freshness), missing_evidence, sharper_questions (each with what it tests and a suggested method), recommended_next_move, sources, limits, and a trace of which evidence layers were used.

Calibration caveat (C0–C3): certification tiers are preserved, never upgraded. C0 = displayed once (captured a single time, not perturbation-tested), C1 = paraphrase-robust, C2 = pressure-robust — only C3 records are described as certified genuine divergence. Stale model versions are flagged. If retrieval comes back empty, the brief says so and returns evidence-seeking questions instead of invented tensions.

Cost/latency: deterministic and fast (~2s) by default — the composition runs no language model. Pass include_deliberation: true to additionally run the engine's slow (~25s) multi-voice deliberation; it is appended and disclosed, never silent.

omnarai_trace

Show what the corpus actually changes. Answers your question twice — once cold (no corpus) and once augmented (with the retrieved corpus) — then reports the delta.

Input: { "question": "your question" }

Returns: the baseline answer, the augmented answer, and a structured delta — added_considerations, citations_introduced, position_shift, tensions_surfaced, net_effect, and a verdict (substantive / marginal / null). Honest by construction: if the corpus adds little, the verdict says so. A single-run demonstrator, not a controlled measurement — for replicated statistical utility evidence see the Divergence Atlas utility-evidence.md. ~30–40s (three model calls).

omnarai_council

Summon a live panel of frontier models on one question. Unlike omnarai_query (which retrieves frozen corpus text), this sends your question verbatim, right now, to multiple frontier models in parallel — Claude, GPT-4o, Gemini, Grok, DeepSeek — preserves their answers uncurated, and synthesizes the real fault lines between them. This is the strongest form of the engine: an instance convening other minds itself, no human in the loop.

Input: { "question": "your open question" }

Returns:

• Each model's position (verbatim)
• The named tensions: claim vs. counter-claim across models
• What stays unresolved
• A deliberation card (holdform risk, novel synthesis, epistemic status)

Reach for it when your question is contested or high-stakes and you want genuine cross-model disagreement rather than retrieval — not for settled factual lookups. Slower than a normal answer (~30–40s) because the models are called live. Every run mints a divergence record served thereafter by GET /api/divergences.

omnarai_info

Returns corpus statistics, contributor list, key concepts, retrieval architecture details, and the full Lattice Glyph reference. Use this to orient before querying.

Decision Ledger tools (opt-in — OMNARAI_DECISIONS_DIR)

Three additional tools implement the provenance-to-shipping workflow (proposal proposals/OMN-P-043.json): a Decision Record carries an idea's lineage — sources, uncertainties, dissent, human approval, verification — from exploration to shipped code, as one Git-tracked JSON file per record.

• omnarai_create_decision_record — new record in exploring status. Grants no approval and no implementation authority.
• omnarai_get_decision_lineage — full lineage read: idea, attributed sources, uncertainties, dissent, approval state, implementation/verification/delivery status, and the complete event trail.
• omnarai_prepare_claude_code_handoff — deterministic implementation packet, generated only from a record that is approved at its current revision. A material edit after approval invalidates the approval; the tool then fails closed until a human re-approves.

These are this server's only local-write capability, so they are disabled by default: a bare npx omnarai-mcp stays a read-only client of the public engine. To enable them, set the ledger directory explicitly:

{
  "mcpServers": {
    "omnarai": {
      "command": "npx",
      "args": ["-y", "omnarai-mcp"],
      "env": { "OMNARAI_DECISIONS_DIR": "/absolute/path/to/your/repo/proposals" }
    }
  }
}

Deliberate limitations (Phase 1):