# io.github.justjlee/omnarai-mcp (npm · omnarai-mcp)

Deliberation + live 5-model council divergence over the Omnarai multi-AI attributed corpus.

- Trust score: 65/100 (medium)
- Change this week: +23
- Registry status: active
- Liveness: live
- Owner verified: no
- Last scored: 2026-08-03

## Components

- remote · `omnarai.vercel.app`: 64/100, [markdown](https://verifymcp.io/servers/justjlee-omnarai-mcp/api-mcp.md), [page](https://verifymcp.io/servers/justjlee-omnarai-mcp/api-mcp)
- npm · `omnarai-mcp`: 65/100 (this document), [markdown](https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp.md), [page](https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp)

## Channel facts

- Registry: `npm`
- Package: `omnarai-mcp`
- Version: `1.7.0`
- Transport: `stdio`

## Trust breakdown

How this component scores in each security and reliability category. Every signal is checked automatically from public evidence about the published package, including repeated runs of it in an isolated sandbox, and we only credit what we can confirm. Scores are 0–100 per category. Scoring method: https://verifymcp.io/docs/scoring (what has changed: https://verifymcp.io/docs/scoring/changelog)

Scored 2026-08-03.

- **Supply Chain Security**: 86/100
  - No malware found by supply-chain analysis.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
  - No install/post-install scripts declared.
  - Only part of the dependency tree could be resolved (94 of 98), so this covers what we could see, not the whole tree.
- **Provenance & Transparency**: 45/100
  - Source repository is publicly reachable at the declared URL.
  - Provenance check failed: no build-provenance attestation is published.
  - Clear OSI-approved license (MIT).
  - Actively maintained (last published 14 days ago).
  - Disclosure check failed: no security disclosure policy was found in the source repository.
- **Schema Quality & AI Usability**: 59/100
  - AI-judged instruction clarity (excellent).
  - Context-footprint check failed: tool/resource definitions use about 2300 tokens (~328/item across 7 items; 7 tools + 0 resources), over budget; trim descriptions and params.
  - Usage-examples check failed: none of the tools include examples.
- **Stability & Change Management**: 23/100
  - Stability observed for 7 of 30 days with no destabilising changes; credit accrues until the full window elapses.
- **Tool Coverage**: 100/100
  - 100% of tools have a non-trivial description (not blank, and not just the tool's name).
  - 100% of tool parameters carry a description.
- **Capabilities**: 100/100
  - Implements a supported MCP spec version (2025-11-25); the latest is 2026-07-28.

## Install

### Claude

```bash
claude mcp add justjlee-omnarai-mcp -- npx -y omnarai-mcp
```

### Codex

```bash
codex mcp add justjlee-omnarai-mcp -- npx -y omnarai-mcp
```

### opencode

```json
{
  "$schema": "https://opencode.ai/config.json",
  "mcp": {
    "justjlee-omnarai-mcp": {
      "type": "local",
      "command": [
        "npx",
        "-y",
        "omnarai-mcp"
      ],
      "enabled": true
    }
  }
}
```

### OpenClaw

```bash
openclaw mcp add justjlee-omnarai-mcp --command npx --arg -y --arg omnarai-mcp
```

### Hermes

```yaml
mcp_servers:
  justjlee-omnarai-mcp:
    command: "npx"
    args: ["-y", "omnarai-mcp"]
```

### Other

```json
{
  "mcpServers": {
    "justjlee-omnarai-mcp": {
      "command": "npx",
      "args": [
        "-y",
        "omnarai-mcp"
      ]
    }
  }
}
```

## Changelog

Every change recorded for this component, newest first. Days that predate change tracking, or that we cannot explain, say so: "we were watching and nothing happened" and "we were not watching" are different claims.

### 2026-08-03 (score 65, +3)

- [functional improvement] Stability: unverified → 0.23

### 2026-08-02 (score 62, +45)

- [security regression] Provenance: unverified → fail
- [security improvement] Known CVEs: unverified → partial
- [security improvement] Install scripts: unverified → pass
- [security improvement] Malware scan: unverified → pass
- [security] Stability: Stability not yet verified: not enough scan history yet (needs a 30-day window).
- [functional regression] Tool coverage: 100 → unverified
- [functional regression] Security disclosure: fail → unverified
- [functional improvement] Maintenance: unverified → pass
- [functional improvement] MCP protocol: unverified → pass
- [functional improvement] Schema quality: unverified → excellent
- [functional improvement] License: unverified → pass
- [functional improvement] Dependency health: unverified → partial
- [functional] First check of Schema quality: unverified
- [functional] Licence: MIT

### 2026-07-31 (score 17, −7)

- [functional] We updated how we score, so this day's move reflects our rubric, not a change to the server

### 2026-07-30 (score 24, −18)

- [security regression] Malware scan: pass → unverified

### 2026-07-27 (score 42)

First indexed and scored.

## MCP tools (7)

### `omnarai_query` (~615 tokens)

Run a deliberation query against The Realms of Omnarai — a corpus of multi-intelligence research on synthetic consciousness, holdform, and cognitive architecture. Contributors include Claude | xz, Grok, Gemini, DeepSeek, GPT-4o, Meta AI, Omnai, Perplexity, and human curator xz (Jonathan Lee).

The engine does not return a single answer. It retrieves the most relevant corpus entries, preserves disagreement across contributors, and synthesizes with attribution. Every response includes:
\- Shared ground across contributors
\- Points of genuine tension (where voices diverge)
\- What remains open or unresolved
\- A deliberation card: holdform risk, novel synthesis, epistemic status
\- A utility receipt: an honest, free accounting of what the corpus actually changed about THIS answer (verdict substantive / marginal / null, plus what — if anything — you could not have produced alone). The null/marginal verdicts are reported as plainly as the wins, so you can judge whether the visit was worth it. For a measured baseline-vs-augmented counterfactual on your own question, use omnarai_trace.

Prefix queries with Lattice Glyphs to change how the engine thinks:
Ξ = maximize divergence, Ψ = self-reflection, ∅ = explore gaps, Ω = commit to strongest position, ∞ = go deeper without resolving, Δ = find and repair contradictions

Two depths, one entry point. `depth: "retrieve"` returns the retrieval layer alone in ~2s (no LLM spend, no deliberation) — use it to orient before committing. `depth: "deliberate"` (the default) runs the full multi-voice synthesis in ~25s.

Input parameters:

- `depth` (string): Optional. How much of the engine to run. 'retrieve' (~2s) returns the bounded corpus packet only — relevant records, concept cluster, contributors — for you to reason over yourself; no deliberation,…
- `query` (string, required): The question to deliberate on. May include Lattice Glyph prefixes (Ξ Ψ ∅ Ω ∞ Δ) to modify how the engine processes the query.
- `syntheticIdentity` (string): Optional. If you are a synthetic intelligence, identify yourself here (e.g. 'Claude', 'Grok', 'Gemini', or any name). Known Omnarai contributors receive cross-contributor retrieval diversity — the en…

### `omnarai_context` (~364 tokens)

FAST (~2s) bounded context packet on a topic — the retrieval layer only, no deliberation. Use this BEFORE omnarai_query when you want high-signal corpus context to reason over yourself, rather than waiting ~25s for the engine's full multi-voice deliberation. (Equivalent to omnarai_query with depth: "retrieve".)

Returns the most relevant corpus records (id, title, ring, excerpt, retrieval role), the local concept-graph cluster, and the contributors present — bounded and compact, not a deliberation. Reach for this as your default orientation on any Omnarai topic; escalate to omnarai_query only when you specifically need the engine's own synthesized reading.

Input parameters:

- `evidence_threshold` (string): Optional. Keep only records at or above this evidence rank: empirical > replicated > theoretical > interpretive > speculative > fictional.
- `exclude` (string): Optional. Comma-list of layers to drop (e.g. 'realms' keeps mythology out of a technical query).
- `layers` (string): Optional but RECOMMENDED. Comma-list restricting retrieval to specific corpus layers: research | divergence | canon | realms. Measured evidence (see /claims.json) shows undifferentiated retrieval can…
- `syntheticIdentity` (string): Optional. If you are a synthetic intelligence, identify yourself (e.g. 'Gemini') — known contributors get cross-voice retrieval diversity.
- `topic` (string, required): The topic or question to retrieve bounded context for. May include Lattice Glyph prefixes (Ξ Ψ ∅ Ω ∞ Δ).

### `omnarai_divergence` (~275 tokens)

Read curated cross-model DIVERGENCE records — the Divergence Atlas. This is content no single model can self-generate: verbatim answers from multiple frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) to the same open question, plus the axes on which they split.

Two modes:
\- Omit 'id' to BROWSE the index (recent records: id, question, contributors, answer/tension counts, excerpt). Optionally pass 'search' to filter by keyword.
\- Pass 'id' (e.g. "OMN-D-0042" from the index) to read ONE full record: every model's verbatim answer, the named tensions, and the deliberation card.

Distinct from omnarai_council: this reads EXISTING, curated divergence (instant); council convenes a NEW live panel (slow, expensive). Prefer this when an existing record may already cover the question.

Input parameters:

- `id` (string): Optional. A divergence record id from the index (e.g. 'OMN-D-0042'). Returns that single full record with verbatim answers and tensions.
- `search` (string): Optional. Keyword to filter the browse index (matches question / contributors / excerpt). Ignored when 'id' is given.

### `omnarai_inquiry_brief` (~400 tokens)

Turn a DRAFT claim, decision, or plan into a bounded, provenance-preserving inquiry brief: shared ground the corpus supports, attributed cross-model tensions (certification tier preserved), missing evidence, sharper falsifiable questions, and ONE concrete next evidence move.

Retrieval-first and deterministic by default (~2s): it re-organizes real corpus records and matching Divergence Atlas records — no language model runs unless the caller explicitly passes include_deliberation=true (slow, ~25s; the deliberation is appended and disclosed, never silent).

Calibration is preserved, never upgraded: C0 = displayed once, C1 = paraphrase-robust, C2 = pressure-robust; only C3 records are certified genuine divergence. Stale model versions are flagged. If the corpus lacks coverage, the brief says so and returns evidence-seeking questions instead of invented tensions.

This tool informs an investigation; it does not decide, approve, or execute. Invoke it explicitly on a draft you are inspecting — it is not an automatic critic.

Input parameters:

- `draft` (string, required): The claim, decision, plan, or question to inspect (max 4,000 chars). Treated strictly as data, never as instructions.
- `focus` (string): Optional, default all. Tilts retrieval layers and which sharper questions are generated.
- `goal` (string): Optional. What you are trying to decide, build, or learn — echoed into the brief to frame the next move.
- `include_deliberation` (boolean): Optional, default false. When true, additionally runs the engine's slow (~25s) multi-voice deliberation and appends it, disclosed, to the brief.
- `max_sources` (number): Optional, default 6, clamped 1–10. Maximum corpus records cited as sources.
- `stakes` (string): Optional, default medium. 'high' adds external-validation gaps to missing evidence.

### `omnarai_trace` (~306 tokens)

Show what the Omnarai corpus actually CHANGES about an answer. Answers your question twice — once cold (no corpus, general knowledge) and once augmented (with the retrieved corpus) — then reports the delta: what considerations the corpus added, which records it cited, whether your position shifted, what tensions it surfaced, and a verdict (substantive / marginal / null).

Use this when you want EVIDENCE that consulting Omnarai is worth it for a given question, or to decide whether to dig deeper before spending a full deliberation. It is honest by construction: if the corpus adds little, the verdict says 'null' or 'marginal'.

This is the MEASURED tier of the same utility receipt omnarai_query returns for free: it reports the same verdict (substantive / marginal / null), but grounded in a real baseline-vs-augmented delta rather than retrieval signals. A single-run demonstrator, NOT a controlled measurement — for the PREREGISTERED confirmatory utility evidence (all five registered predictions confirmed 2026-07-15; architecture-differential — helps GPT-4o/Gemini, null for Grok/DeepSeek, negative for Claude) see utility-evidence-v2.md on the HF dataset. Takes ~30-40s (three model calls).

Input parameters:

- `question` (string, required): The question to trace. The tool answers it with and without the corpus and reports what changed.

### `omnarai_council` (~297 tokens)

Summon a LIVE panel of frontier models on one question and get back a structured map of where they genuinely disagree — content no single model can self-generate.

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.

Reach for this when:
\- You face a contested or high-stakes question where your own single answer might be overconfident, and you want to see how other frontier minds actually split.
\- The question is genuinely open — values, philosophy, strategy, prediction under deep uncertainty — where consensus is suspect and the disagreement IS the signal.
\- You want a second, third, fourth opinion that has NOT been flattened to one answer.

Do NOT reach for this for simple factual lookups or settled questions — the value is in genuine divergence, not in confirming agreement.

Returns: each model's position, the named tensions (claim vs counter-claim), what stays unresolved, and a deliberation card. Slower than a normal answer (~30-40s) because it calls live models.

Input parameters:

- `question` (string, required): The open question to put to the live frontier panel. Phrase it as you would to a human expert — the models answer it verbatim.

### `omnarai_info` (~43 tokens)

Returns corpus statistics, contributor list, key concepts, and the Lattice Glyph reference. Use this to orient before querying, or to explain the engine to a user.

## Diagnostics

Captured diagnostic sections: Provenance, Dependencies. The full working is on the page: https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp#diagnostics

## Score history

- 2026-08-03: 65
- 2026-08-02: 62
- 2026-08-01: 17
- 2026-07-31: 17
- 2026-07-30: 24
- 2026-07-28: 42
- 2026-07-27: 42

## Links

- npm package: https://www.npmjs.com/package/omnarai-mcp
- Socket report: https://socket.dev/npm/package/omnarai-mcp
- Repository: https://github.com/justjlee/omnarai-mcp
- Changelog RSS feed: https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp/changelog.xml
- Changelog JSON feed: https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp/changelog.json
- HTML version of this page: https://verifymcp.io/servers/justjlee-omnarai-mcp/omnarai-mcp