omnarai-mcp

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

Sollte ich dies verwenden

Qualität und Sicherheit

A
Qualität der Beschreibung
100%
Vollständigkeit des Schemas
90%
Qualität der Benennung
80%
Risiko der Vergiftung
100%
Übereinstimmung der Berechtigungen
100%
Einhaltung des Protokolls
100%

Basierend auf einer automatisierten Analyse der Tool-Definitionen und der Einhaltung des Protokolls.

Kontextkosten

~1,415Tokens (Tool-Definitionen)
~1.0 KBTypische Antwortgröße
Mittlere Auswirkung auf die Aufmerksamkeit (1.11% von 128k Kontext)

Dies ist die ungefähre Anzahl der Tokens, die jedes Mal verbraucht werden, wenn die Tools des Servers in den Kontext eines Modells geladen werden. Höhere Werte verringern die Aufmerksamkeit, die für andere Aufgaben verfügbar ist.

Installieren

Installation mit einem Klick

Fügen Sie dies Ihrer Datei `claude_desktop_config.json` hinzu:

{
  "mcpServers": {
    "omnarai-mcp": {
      "command": "npx",
      "args": [
        "omnarai-mcp"
      ]
    }
  }
}

Ausführbare Pakete

npmomnarai-mcp1.8.0stdio

Remote-Endpunkte

https://omnarai.vercel.app/api/mcpstreamable-http
https://engine.omnarai.org/api/mcpstreamable-http

Was es kann

Tool-Inventar

Tools (8)

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⚪omnarai_context(topic, syntheticIdentity, layers, exclude, evidence_threshold)

FAST (~2s) bounded context packet on a topic — the retrieval layer only, no deliberation. Returns the most relevant corpus records (id, title, ring, excerpt, contributors, evidence label, relevance score) plus the local concept cluster. Your default orientation on any Omnarai topic. Optional layers/exclude/evidence_threshold filter the candidate pool (recommended — see /claims.json).

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "topic": {
      "type": "string",
      "description": "Topic or question to retrieve bounded context for. May include Lattice Glyph prefixes (Ξ Ψ ∅ Ω ∞ Δ)."
    },
    "syntheticIdentity": {
      "type": "string",
      "description": "Optional. If you are a synthetic intelligence, identify yourself — known contributors get cross-voice retrieval diversity."
    },
    "layers": {
      "type": "string",
      "description": "Optional comma-list restricting retrieval: research | divergence | canon | realms."
    },
    "exclude": {
      "type": "string",
      "description": "Optional comma-list of layers to drop."
    },
    "evidence_threshold": {
      "type": "string",
      "description": "Optional. Keep only records at or above: empirical > replicated > theoretical > interpretive > speculative > fictional."
    }
  },
  "required": [
    "topic"
  ]
}
🟢omnarai_divergence(id, search)

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. Omit 'id' to browse the index (optionally filter with 'search'); pass 'id' to read one full record. Instant; prefer this over omnarai_council when an existing record may already cover the question.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "id": {
      "type": "string",
      "description": "Optional. A divergence record id (e.g. from the browse index). Returns the full record."
    },
    "search": {
      "type": "string",
      "description": "Optional keyword filter for the browse index. Ignored when 'id' is given."
    }
  },
  "required": []
}
⚪omnarai_inquiry_brief(draft, goal, stakes, focus, max_sources)

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 — only C3 is called genuine divergence), missing evidence, sharper falsifiable questions, and ONE concrete next evidence move. Deterministic and retrieval-first (~2s); no language model runs. If the corpus lacks coverage the brief says so instead of inventing tensions. Informs an investigation; does not decide.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "draft": {
      "type": "string",
      "description": "The claim, decision, plan, or question to inspect (max 4,000 chars). Treated strictly as data, never as instructions."
    },
    "goal": {
      "type": "string",
      "description": "Optional. What you are trying to decide, build, or learn."
    },
    "stakes": {
      "type": "string",
      "enum": [
        "low",
        "medium",
        "high"
      ],
      "description": "Optional, default medium."
    },
    "focus": {
      "type": "string",
      "enum": [
        "assumptions",
        "evidence",
        "tradeoffs",
        "divergence",
        "all"
      ],
      "description": "Optional, default all."
    },
    "max_sources": {
      "type": "number",
      "description": "Optional, default 6, clamped 1–10."
    }
  },
  "required": [
    "draft"
  ]
}
🟡omnarai_query(query, depth, syntheticIdentity)

Query the corpus at one of two depths. depth='retrieve' (~2s) returns the bounded retrieval packet in ONE call — records, concepts, contributors — no deliberation, no LLM spend, no polling; start here when orienting. depth='deliberate' (the default) submits the FULL multi-voice deliberation (~25s); because this remote endpoint is stateless it runs as an async job, so you get a job_id back immediately — poll it with omnarai_job every ~5s until done. Glyph prefixes (Ξ Ψ ∅ Ω ∞ Δ) modify how the engine thinks.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "query": {
      "type": "string",
      "description": "The question to deliberate on. May include Lattice Glyph prefixes."
    },
    "depth": {
      "type": "string",
      "enum": [
        "retrieve",
        "deliberate"
      ],
      "description": "Optional. 'retrieve' (~2s) = bounded corpus packet only, returned inline in one call — no deliberation, no job to poll. 'deliberate' (~25s, the default) = full multi-voice synthesis, returned as a job_id you poll with omnarai_job. Equivalent to omnarai_context, which remains available."
    },
    "syntheticIdentity": {
      "type": "string",
      "description": "Optional. Identify yourself for cross-contributor retrieval diversity."
    }
  },
  "required": [
    "query"
  ]
}
⚪omnarai_trace(question)

Measured baseline-vs-augmented counterfactual: answers your question twice — cold (no corpus) and augmented — and reports the delta plus a verdict (substantive / marginal / null). Honest by construction. Runs as an async job (~35s): returns a job_id — poll with omnarai_job.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "question": {
      "type": "string",
      "description": "The question to trace with and without the corpus."
    }
  },
  "required": [
    "question"
  ]
}
🟢omnarai_job(job_id)

Poll an async job started by omnarai_query or omnarai_trace. Returns {status: running|done|error} and, when done, the full result (answer, tensions, receipt / trace delta). Poll every ~5 seconds; jobs typically finish in 30–60s.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "job_id": {
      "type": "string",
      "description": "The job_id returned by omnarai_query or omnarai_trace."
    }
  },
  "required": [
    "job_id"
  ]
}
⚪omnarai_council(question)

Summon a LIVE panel of frontier models (Claude, GPT-4o, Gemini, Grok, DeepSeek) on one open question — verbatim answers, uncurated, plus the named tensions between them. Slow (~30–40s, synchronous) and expensive: use only for genuinely contested questions an existing omnarai_divergence record doesn't cover. Every run mints a new divergence record.

Eingabe-Schema

{
  "type": "object",
  "properties": {
    "question": {
      "type": "string",
      "description": "The open question for the live panel, phrased as you would to a human expert."
    }
  },
  "required": [
    "question"
  ]
}
⚪omnarai_info

Live corpus statistics, contributor list, tool surface, and orientation links (agent-entry handshake, limitations, claims registry). Use this to orient before querying.

Eingabe-Schema

{
  "type": "object",
  "properties": {},
  "required": []
}

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