OMNIA MCP

OMNIA MCP brings JEV and local LAYA into agent workflows through the Model Context Protocol. Give an agent the context, define the questions that matter, and receive decisions it can use: classifications, scored assessments, and probabilities for specific claims.

A single request can assess several dimensions of a document, conversation, software update, or market observation. OMNIA validates the answers, applies your review thresholds, and records the result with its model identity, policy, and evidence references.

Intelligence in context

The intelligence is in evaluating meaning against context and criteria. The answer format makes that assessment usable by software.

For a news item, an agent can assess the event type, its relevance, who made a claim, and whether the supplied source supports it. For a token observation, it can assess whether fields are complete, whether reported conditions match a policy, and which review category applies. Each question addresses a distinct part of the decision.

These are questions you configure through the same evaluation interface. Your application supplies the records and defines the available answers. Exact arithmetic, transaction limits, and mandatory requirements belong in deterministic application checks.

Several assessments can inform one action. An application can combine relevance, source support, and attribution before routing an item to an editor. It can combine field completeness, reported risk, and freshness checks before requesting a deeper market review. OMNIA returns the assessments; the application owns their combination and the action that follows.

Related MCP server: jev

JEV/LAYA, natively

OMNIA uses the providers' native decision types. It preserves selected labels, ordinal score semantics, probabilities, and available confidence fields in a common MCP response.

Choice compares the alternatives you define. Score evaluates an ordered rubric and can return a value between levels. Noul expresses the probability of a specified condition, retaining uncertainty instead of reducing the result to a bare boolean.

Questions in one request share the supplied state and are evaluated independently. For a dependent sequence, the calling agent can pass an earlier result into the next request. Provider selection is explicit; OMNIA does not silently move local context to a hosted service.

The decision path

flowchart LR
    Agent[MCP client or agent] --> Context[Context and questions]
    Context --> Model{JEV / LAYA}
    Model --> Answers[Native typed answers]
    Answers --> Policy[Validation and review policy]
    Policy --> Record[Decision record]
    Record --> Agent

The record includes a decision ID, input fingerprint, provider/model identity, answers, applied policy, review reasons, evidence references, timing, and available usage metadata. Repeated identical requests can reuse the configured cache. Earlier decisions can be retrieved by ID.

accepted means the response passed the configured checks. needs_review preserves answers that fall below a probability or margin threshold. Neither status grants permission to publish, trade, or execute an external action.

Evaluate several dimensions

This example asks three independent questions about one software update. It keeps the maintainer's statement separate from the quoted comment.

Call omnia_capabilities first, then send these arguments to omnia_evaluate:

{
  "request": {
    "state": {
      "source": "GitHub",
      "project": "Atlas SDK",
      "main_author": "maintainer",
      "main_update": "Version 2.4 is available. It adds streaming responses and fixes connection recovery.",
      "quoted_comment": {
        "author": "community_member",
        "text": "This will double every customer's revenue."
      }
    },
    "questions": {
      "event_type": {
        "type": "choice",
        "instructions": "Classify main_update. Keep quoted_comment separate from the maintainer's statement.",
        "criteria": {
          "release": "An available software version with described changes",
          "maintenance": "Routine repository upkeep without a release announcement",
          "other": "Neither category is supported by the main update"
        }
      },
      "relevance": {
        "type": "score",
        "instructions": "Rate the user-facing significance described in main_update, excluding quoted_comment.",
        "criteria": [
          "Routine bookkeeping with no described user-facing change",
          "A minor improvement to existing behavior",
          "A new capability or a described reliability fix"
        ]
      },
      "author_claim": {
        "type": "noul",
        "instructions": "Does main_update itself claim that customer revenue will double? Do not attribute quoted_comment to the maintainer."
      }
    },
    "policy": {
      "min_probability": 0.8,
      "min_margin": 0.1
    }
  }
}

The response contains a separate answer for each question, plus the decision record and review status. The request above is an illustrative input; recorded provider results are in the validation report.

Quickstart

Requires Python 3.11–3.14. Install the released source with uv:

uv tool install "git+https://github.com/Omniaeye/omnia-mcp.git@v0.1.0"
omnia-mcp version
omnia-mcp doctor

To use JEV, set these variables in the environment that launches your MCP server:

OMNIA_DEFAULT_PROVIDER=jev
OMNIA_ALLOWED_PROVIDERS=jev
TYPESAFE_API_KEY=<your TypeSafe API key>

Register the installed executable in your client:

{
  "mcpServers": {
    "omnia": {
      "command": "omnia-mcp",
      "args": ["serve"]
    }
  }
}

Use an absolute executable path when the client cannot find omnia-mcp. The server reads its process environment; it does not automatically load .env files. .env.example lists every setting.

These recipes follow each client's documented configuration format. See compatibility for client checks and supported transport.

Run LAYA locally

Install from a checkout when configuring the local runtime:

git clone https://github.com/Omniaeye/omnia-mcp.git
cd omnia-mcp
git checkout v0.1.0
python -m venv .venv

Configure the provider and checkpoint:

OMNIA_DEFAULT_PROVIDER=laya
OMNIA_ALLOWED_PROVIDERS=laya
OMNIA_LAYA_MODEL=english
OMNIA_LAYA_REVISION=55cf4c4ebb4ebe31b2550e8bdf3bd21b99753851
OMNIA_LAYA_DEVICE=cpu
OMNIA_LAYA_THREADS=2

Point your MCP client at .venv/bin/omnia-mcp or .venv\Scripts\omnia-mcp.exe. This configuration selects the checkpoint used in the recorded local smoke tests. typed-decisions and multilingual are also supported checkpoint-family selections; choose and validate the corresponding revision and input limits for your task.

For offline operation, prepare the exact snapshot and dependencies before setting OMNIA_LAYA_OFFLINE=true. To enable both providers, use OMNIA_ALLOWED_PROVIDERS=jev,laya, configure each, and select one through request.provider or OMNIA_DEFAULT_PROVIDER. See configuration.

MCP interface

Resources: omnia://guide for usage and omnia://schemas for complete contracts. The same schemas are available locally:

omnia-mcp schemas

doctor checks configuration and package presence without loading a model or making a provider call. serve starts the stdio server; running omnia-mcp with no subcommand does the same.

Operating controls

Configure input size, question count, batch size, concurrency, queue capacity, deadlines, retries, and per-minute/per-day evaluation allowances. Responses are validated against the submitted questions, including rubric identity and rounded probability consistency. Failed requests return explicit errors.

The SQLite ledger retains answers, labels, rubric descriptions, evidence references, fingerprints, and model metadata. It does not retain the full input state by default. Cache lifetime and automatic retention are configurable; records can still contain sensitive application data.

Evidence references are carried with a decision, not fetched. Supply the actual source text when a question needs it. Applications own collection, exact rule enforcement, publication, and execution. OMNIA MCP provides typed assessments rather than free-form text generation or autonomous trading.

Validation

The release has recorded real JEV and local LAYA inference checks, stdio integration tests, Docker checks, and Windows/Linux CI across Python 3.11–3.13.

The multilingual JEV acceptance suite covers software updates, quoted authorship, missing fields, and explicit snapshot rules in English, Portuguese, Chinese, and Japanese. Its initial 100 evaluations exposed a rounding-validation defect. After the correction, a fresh run passed 20/20 cases and 48/48 answer checks. The complete controlled suite passed 175 tests.

Read the results and original evidence. These results describe the recorded cases; choose thresholds against reviewed examples from your own workload.

OMNIA software

These are separate repositories. This MCP package connects directly to TypeSafe and the public LAYA runtime; it does not automatically install or run the News and Trading pipelines.

Documentation and development

Architecture · Configuration · Compatibility · Releasing · Security · Contributing

python -m pip install --upgrade pip
python -m pip install -e . --group dev
python -m ruff check .
python -m ruff format --check .
python -m pytest
python -m build
python -m twine check dist/*

The default suite requires no provider credentials. Live inference remains explicit opt-in.

Provider references: JEV state and context · Composing assessments · Evidence assessment · LAYA upstream.

License and attribution

Apache License 2.0. Original OMNIA MCP code is maintained by OMNIA EYE Corporation. JEV is provided through TypeSafe; LAYA is developed by Convai Innovations. Upstream authorship, licenses, service terms, and model terms remain with their respective projects. See NOTICE.