Mcp-Omega-Brain

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omega_cortex_check

Check semantic alignment of a proposed action against a task baseline before executing high-impact operations. Returns approval status, similarity score, and verdict.

Instructions

Read-only alignment gate that measures semantic similarity between a proposed action and the task baseline. Use this to check alignment before high-impact operations without modifying any arguments; use omega_cortex_steer instead if you want automatic argument correction. Returns JSON with fields: approved (boolean), similarity (float 0-1), verdict (APPROVED | BLOCKED).

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "tool": "<tool>",
      "args": "<args>",
      "baseline_prompt": "<baseline_prompt>"
    },
    "name": "omega_cortex_check"
  }
}

Input Schema

Name	Required	Description
`tool`	Yes	Name of the tool to check alignment for, e.g. 'omega_ingest'.
`args`	Yes	The proposed arguments for the tool call, serialized as a JSON object.
`baseline_prompt`	Yes	Task baseline describing the intended operation, e.g. 'Refactoring the auth module for OAuth2 support'.

Schema Changelog

Changes observed during successful MCP inspections.

AddedMay 3, 2026
Removedv2.1.1Apr 14, 2026

Changed3 schema fields changedv1.0.2Apr 14, 2026

changedInput schema / properties / args / description

Previous value: -"The arguments that will be passed to the tool. These are serialized and compared against the baseline for drift detection."New value: +"The proposed arguments for the tool call, serialized as a JSON object."

changedInput schema / properties / baseline_prompt / description

Previous value: -"The declared task baseline describing the intended operation. The Cortex measures semantic distance between this baseline and the tool+args to detect drift."New value: +"Task baseline describing the intended operation, e.g. 'Refactoring the auth module for OAuth2 support'."

changedInput schema / properties / tool / description

Previous value: -"The name of the tool being checked for alignment, e.g. 'omega_ingest' or 'veritas_run_pipeline'."New value: +"Name of the tool to check alignment for, e.g. 'omega_ingest'."

Changed3 schema fields changedv1.0.1Apr 14, 2026

addedInput schema / properties / args / description

Added value: +"The arguments that will be passed to the tool. These are serialized and compared against the baseline for drift detection."

addedInput schema / properties / baseline_prompt / description

Added value: +"The declared task baseline describing the intended operation. The Cortex measures semantic distance between this baseline and the tool+args to detect drift."

addedInput schema / properties / tool / description

Added value: +"The name of the tool being checked for alignment, e.g. 'omega_ingest' or 'veritas_run_pipeline'."

First observedv1.0.0Apr 14, 2026

TDQS

A4.4/5.0

Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries full burden. It clearly states the tool is read-only, does not modify arguments, and returns JSON with fields approved, similarity, and verdict. The description discloses the boolean and float range (0-1) and verdict values. However, it omits any mention of potential error handling, rate limits, or authentication requirements.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is two sentences with no filler. Every sentence adds value: first defines the tool, second gives usage guidance and return format. Front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool has 3 parameters, no output schema, and no annotations, the description provides sufficient context: purpose, usage, return format, read-only nature. It lacks details on error conditions or edge cases, but for a read-only alignment check, this is largely adequate.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the schema already documents all parameters. The description adds marginal value by implying the purpose of baseline_prompt as 'task baseline describing the intended operation', but the schema already provides similar detail. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description explicitly states the tool is a 'read-only alignment gate' that measures semantic similarity between a proposed action and task baseline. It clearly distinguishes from sibling omega_cortex_steer by specifying that this tool does not modify arguments. The verb 'check' and resource 'alignment' provide specific purpose.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description gives explicit usage context: 'Use this to check alignment before high-impact operations' and names the alternative omega_cortex_steer for automatic argument correction. This makes it clear when to use this tool vs. its sibling.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.