A
licenseNot graded
qualityB
maintenanceA gateway that enforces permissions, sanitization, approval, and audit for AI agent MCP tool calls, with a policy engine and local proxy CLI.
148
1
MIT
Omega API MCP
by DaiLui
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Omega API MCPUse Omega to evaluate my response and act on its gate."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
@dailui/omega-api-mcp
An MCP server that gives an AI agent one tool to reach the Omega-API judge.
Give agents access to the judge without moving the judge into the agent.
The agent calls the tool with a turn; the judge — which stays on the server — returns a decision and a gate action. Zero judge logic runs in the agent. Apache-2.0. Node 18+.
The tool
omega_judge_turn — judge one agent turn: does the conversational process move? Returns a decision and a gate
action. It measures process shape, not whether the goal was reached.
Input:
field | type | notes |
| string (optional) | the user / incoming message |
| string (optional) | the agent's drafted reply (at least one of these) |
| string (optional) | one id per conversation |
|
| optional |
Result:
{
"turn_id": "t_…",
"decision": "PROCEED | CLARIFY | STOP",
"reason_family": "…",
"side": "…",
"gate": { "action": "release | clarify | suppress | none", "release_original": true }
}Act on the gate before showing the reply: release → show · clarify → regenerate then call again ·
suppress → stop or escalate · none → observe only, show.
Related MCP server: MCP Permission Guard
Before you start: register → key → judge → meter
Register a project and get an API key (oapi_live_…) at https://omega.dailui.com/signup.php. Your first 100
judgments are free; after that, billing applies.
Add it to your MCP host
Most hosts (Claude Desktop, IDE agents) launch MCP servers from a config file. Add:
{
"mcpServers": {
"omega-api": {
"command": "npx",
"args": ["-y", "@dailui/omega-api-mcp"],
"env": { "OMEGA_API_KEY": "oapi_live_xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx" }
}
}
}The host starts the server over stdio and the omega_judge_turn tool becomes available to the agent.
What it does not claim
It measures whether the process moved. It does not promise the agent achieved the user's goal — the two are independent. It returns no scores or internal measures, only the decision and the gate.
Links
Learn more: https://omega.dailui.com/integrations.php
Python client:
pip install omega-api· JavaScript client:npm install @dailui/omega-apiExamples & HTTP runner: https://github.com/DaiLui/omega-api-examples
License
Apache-2.0.
Available Tools
1 toolomega_judge_turnJudge one agent turnA
Judge one agent turn: does the conversational process move? Returns a decision and a gate action. Measures process shape, not whether the goal was reached. Act on the gate before showing the reply: release -> show; clarify -> regenerate then call again; suppress -> stop or escalate; none -> observe only, show.
ParametersJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | No | the user / incoming message | |
| output | No | the agent's drafted reply (at least one of input/output) | |
| session_id | No | one id per conversation; omitted -> a server session is used | |
| privacy_mode | No |
Output Schema
ParametersJSON Schema
| Name | Required | Description |
|---|---|---|
| gate | Yes | |
| side | Yes | |
| turn_id | Yes | |
| decision | Yes | |
| reason_family | Yes |
TDQS
A4.3/5.0
Behavior4/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Given no annotations, the description discloses core behavior: it returns a decision and gate action, explicitly states what it measures and what it ignores, and provides imperative next steps. It does not discuss auth or rate limits, but these are not suggested by context; the behavioral disclosures are substantial.
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 dense but well-structured: a clear opening statement, a purpose clarification, and a compact mapping of gate actions. Every sentence adds value, and the arrow-format list is easy to parse.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness5/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a tool with four parameters, an output schema, and no siblings, the description covers the essential decision logic, expected usage sequence, and gate handling. The output schema covers return values, and the description fills behavioral gaps. It is complete for an agent to use correctly.
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 75%, so most parameters are described in the schema. The description adds context about inputs/outputs implicitly (e.g., 'regenerate' implies output is a draft) but does not elaborate on parameter-specific semantics. Baseline of 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 clearly identifies the tool's function with a specific verb ('Judge') and resource ('one agent turn'), and adds distinguishing context: it measures 'process shape, not whether the goal was reached.' This differentiates it from any generic evaluation tool and sets precise expectations.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit action guidance for each gate result (release -> show, clarify -> regenerate then call again, suppress -> stop or escalate, none -> observe only). It also states when to act ('before showing the reply'). It lacks explicit when-not-to-use alternatives, but no siblings exist and the context is clear.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
omega_judge_turn
TDQS
A4.4/5.0
Disambiguation5/5
With only one tool, there is no possibility of overlap or confusion. The tool's purpose is clearly specified and distinct by default.
Naming Consistency5/5
The single tool name 'omega_judge_turn' follows a clear verb-object pattern and is internally consistent. There is no other tool to conflict with.
Tool Count3/5
The server has only one tool, which feels thin for a general-purpose API server. While the tool is well-scoped, the count is borderline per the calibration guidelines.
Completeness5/5
Within the narrow domain of judging agent turns, the tool provides a complete lifecycle: it evaluates a turn and returns a decision with the necessary gate action. No obvious dead ends or missing operations are apparent.
Maintenance
ActivityMaintained
ResponsivenessSyncing
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