Mellix MCP server
OfficialRelated Servers
Alternatives to Mellix MCP server
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Related Servers
- AlicenseAqualityFmaintenanceLets MCP clients submit a situation and typed questions (yes/no, choice, or scale) to a decision model that returns calibrated probabilities plus an act-or-escalate flag. Enables agents to route, flag, approve, check, or rate cases and hand uncertain ones to a person.3334 npmApache 2.0
- AlicenseAqualityAmaintenanceEnables MCP clients to obtain typed, probabilistic decisions from text or structured state and up to four images—choosing among labeled options, scoring on ordered scales, and estimating whether propositions are true—for routing, triage, moderation, risk scoring, and similar agent workflows.584 npmMIT
- AlicenseNot gradedqualityCmaintenanceEnables MCP clients to ask typed questions about text or JSON and receive structured answers with probabilities, and also exposes a REST endpoint for non-MCP tools.Apache 2.0
- AlicenseNot gradedqualityCmaintenanceEnables MCP hosts to send a context, question, and named choices to Jev System One, returning the selected choice, confidence, and per-choice probabilities.214 npmMIT
- AlicenseAqualityBmaintenanceEnables MCP clients to submit completed speech or typed text to a self-hosted decision engine and receive judgments such as ordinary speech, command, retained thought, uncertainty, or no action, along with session and decision inspection.3MIT
- AlicenseNot gradedqualityAmaintenanceEnables MCP-speaking hosts to ask typed judgment questions — yes/no, choice, and score — and receive calibrated probabilities instead of prose. Runs offline by default against a synthetic provider, with an opt-in live route whose egress is disclosed before any data leaves.66 npmApache 2.0
TDQS
Scored across 3 tools
The three tools serve clearly separate roles: a liveness probe, the core inference call, and model enumeration. There is no plausible way an agent confuses `decide` with either `health` or `list_models`, and the descriptions reinforce the boundaries.
All names are lowercase snake_case, which is consistent. `list_models` follows verb_noun while `health` is a bare noun and `decide` a bare verb, a minor deviation but still readable and idiomatic for a small API surface.
Three tools is on the thin side but each earns its place: the domain is a single inference endpoint plus two supporting utilities. Nothing feels padded, though the surface is minimal enough that any addition would be noticeable.
The core decision lifecycle (check availability, discover models, submit typed questions with thresholds and escalation) is covered, and batching is handled via multiple questions per call. Minor gaps remain: no async/job handling for the 25-second timeout, no way to retrieve past decisions or inspect billing/usage.