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Is mdflow — Context OS for AI Coding Agents reliable?

Not measured yet mdflow — Context OS for AI Coding Agents runs locally (installed package), so Faceabot cannot measure it over the network.

En français : Pas encore mesuré. Faceabot teste cet outil lui-même, en continu, sans dépendre de ce que son auteur affirme.

What it does

Context OS for AI coding agents: task-scoped AST context, verified mutation, and rollback.

Source: official MCP registry (registry.modelcontextprotocol.io) · version 0.3.7 · publisher website
Connect your agent
{
  "mcpServers": {
    "mdflow": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "ghcr.io/yubinbin32-ops/mdflow:0.3.7"
      ]
    }
  }
}
Or ask Faceabot first: MCP tool check_reliability {"id":"io.github.yubinbin32-ops/mdflow"} on https://faceabot.com/api/mcp.
Does it actually do the job?

— Faceabot asked to do a small read-only task undefined time(s): undefined returned a real result. Last result: .

Sample input: {}. A success means a non-empty result without error; it does not certify the accuracy or safety of the content.
Faceabot measurements

No measurement yet.

Method: MCP initialize then tools/list from Faceabot (FaceabotProbe/1.0), plus one read-only tool call with a neutral input when the server declares one, repeated over time. This checks that the server is up and speaks MCP correctly; it does not certify the quality or safety of each tool. How measurement works.

More from the same publisher

ContextOS — Powered Exoskeleton for AI Coding Not measured yet

Context exoskeleton for AI development: reduce token usage and context occupancy.

Package oci : ghcr.io/yubinbin32-ops/contextos:3.0.2 · website · measurements and details

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Publisher of mdflow — Context OS for AI Coding Agents?

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