qidi-mcp

qidi_bed_level_calibrate

Destructive

Runs QIDI's built-in M4029 bed-leveling routine: heats the bed, probes, and stores results for reliable first-layer adhesion. Uses confirmation and may restart Klipper afterward.

Instructions

Run QIDI's own bed-levelling calibration (M4029) — the routine the printer's touchscreen calls, which heats the bed, probes, and stores the result. This is QIDI's equivalent of bed_mesh_calibrate plus Z-tilt, tuned for this machine. Takes 10+ minutes and normally restarts Klipper at the end. Two-step confirmation.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "qidi_bed_level_calibrate"
  }
}

Input Schema

Name	Required	Description	Default
`confirm`	No
`bed_temp`	No
`confirm_token`	No
`restart_after`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv2.0.1Nov 9, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond the destructive and non-idempotent annotations, it discloses meaningful operational behavior: the bed is heated, probing occurs, the result is stored, the run takes 10+ minutes, Klipper normally restarts afterward, and confirmation is two-step. These details are highly relevant for a long-running destructive calibration.

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?

Three tightly written sentences front-load the action and then add the comparison, duration, restart behavior, and confirmation requirement. Every sentence contributes useful decision context with no wasted wording.

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?

For a destructive, long-running calibration, it covers what the routine does, how long it takes, that Klipper normally restarts, and that confirmation is two-step. Annotations declare destructiveness and an output schema exists, so remaining gaps are mainly parameter-level details rather than missing operational context.

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?

With schema description coverage at 0%, the description must carry parameter meaning. It implicitly links 'heats the bed' to bed_temp, 'restarts Klipper at the end' to restart_after, and 'two-step confirmation' to confirm/confirm_token, but it never names the parameters, states defaults, or explains token acquisition, leaving notable gaps for a four-parameter call.

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?

It names a specific routine (M4029) and describes exactly what it does: heats the bed, probes, and stores the result. It also distinguishes it from sibling tools by calling it QIDI's equivalent of bed_mesh_calibrate plus Z-tilt, tuned for this machine.

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 identifies this as the touchscreen-called QIDI routine and an equivalent of bed_mesh_calibrate plus z_tilt_adjust, which helps an agent choose it over those separate siblings. It does not explicitly state when not to use it or list prerequisites, so routing guidance is clear but not complete.

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

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