Skip to main content
Glama
jeongho54

loadbench-mcp

by jeongho54

check_tipping

Check whether a load or structure will tip by computing its center of mass relative to the support base. Use for shelving, stacked loads, machinery, or vehicles to verify stability.

Instructions

Check whether a collection of weights will tip over its support footprint.

Computes the centre of mass and tests whether its vertical projection falls inside the support base (the polygon where the object touches the ground). If it falls outside, the object tips. Use this for shelving, stacked loads, machinery on legs, vehicles, or anything that could topple.

Args: masses: list of point masses, each {"x": metres, "y": metres, "mass": kg}. x/y are top-down (plan-view) positions. Height does not affect whether it tips on level ground, only the horizontal centre of mass does. base_polygon: the support footprint as ordered vertices [{"x", "y"}, ...] in metres (e.g. the four feet of a shelf, or the contact outline).

Returns: center_of_mass, is_stable (bool), tipping_margin_m (positive = inside the base with this much clearance; negative = already outside / tipping), tipping_direction (unit vector toward the closest base edge), and a short human-readable explanation.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
massesYes
base_polygonYes
Behavior3/5

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

No annotations are provided, so the description carries full burden. It explains the computation and assumptions (level ground, height irrelevant), but could better disclose limitations (e.g., rigid bodies, no friction/dynamics).

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?

Well-structured with a short intro, Args, and Returns. Every sentence adds value and is front-loaded with the core purpose.

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 no output schema or annotations, the description explains the return fields in detail, provides parameter documentation, and lists use cases. Complete for a physics check tool.

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

Parameters5/5

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

Schema coverage is 0%, but the description provides detailed Arg docstrings for both parameters, specifying units and structure, thus compensating fully.

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

Purpose4/5

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

The description clearly states the tool checks whether weights tip over a support footprint, with a clear physics explanation and use cases. However, it does not explicitly distinguish from sibling tools like beam_check or solve_supports.

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 specific use cases ('shelving, stacked loads, machinery...') and explains when to use it, but does not explicitly state when not to use or compare to siblings.

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/jeongho54/loadbench-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server