Skip to main content
Glama
gchen19

AnkusDrive

by gchen19
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Local

material_get

Look up material properties by name and return SI-unit values such as Young's modulus, density, and yield strength for FEM simulation.

Instructions

Look up a material by name (e.g. 'AL6061-T6'). Returns the full property card as SI quantity strings (YoungsModulus, PoissonRatio, Density, yield_strength, fracture_toughness, thermal_conductivity, cte, rough_cost, refractive_index where applicable, source, basis). The structural keys are FEM-card-compatible, so the result feeds fem_set_material directly. On a miss returns {ok:false, reason} with a did_you_mean suggestion.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden and does so well: it enumerates the returned keys, states the value format (SI quantity strings), and describes the failure mode ({ok:false, reason} plus a did_you_mean suggestion). Missing only things like case-sensitivity or match strictness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three dense sentences, front-loaded with the core lookup action, followed by return contract and error behavior. Every sentence contributes, though the key enumeration is somewhat long.

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 single-parameter lookup with no output schema and no annotations, the description supplies the return shape, value format, error contract, and downstream compatibility. An agent has essentially everything needed to call and interpret it.

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 coverage is 0%, so the description must compensate. It gives one concrete example ('AL6061-T6'), which conveys the expected name format, but says nothing about case sensitivity, aliases, or fuzzy matching behavior for the single required parameter.

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?

States a specific verb ('look up') and resource ('a material by name') with a concrete example value ('AL6061-T6'). An agent can distinguish this from material_list/material_select purely from the description.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Usage is implied through the example and the note that the result feeds fem_set_material directly, which is a useful downstream routing hint. However, it never explicitly states when to choose this over material_list or material_select, nor any preconditions.

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

Deploy Server

Other Tools