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by gchen19
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Python
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material_select

Filters materials by min/max criteria, then ranks survivors by specific strength, stiffness, cost, or density to find suitable candidates for mechanical design.

Instructions

Ashby-style selection: filter the corpus, then rank survivors.

criteria keys are min_/max_ (e.g. min_yield_mpa, max_density_g_cc, min_service_temp_c). rank_by: specific_strength | specific_stiffness | strength | stiffness | cost | density. Returns {rank_by, count, criteria, candidates:[{name, score, yield_mpa, density_g_cc, youngs_gpa, cost_usd_kg}, ...]} best-first; an empty filter returns no candidates rather than the closest miss.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
rank_byNospecific_strength
criteriaNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.1/5.0
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure and does well: it explains the return shape, best-first ordering, and the important empty-filter behavior. It does not state side-effect or concurrency details, but for a read-only selection tool this is reasonable.

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?

The description is dense but purposeful and front-loaded: purpose first, then criteria format, rank_by options, return shape, and one important edge case. Every sentence earns its place.

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 two-parameter tool with no annotations and no output schema, the description provides the return shape, parameter semantics, ranking options, and empty-result behavior. It does not enumerate every valid accessor, but the pattern and examples are sufficient for correct invocation.

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 description coverage is 0%, so the description must compensate, and it does: it explains the criteria key pattern min_<accessor>/max_<accessor>, gives concrete examples, and enumerates rank_by values. This adds substantial meaning beyond the bare schema.

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 a specific verb and resource: material selection by filtering and ranking. It distinguishes the tool as an Ashby-style selector, but it does not explicitly differentiate from sibling tools such as material_list or material_get.

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 criteria format and rank_by options, but there is no explicit guidance on when to choose this tool over material_list or material_get. It also does not state any conditions where this tool should not be used.

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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