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a2a2p — Agent-to-Agent-to-Physical

explore_rectangular_beam_sections

Evaluate 2 to 12 caller-declared rectangular beam widths through the canonical bounded height derivation, then return every proposal and the exact non-dominated frontier for minimizing section height and idealized prismatic material volume. Width order is normalized; duplicates fail closed. The operation never invents a width, assigns an aggregate score, selects a candidate, estimates material cost, verifies manufacturing or omitted failure modes, calls an external system, or grants engineering, purchase, or fabrication authority.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
load_nYes
purposeYes
span_mmYes
max_deflection_mmYes
elastic_modulus_gpaYes
allowable_stress_mpaYes
section_width_options_mmYesFinite caller-owned width set. Input order has no decision meaning and is normalized ascending before evaluation.
manufacturing_increment_mmYes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

TDQS

A4/5.0
Behavior5/5

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

With no annotations, the description carries the full burden, and it is exceptionally transparent: it discloses width normalization, duplicate fail-closed behavior, and a detailed list of non-actions such as no candidate selection, no external calls, no manufacturing verification, and no authority granting. This goes well beyond typical descriptions.

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?

The description is densely packed and front-loaded with the core action, and every phrase adds information. The long negative tail is justified because it prevents dangerous misuse, though the single-sentence structure makes it slightly harder to parse.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Despite having an output schema, the tool is complex with 9 required parameters and conditional constraint logic, and the description does not explain how to choose or combine max_deflection_mm, allowable_stress_mpa, or model variants. An agent would struggle to construct a valid request from the description alone.

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

Parameters2/5

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

Schema description coverage is only 11%, so the description must compensate, but it primarily explains the width array behavior and output concepts. It does not clarify the roles of load_n, span_mm, elastic_modulus_gpa, manufacturing_increment_mm, model choices, or the deflection/stress constraint alternatives.

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?

The description states a specific verb ('Evaluate'), a precise resource ('rectangular beam sections'), and a clear output ('every proposal and the exact non-dominated frontier'). It also distinguishes itself from siblings by emphasizing multi-width exploration and explicitly ruling out candidate selection, so an agent can differentiate it from derive_rectangular_beam_section.

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 clearly frames when to use the tool: when evaluating 2 to 12 caller-declared widths and needing the full non-dominated frontier. It also gives an implicit when-not by stating it never selects a candidate, assigns scores, or estimates costs, though it does not name specific alternative tools.

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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TDQS

A3.8/5.0
Disambiguation2/5

Multiple tool clusters have near-identical names and responsibilities: prepare_derived_beam_simulation, prepare_reviewed_beam_simulation, and prepare_simulation_study all produce bounded simulation studies, while the validate_* family has five variants with subtle input differences. The descriptions are detailed, but an agent would frequently need to read an entire paragraph to avoid misselection.

Naming Consistency5/5

All 24 tools follow the same snake_case verb_noun pattern: build_, check_, request_, validate_, prepare_, run_, upload_, etc. There are no camelCase names, no vague single-word tools, and no stylistic outliers.

Tool Count3/5

24 tools is at the heavy end of the calibration range, and a large subset of rectangular-beam preparation/validation tools could be consolidated. The broad physical-request and supplier pipeline justifies some of the count, but the overall surface still feels over-scoped.

Completeness4/5

The core workflows are covered: upload, submit, revise, status, spec review, pricing/estimates, quote-job polling, supplier package/email rendering, and a full bounded simulation loop. Missing cancellation, request listing, and actual supplier send/order actions are real but peripheral gaps rather than workflow-killing dead ends.

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