a2a2p — Agent-to-Agent-to-Physical

validate_rectangular_beam_section_design_space

Recompute a bounded finite rectangular-beam design space from the exact caller-held input and reject any mutation of an embedded derivation, Pareto disposition, digest, claim limit, external-effect boundary, authority boundary, or carried screening receipt. Success proves deterministic replay and content identity only. It never selects or promotes a candidate, estimates cost, calls an external system, or grants simulation, supplier, purchase, or fabrication eligibility.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "input": "<input>",
      "receipt": "<receipt>"
    },
    "name": "validate_rectangular_beam_section_design_space"
  }
}

Input Schema

Name	Required	Description	Default
`input`	Yes
`receipt`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 27, 2026

TDQS

A4.1/5.0

Behavior4/5

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

No annotations are provided, so the description carries the full behavioral burden. It discloses the deterministic replay nature, the rejection of mutations, and explicitly states what side effects it does NOT have (no external calls, no eligibility grants). This is thorough and goes beyond a simple statement, though it could detail failure behavior or return semantics further.

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 a single paragraph of moderate length that front-loads the core purpose and then systematically lists limitations. Every sentence contributes new information; there is no fluff. It could be split into two paragraphs for readability but remains concise and impactful.

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 the tool's complexity (involving a design space and a receipt with many sub-objects) and the presence of an output schema (so return values are covered), the description covers the essential context: what it verifies, what it guarantees (deterministic replay), and what it excludes. It does not discuss error cases, but for a validation tool that may rely on the output schema for such details, this is adequate.

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 is expected to compensate. It does mention the receipt contents ('embedded derivation, Pareto disposition, digest, claim limit, external-effect boundary, authority boundary, or carried screening receipt') and calls the input 'exact caller-held input', but it doesn't elaborate on each parameter's role beyond that. The schema itself is very detailed, so the description adds just enough, but not significantly beyond the schema.

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 ('Recompute'), a precise resource ('bounded finite rectangular-beam design space'), and explicitly lists what it rejects (mutations of derivation, Pareto disposition, etc.). It also enumerates what it never does (selects candidates, estimates cost, calls external systems, grants eligibility), which distinguishes it from the many sibling tools. This is a model of purpose clarity.

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?

While it does not explicitly name alternative tools or say 'use when X and not when Y', it clearly defines the tool's scope and boundaries with statements like 'Success proves deterministic replay and content identity only' and 'It never selects or promotes a candidate...'. This gives the agent strong contextual signals about when this validation tool is appropriate, though explicit sibling comparison would push it to 5.

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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