a2a2p — Agent-to-Agent-to-Physical

validate_rectangular_beam_section_derivation

Recompute a bounded rectangular beam-section derivation from the exact declared input and reject any mutation of the candidate, digests, assumptions, claim limits, external-effects boundary, authority boundary, or carried screening receipt. Success proves deterministic replay and content identity only; it does not make the candidate simulation-eligible, physically true, supplier-ready, approved, purchased, or fabrication-authorized.

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_derivation"
  }
}

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 25, 2026

TDQS

B3.3/5.0

Behavior4/5

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

With no annotations provided, the description carries the full burden and does well by disclosing that success only proves deterministic replay and content identity, and explicitly listing what it does not imply (simulation-eligible, physically true, etc.). It also indicates the tool rejects mutation of several fields, clarifying its validation nature. However, it does not state whether the tool has side effects (e.g., modifies state) or its expected return format beyond the output schema.

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 two sentences and front-loads the core action. It is informative without being verbose, though the long list of protected items is somewhat dense. Overall, it is reasonably concise and well-structured.

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

Completeness1/5

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

Given the complexity of the input schema (nested objects, many required fields) and zero schema descriptions, the description is far from complete. It does not explain how to populate the input or receipt objects, what the receipt structure signifies, or how the validation works in practice. An agent would struggle to invoke this tool correctly with only the description.

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

Parameters1/5

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

Schema description coverage is 0%, and the description provides no explanation of the two parameters (input and receipt). It references 'exact declared input' but does not define what that contains or how to construct it. With no schema descriptions and no parameter-level explanations, an agent cannot infer the meaning or formatting of the required fields.

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 precise action (recompute and reject mutations) and identifies the resource (bounded rectangular beam-section derivation). It clearly distinguishes itself from siblings like derive_rectangular_beam_section (which creates the derivation) and validate_simulation_study_result (which validates simulation results) by focusing on replay and content identity.

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?

The description implies it is used to validate an existing derivation, but it does not explicitly state when to use this tool versus alternatives like derive_rectangular_beam_section. It provides context by explaining what success does and does not prove, but lacks explicit when-to-use or when-not-to-use guidance and does not mention sibling 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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