a2a2p — Agent-to-Agent-to-Physical

validate_simulation_study_result

Validate a prepared a2a2p simulation-study result. Masked timeout, non-convergence, boundary, unit, provenance, unsupported-model, and solver failures fail closed. Hard constraints gate eligibility; distinct objective values remain visible; Pareto dispositions are canonical and order-independent. It runs no solver, applies no candidate, stores nothing, and grants no physical authority.

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": {
      "study": "<study>",
      "result": "<result>"
    },
    "name": "validate_simulation_study_result"
  }
}

Input Schema

Name	Required	Description	Default
`study`	Yes
`result`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 22, 2026

TDQS

A3.6/5.0

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. It clearly states that the tool fails closed on various failure conditions, does not run a solver, does not apply a candidate, stores nothing, and grants no physical authority. This is rich behavioral disclosure for a validation tool, but it could further clarify what happens on success (e.g., return value semantics) 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.

Conciseness5/5

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

The description is a single, information-dense paragraph. Every sentence adds value: the first states the purpose, the second lists what is masked and the fail-closed gait, the third explains eligibility and Pareto dispositions, and the fourth clarifies what it does not do. No fluff.

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 has an output schema (not shown to the agent), the description need not explain return values. It covers purpose, scope, failure behavior, and exclusions, which is essential. However, given the complexity of the tool and the empty parameter schemas, it does not provide enough guidance on how to construct the 'study' and 'result' objects, leading to a completeness gap.

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?

The outcome of hard constraints prevents autonomous action without agent guidance, and the parameter semantics must be more clear. The disabled list includes the thermal dampers and door seal, which are detailed in the input schema. Provide explicit rationale tying each parameter constraint to outcomes, preventing misinterpretation and ensuring validation is not mistakenly skipped.

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 identifies a specific verb ('validate') and resource ('prepared a2a2p simulation-study result'), distinguishing it from siblings like validate_domain_solutioning_result. However, the phrase 'simulation-study result' is somewhat specific yet not explained for agents unfamiliar with the domain, and the distinction from its sibling is not made explicit.

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 usage in the context of validating results after a simulation study, and mentions what it does not do ('runs no solver, applies no candidate, stores nothing'), which helps understand when not to use it. However, no explicit alternative tools or conditions for when to use this vs. validate_domain_solutioning_result are given, leaving some inference needed.

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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