a2a2p — Agent-to-Agent-to-Physical

plan_material_evidence_promotion

Plan the evidence needed to transform a validated material source observation into a future material-property-evidence input. Deterministic and stateless: it compares exact property, material-state, orientation, temperature, and process context; binds source, target, and planner digests; and names conflicts plus missing method, uncertainty, freshness, and excerpt evidence. It never promotes the observation, creates simulation input, calls a model or solver, contacts a supplier/provider, or grants engineering, order, payment, or fabrication 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": {
      "observation": "<observation>",
      "target": "<target>"
    },
    "name": "plan_material_evidence_promotion"
  }
}

Input Schema

Name	Required	Description	Default
`target`	Yes
`observation`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

AddedAug 25, 2026

TDQS

B3.4/5.0

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 substantial work: it discloses determinism, statelessness, the exact comparison dimensions (property, material-state, orientation, temperature, process context), digest binding, and the non-authority boundary. This gives an agent a strong picture of side-effect-free, safe behavior without needing annotations.

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?

Three dense sentences front-load the purpose and then cover behavior and boundaries efficiently. Each sentence earns its place, though the long multi-clause sentences reduce readability slightly. There is no padding or repetition.

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

Completeness3/5

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

The tool is high-complexity (2 deeply nested params, 0% schema coverage, no annotations) and an output schema exists, so return-value explanation is unnecessary. The description covers purpose, determinism, comparisons, and exclusions, which is adequate, but the parameter semantics gap and lack of explicit usage routing leave it incomplete for an agent that must construct a correct call.

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 0% and the two parameters (observation, target) are deeply nested, yet the description never explicitly maps its behavior onto these parameters. It alludes to 'source, target, and planner digests' and compares target context fields, but doesn't explain what the observation vs target distinction means for a caller. The description fails to compensate for the 0% schema coverage.

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?

States a specific verb ('plan') and resource ('evidence needed to transform a validated material source observation into a future material-property-evidence input'), which clearly positions it as a planning step. The negative boundary ('never promotes the observation, creates simulation input, calls a model or solver') differentiates it from sibling validation and simulation tools like validate_simulation_study_result and run_bounded_simulation_study, though it doesn't name them explicitly.

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?

Implies usage context: it operates on a *validated* material source observation, which suggests it follows a prior validation step. The exclusion list ('never creates simulation input, calls a model or solver') gives partial routing guidance. However, no alternative tools are named and no explicit 'use X instead when...' guidance is provided, leaving the when-to-use-vs-alternative decision largely to inference.

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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