Santismm Knowledge — Harness Engineering, Agentic AI & Governance

Compare robot platforms on published facts

compare_physical_ai_platforms

Read-onlyIdempotent

Compare 2–5 platforms side by side on published fields only — payload, runtime, weight, speed, IP rating, temperature range, price, autonomy, coordination, control, perception, environments, sectors, evidence stage, access and lifecycle. Every cell says whether the value is published, unpublished or an editorial estimate, who stated it (manufacturer, press) and under what test conditions. There is no ranking or winner: present it as a table of facts and say which values are unknown. Use this when the user is choosing between named platforms.

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": {
      "platforms": "<platforms>"
    },
    "name": "compare_physical_ai_platforms"
  }
}

Input Schema

Name	Required	Description
`locale`	No	Language of the returned body. Default: en.
`criteria`	No	Fields to compare. Omit for all.
`platforms`	Yes	Platform slugs, e.g. ['spot', 'anymal-d'].

Output Schema

Name	Required	Description	Default
`locale`	No
`api_version`	Yes
`model_version`	No
`dataset_version`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

AddedNov 6, 2026

TDQS

A3.9/5.0

Behavior4/5

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

Goes well beyond annotations by explaining the provenance model behind each cell (published / unpublished / editorial estimate), who stated it (manufacturer, press), test conditions, and the explicit absence of ranking or a winner. This is exactly the kind of output-behavior context annotations cannot carry. It does not mention rate limits or how unknown values affect the table beyond 'say which values are unknown'.

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?

Front-loaded with the verb and constraint, then provenance, then the selection cue — a sensible order. The long field enumeration duplicates the criteria enum and costs some length without adding new information.

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?

With an output schema present, return shape needn't be described, yet the description still conveys the provenance and no-ranking policy that an agent must relay to the user. Nothing critical about calling it correctly is missing; only a pointer to sibling comparison tools is absent.

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?

Schema coverage is 100% and all three parameters are documented in-schema, including the locale enum and criteria list, so the baseline is 3. The description's field enumeration restates the criteria enum rather than adding format or defaulting semantics.

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+resource ('Compare 2–5 platforms side by side') and pins the scope to 'published fields only', then enumerates the fields so an agent knows exactly what comes back. It never mentions the sibling compare_models, so it stops short of full sibling differentiation.

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?

'Use this when the user is choosing between named platforms' gives a clear triggering context, and the 2–5 platform bound plus 'published fields only' implicitly excludes broad discovery queries. It does not point to alternatives (e.g. compare_models or search_physical_ai) when the user has no named platforms.

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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