Santismm Knowledge — Harness Engineering, Agentic AI & Governance

Get one Physical AI platform, deployment, guide or reference

get_physical_ai_item

Read-onlyIdempotent

Read one record by type and slug: a platform sheet (specs with test conditions, provenance and sources, autonomy by task, evidence stage, access, lifecycle), a documented case study (sourced facts, published KPIs, lessons), a guide, a cited source by id — or, with type 'source' and no slug, the whole resolved bibliography with each source's evidence level — or the taxonomy and methodology (no slug). A spec or KPI the maker has not published is null with status 'unpublished' — report it as unknown, never estimate it. Use this after search_physical_ai, with the type and slug it returned.

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": {
      "type": "<type>"
    },
    "name": "get_physical_ai_item"
  }
}

Input Schema

Name	Required	Description
`slug`	No	Required for platform, case_study and guide, e.g. 'spot'. For source, a source id; omit it to list every source. Ignored for taxonomy and methodology.
`type`	Yes	platform, case_study, guide, source, taxonomy or methodology.
`locale`	No	Language of the returned body. Default: en.

Output Schema

Name	Required	Description	Default
`type`	Yes
`locale`	No
`api_version`	Yes
`resource_uri`	Yes
`dataset_version`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedNov 7, 2026

changedInput schema / properties / slug / description

Previous value: -"Required except for taxonomy and methodology, e.g. 'spot' or a source id."New value: +"Required for platform, case_study and guide, e.g. 'spot'. For source, a source id; omit it to list every source. Ignored for taxonomy and methodology."

AddedNov 6, 2026

TDQS

A4/5.0

Behavior4/5

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

Annotations already cover read-only, idempotent, non-destructive and open-world behavior, so the bar is lower, yet the description adds real value: unpublished specs/KPIs come back as null with status 'unpublished' and must be reported as unknown, never estimated. That is a data-integrity rule an agent could not infer from the schema. It does not describe size or pagination of the whole-bibliography mode.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness3/5

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

The verb-first opening is well front-loaded, but the body is a single run-on sentence with three em-dash interruptions and dense parenthetical inventories, making it harder to scan than a short structured list of types would be. The content earns its place, but the packaging does not.

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?

For a six-type polymorphic read tool with an output schema and full annotations, the description covers payload per type, the null/unpublished policy, and call sequencing. Remaining gaps (locale effect, whether the all-sources listing is paginated) are modest given the schema and output schema already in place.

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 description coverage is 100% and the schema already states slug is required for platform/case_study/guide, is a source id for source, and is ignored for taxonomy/methodology. The description's parallel statements largely restate that rather than adding new meaning, so the baseline 3 applies.

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?

Opens with a specific verb+resource ('Read one record by type and slug') and then disambiguates each of the six record types by naming what the record contains (specs, provenance, KPIs, evidence level, taxonomy/methodology). That is far more than a restatement of the title and lets an agent pick the right type without opening the schema.

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?

Explicitly routes the agent: 'Use this after search_physical_ai, with the type and slug it returned,' and documents the special no-slug modes (all sources, taxonomy, methodology). It does not point at the analytical siblings (compare_physical_ai_platforms, assess_physical_ai_fit) as alternatives, so the when-not side is only implied.

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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