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calculators

get_calculator_schema

Get the input and output schema for a calculator, generated from its reviewed contract: parameter types, units, allowed dropdown values and definitional bounds, plus outputs keyed by named range (including the status output). Table inputs carry a 'grid' block giving the exact number of rows to send, the column layout, which columns are editable, and how to shape a row; a table whose 'grid' block says writable=false cannot be supplied at all. Schema 0.18 exposes required and omitted declarations plus version-bound visibility tables; older versions require every input while awaiting metadata migration. Optionally resolves one immutable platform version. Returns trust metadata for the resolved version.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
slugYesThe calculator slug, e.g. 'steel-column-capacity-calculator'.
platform_versionNoOptional positive platform version number. Omit for the current published version.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.9/5.0
Behavior4/5

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

With no annotations, the description carries the full burden, and it delivers: it discloses the writable=false table constraint, the pre-0.18 'every input required' behavior versus 0.18's required/omitted declarations, and that it returns trust metadata for the resolved version. It doesn't cover auth requirements or rate limits, keeping it below a 5.

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 purpose, then details about the returned schema structure and version behavior. Dense but every clause adds information; the middle sentence about 'grid' blocks is long but earns its place for a schema-inspection tool.

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?

No output schema exists, so the description must characterize returns — and it does, describing parameter types, units, dropdown values, bounds, named-range outputs, the status output, and grid blocks. It also covers the version-visibility caveat. Minor gaps remain (no example call, no errors), but the return-shape coverage is notably thorough.

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% — both slug and platform_version are fully documented in-schema, including the 'omit for current published version' default and the 'must be positive' constraint. The description adds only the 'one immutable platform version' framing, so 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?

States a specific verb+resource ('Get the input and output schema for a calculator') and names the source of truth ('generated from its reviewed contract'). This unambiguously distinguishes it from run_calculator (execute) and search_calculators (discover).

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 this is the inspection step before invocation, and mentions the optional platform_version resolution, but never explicitly states when to call this versus run_calculator or how it fits a workflow. Usage is inferable rather than spelled out.

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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