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Save Calculation to Workspace

workspace_save

Save calculation to persistent workspace (survives restarts).

Examples: save_calculation("portfolio_return", "10000 * 1.07^5", 14025.52) save_calculation("circle_area", "pi * 5^2", 78.54)

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYesVariable name for the saved calculation. Used to retrieve it later. Example: 'circle_area'
resultYesNumeric result of evaluating the expression, e.g., 78.54
expressionYesThe mathematical expression that was evaluated. Example: 'pi * r**2'

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
topicYes
actionNosave_calculation
is_newYes
resultYes
successYes
difficultyYes
expressionYes
session_idNo
total_variablesYes

TDQS

A4/5.0
Behavior4/5

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

The description adds the behavioral detail that the workspace is 'persistent (survives restarts)', which is not captured in the annotations. The annotations (readOnlyHint=false, idempotentHint=false) already indicate a non-read, non-idempotent operation, so the description adds useful context about persistence. No contradiction.

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 two sentences with examples, clearly front-loaded with the purpose statement. Every sentence serves a purpose, and the examples are concise and illustrative without redundancy.

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 simple save tool with a complete schema and an output schema, the description covers the key aspect (persistence) and does not need to explain return values. It could mention overwrite behavior or error cases, but these are not essential for basic understanding. The description is sufficient for the tool's complexity.

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?

The input schema provides descriptions for all three parameters (name, expression, result) with 100% coverage, so the baseline is 3. The description's examples show sample values and ordering, but they do not add meaningful semantic detail beyond the schema's field descriptions.

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?

The description clearly states a specific verb ('Save') and resource ('calculation to persistent workspace'), with an added detail that it survives restarts. This distinguishes it from siblings like workspace_load and the calc_* tools.

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 does not explicitly state when to use this tool versus alternatives. It implies usage through the verb 'save' and the persistence note, but there is no explicit 'when to use' or mention of workspace_load as the counterpart. The examples illustrate usage but do not provide alternative guidance.

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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TDQS

A4/5.0
Disambiguation4/5

Most tools are clearly distinct (calculation, interest, stats, units, matrix ops, plotting, workspace). However, plot_function, plot_line_chart, and plot_financial_line could be confused since they all produce line-like plots, though descriptions note their specific use cases.

Naming Consistency5/5

Tool names follow a clear, consistent prefix pattern: calc_*, matrix_*, plot_*, and workspace_*. This makes it easy to infer related functionality at a glance.

Tool Count4/5

17 tools is on the higher side but acceptable for the wide math scope (basic arithmetic, statistics, units, matrices, plotting, workspace). Each tool serves a distinct purpose, though a couple like plot_line_chart and plot_function could potentially be consolidated.

Completeness4/5

Core mathematical operations are well covered: expression evaluation, statistics, unit conversion, matrix operations, and common plot types. Minor gaps exist (e.g., no bar chart, no equation solving), but these are not critical for the server's apparent educational purpose.