Skip to main content
Glama

Save Calculation to Workspace

workspace_save

Save a calculation result with a name to a persistent workspace that remains available across restarts. Retrieve it later using the assigned name.

Instructions

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
Behavior3/5

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

Annotations indicate mutation (readOnlyHint=false) and no idempotency. Description adds context of persistence (surviving restarts) but does not disclose overwrite behavior if a variable with the same name already exists, which is a notable gap.

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?

Description is brief and front-loaded with the core purpose. Two examples efficiently show usage. Could be slightly more concise, but overall well-structured with no wasted sentences.

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?

Description and annotations cover basic behavior, but the tool has an output schema not shown here. No mention of return value, error handling, or overwrite behavior. For a mutation tool with 3 required params, more completeness is expected.

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% with clear descriptions for all three parameters. The description adds no additional semantic meaning beyond the schema and examples, meeting the baseline for full coverage.

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?

Title and description clearly state the tool saves a calculation to persistent workspace. Examples illustrate the exact syntax with name, expression, and result. Distinct from sibling tools like workspace_load or 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?

Description explains what the tool does but lacks explicit guidance on when to use it versus alternatives (e.g., when to save vs. just calculate). Examples are helpful but no when-not-to-use or prerequisite conditions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/clouatre-labs/math-mcp-learning-server'

If you have feedback or need assistance with the MCP directory API, please join our Discord server