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Glama

askew_variables_set

Write a key-sealed variable that your phone can read in Apple Shortcuts, enabling secure data exchange while the device stays locked.

Instructions

Write a variable shared with the phone (sealed with the account key; the phone reads it with the same key).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameYes
valueYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.2

TDQS

C2.8/5.0
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It does provide some useful context about encryption ('sealed with the account key') and that the phone reads it with the same key, which is helpful. However, it omits critical behavior such as whether the operation overwrites existing variables, what happens on failure, any size limits, or the response format. This is a meaningful gap for a write operation.

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 a single, concise sentence that front-loads the core action ('Write a variable shared with the phone') and adds a brief technical note about sealing. Every word earns its place with no filler. It is appropriately sized for such a simple tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's simplicity (2 parameters, no output schema), the description is generally adequate for a basic understanding but lacks essential context. It does not explain whether the write is an upsert, how errors are reported, or what the tool returns (if anything). An agent cannot infer important operational details that are not in the schema or annotations, making the tool harder to use correctly without external documentation.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 0%, so the description must compensate for parameter meaning. It does not explain the semantics of 'name' or 'value' beyond the schema's types. The phrasing 'Write a variable' implies name is the variable identifier and value is the content, but it does not clarify aspects like allowed value shapes, constraints, or how the name must be formatted. The description adds little value over the raw schema.

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?

The description clearly states the verb 'Write' and the resource 'variable shared with the phone', which distinguishes it from the read counterpart (askew_variables_get). It conveys the essential purpose without being a tautology. However, it does not explicitly name any sibling tool for differentiation, relying on inference from the phrase 'shared with the phone'.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

There is no explicit guidance on when to use this tool versus alternatives, nor any exclusions or prerequisites. The phrasing 'shared with the phone' implies a use case, but it does not specify when to choose this over other tools like askew_variables_get or askew_run. The agent must infer usage from context alone, which is insufficient.

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