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Skysay

Get an AI agent's call variables

get_agent_variables
Read-onlyIdempotent

Read an AI agent's call variables config (declared variables, SMS templates, SMS mode, caller lookup), with the revision a write must name and the schema_hash a call admitted now carries. The same answer as GET /v1/agents/{agent_id}/variables.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
agent_idYes
organization_idNoOptional. Defaults to the authenticated workspace; when supplied it must be that workspace's id.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A3.7/5.0
Behavior4/5

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

Annotations already declare readOnly/idempotent/non-destructive/closed-world, so safety is covered. The description adds genuine value beyond that: it discloses the returned contents and, importantly, that the payload carries the revision required by a write and the schema_hash a call admitted now carries. It omits auth or pagination details, preventing 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?

Two sentences, front-loaded with verb and resource, with the field enumeration in a parenthetical and the endpoint equivalence at the end. Efficient, though the last sentence is a mapping convenience rather than essential information.

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 readOnly tool with no output schema and only two parameters, the description adequately explains what is returned and why the revision/schema_hash matter. An agent can call this correctly with the information given, though the revision semantics could be expanded.

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 50%: organization_id is documented inline, agent_id is not. The description only reinforces agent_id via the GET path template and adds no syntax or format guidance. With the schema doing half the work, this is a baseline 3.

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?

States a specific verb (Read) and resource (an AI agent's call variables config), then enumerates the contained fields: declared variables, SMS templates, SMS mode, caller lookup. This clearly separates it from get_agent and from the write counterpart. It stops short of naming sibling tools explicitly, so it falls just under a 5.

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?

Usage is implied rather than stated: the mention of 'the revision a write must name' hints that this read feeds the set_agent_variables write, and the REST endpoint mapping grounds it. But there is no explicit 'use this when' or when-not guidance, so an agent must infer the workflow.

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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