Skip to main content
Glama

import_flow

Import a phone-system flow or voice-agent config from a platform (e.g. Vapi) into the canonical, diffable Flow IR — the first step of putting your phone system under version control. Reports the fields the IR abstracts away. Pass save:true to persist it as a versioned flow. The same IR exports back out, so it doubles as a migration surface between platforms.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoName for the saved flow (defaults to the config name).
saveNoPersist the imported IR as a new versioned flow.
configYesThe platform's native flow/agent object.
platformYesThe source platform.

TDQS

A4.4/5.0
Behavior4/5

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

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool reports abstracted fields, supports a save:true parameter to persist the flow, and that the IR can be exported back out—useful migration context. It doesn't cover all edge cases (e.g., idempotency, failure modes), but covers the key behaviors well.

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?

Three sentences, each packed with relevant info: purpose, ability to report abstracted fields, save behavior, and migration use-case. No filler; front-loaded with the core purpose.

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 complex tool with nested objects and no output schema, the description covers purpose, usage context, save behavior, and what the tool reports. It's slightly light on return-value format, but the statement 'Reports the fields the IR abstracts away' gives a clear idea of the output. Good overall completeness.

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

Parameters4/5

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

Schema description coverage is 100%, so parameters are well-documented already. The description adds extra semantics beyond the schema: it clarifies that save:true persists the imported IR, that name defaults to config name, and that the config is platform-native. This elevates it above the baseline.

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 the tool's function: importing a phone-system flow or voice-agent config into the canonical, diffable Flow IR. It uses a specific verb ('Import') and resource ('Flow IR'), and distinguishes itself from sibling tools like diff_flow and test_flow by positioning this as the first step in version control.

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

Usage Guidelines4/5

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

The description provides clear context for when to use it: as the first step of putting a phone system under version control and as a migration surface between platforms. It doesn't explicitly mention alternatives, but the contrast with sibling tools (diff, test) makes the usage context unambiguous.

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

Try in Browser

Glama MCP Gateway

Add one secure layer between your agents and this server.

TDQS

A4/5.0
Disambiguation4/5

Most tools are clearly distinct by resource (monitors, suites, flows, numbers, recordings), but run_test and test_flow could be confused since both execute tests, though their scopes differ. The descriptions help disambiguate them.

Naming Consistency5/5

All tool names follow a consistent verb_noun snake_case pattern (create_, get_, list_, run_, verify_, etc.), with no camelCase or mixed conventions. Even compound names like get_monitor_health and verify_number_confirm remain predictable.

Tool Count4/5

At 16 tools, the set is slightly above the optimal 3-15 range, but the breadth of the voice-agent testing/monitoring domain justifies each tool's existence. It feels well-scoped rather than bloated.

Completeness2/5

The tool set lacks update/delete operations for most entities (monitors, suites, flows) and omits a get_run tool to retrieve individual live test results, leaving significant gaps that agents cannot work around. This will cause failures in lifecycle management and live-run result retrieval.

Resources