Skip to main content
Glama

parameter_set_labels

Assign human-readable labels to a FMOD enumeration parameter for clear value selection in audio events, replacing numeric indices with ordered text names.

Instructions

Set enumeration labels on a UserEnumeration parameter.

Args: event_target: event:/ path or {guid}. parameter_name: The parameter's name. labels: Human-readable labels, in order (e.g. ["Off","Quiet","Loud"]).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
labelsYes
event_targetYes
parameter_nameYes
Behavior2/5

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

With no annotations, the description carries full responsibility. It does not disclose side effects (e.g., overwrites existing labels), error conditions (e.g., parameter not found or wrong type), or any expected return value. Only a terse statement of the action is given, which is insufficient for a mutation tool.

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 concise: a single sentence plus a compact argument list. It front-loads the purpose and provides essential parameter details without fluff, making it easy to scan.

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?

For a simple tool, the description covers the basics but omits context that would improve safe usage: whether the parameter must already be of type UserEnumeration, what happens if label count mismatches, and what the tool returns (if anything). Given no output schema, some return behavior disclosure would be helpful.

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 coverage is 0%, and the description compensates by explaining each parameter: event_target as 'event:/ path or {guid}', parameter_name as 'the parameter's name', and labels as 'Human-readable labels, in order' with an example. This adds meaning beyond the bare schema, though it could be more detailed (e.g., label count vs enum count).

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 action ('Set enumeration labels') and the resource ('UserEnumeration parameter'). It distinguishes from siblings like parameter_set_initial by focusing specifically on labels, making the tool's purpose unambiguous.

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 implies usage by mentioning 'enumeration labels' and the parameter type, but does not explicitly state when to prefer this over alternative parameter tools (e.g., parameter_set_initial) or provide prerequisites like the parameter must already exist. No alternatives are mentioned.

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/xDarkzx/Dans_Fmod_Studio_MCP'

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