Skip to main content
Glama
daredoole

audio-calibration-mcp

by daredoole

audio_target_registry

Retrieve versioned, evidence-labelled listening target starting points for your device class to establish accurate calibration baselines.

Instructions

List versioned, evidence-labelled listening target starting points.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
deviceClassNo
Behavior2/5

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

With no annotations, the description carries the full burden of behavioral disclosure. It implies a read-only operation ('List') but doesn't explicitly state it, nor does it mention any limitations, side effects, authentication requirements, or how 'versioned' and 'evidence-labelled' affect the response. The description is minimal and doesn't go beyond the basic action.

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?

The description is a single, front-loaded sentence with no filler. It is concise and to the point, though it arguably sacrifices necessary detail for brevity. Structurally it scores well for efficiency.

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?

The description is too sparse for a registry tool in a complex domain. It doesn't explain what 'listening target starting points' are, what 'versioned' and 'evidence-labelled' imply, or how the optional deviceClass parameter influences output. With no output schema, the agent is left without enough context to call it correctly or interpret results.

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

Parameters1/5

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

The only parameter, deviceClass, is an enum but has no schema description and is entirely absent from the tool description. Schema description coverage is 0%, so the description must compensate, but it fails to explain the parameter's meaning, purpose, or how it filters results. The agent has no semantic guidance beyond the enum values themselves.

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 states a specific action ('List') and a specific resource ('versioned, evidence-labelled listening target starting points'). It conveys the core purpose clearly and distinguishes it from generic listing tools, though it doesn't explicitly differentiate from siblings like audio_evidence_registry.

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?

No guidance is provided on when to use this tool versus alternatives. There is no mention of prerequisites, use cases, or exclusions. The agent must infer usage solely from the name and description.

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/daredoole/audio-calibration-mcp'

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