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Glama

get_asr

Retrieve the active live speech-recognition engine for upcoming recordings to confirm which mode (auto, doubao-web, doubao-ime, sr, or off) will process speech.

Instructions

Current live speech-recognition engine for future recordings (auto|doubao-web|doubao-ime|sr|off)

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "get_asr"
  }
}

Input Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

B3.4/5.0
Behavior3/5

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

No annotations are supplied, so the description carries the full burden. It does disclose a meaningful behavioral nuance: the value governs future recordings rather than ones already in progress. It does not explicitly state that the call is side-effect free, though the 'get_' name and zero parameters make that low risk.

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?

A single compact fragment with the resource front-loaded and the enumerated value set in a tight parenthetical. Nothing is redundant. The only cost is that it is a phrase rather than a sentence, which reads slightly telegraphic.

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?

With no output schema, the description must convey the return value and it does, including the full set of possible engine identifiers. For a zero-parameter getter this is nearly sufficient; the default value and what 'auto' resolves to are the only notable omissions.

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?

The tool takes zero parameters, so there is nothing for the description to disambiguate — baseline 4 applies. The parenthetical value list is not input syntax for this tool but usefully documents the domain of the returned value, which lets an agent interpret the result without consulting set_asr.

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 identifies a specific resource ('current live speech-recognition engine') and its scope ('for future recordings'), which cleanly distinguishes it from the sibling set_asr. It is a noun phrase rather than a verb-led statement, so the read action is only implied by the 'get_' prefix, but the subject matter is unambiguous.

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 when-to-use guidance, no mention of the natural counterpart set_asr, and no statement of preconditions (e.g. that it should be read before changing the engine). The pairing with set_asr is inferable from the sibling name alone, which the description does nothing to reinforce.

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