Discover available speech-to-text models and their features to select the right one for audio transcription tasks.
List all available ASR models and their capabilities.
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
| Name | Required | Description | Default |
|---|---|---|---|
| text | Yes | ||
| type | Yes | ||
| _meta | No | ||
| annotations | No |
Changes observed during successful MCP inspections.
v0.1.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. While 'List' implies a read-only operation, the description doesn't specify whether this requires authentication, what format the capabilities are returned in, if there are rate limits, or if the list is static or dynamic. For a tool with zero annotation coverage, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, efficient sentence that directly states the tool's purpose without any fluff. It's front-loaded with the core action ('List all available ASR models') and adds necessary detail ('and their capabilities'). Every word earns its place, making it highly concise.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's low complexity (0 parameters) and the presence of an output schema (which should document the return values), the description is minimally adequate. However, it lacks context about when to use it relative to siblings and behavioral details not covered by annotations (which are absent). This makes it incomplete for optimal agent use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
The input schema has 0 parameters with 100% coverage, so there are no parameters to document. The description appropriately doesn't mention any parameters, which is correct for this case. Baseline 4 is applied as per the rules for 0 parameters, since no additional parameter semantics are needed.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the verb ('List') and resource ('all available ASR models and their capabilities'), making the purpose immediately understandable. However, it doesn't explicitly differentiate from sibling tools like 'speech_to_text' which might also involve ASR models, though the distinction is somewhat implied by the listing vs. processing focus.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides no guidance on when to use this tool versus alternatives. It doesn't mention whether this should be used for discovery before invoking 'speech_to_text', or if it's for administrative purposes. With sibling tools like 'diarize_speech' and 'speech_to_text' that likely use ASR models, the lack of contextual guidance is a notable gap.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.