speechmatics_get_usageGet usage statistics (transcription minutes) for the account over a date range. Batch API: GET /usage.
| Name | Required | Description | Default |
|---|---|---|---|
| since | No | Start date, YYYY-MM-DD. | |
| until | No | End date, YYYY-MM-DD. |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint: true, so the read-only nature is known. The description goes beyond annotations by clarifying the data scope (account-level transcription minutes) and the temporal dimension (date range). It also introduces the Batch API endpoint, which indicates the underlying service context. This adds meaningful context beyond the annotation without contradiction.
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 two sentences, front-loaded with the primary purpose, and includes the API endpoint as supplementary info. There is no redundant phrasing or unnecessary detail. Every sentence contributes to understanding the tool's function and scope.
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?
With only two optional parameters and no output schema, the complexity is low. However, the description does not explain behavior when parameters are omitted (e.g., whether a default range applies) or describe the response format in any detail. These gaps, while not critical for simple usage stats, leave room for minor ambiguity in agent invocation.
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?
Schema description coverage is 100%, as both 'since' and 'until' parameters have descriptive strings. The description adds 'over a date range' which maps to these parameters, but does not provide additional syntax, defaults, or edge-case details. Since the schema carries the semantic load, the description adds limited value beyond the structured fields.
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 'Get' and the resource 'usage statistics (transcription minutes) for the account over a date range.' It distinguishes itself from sibling tools that operate on individual jobs (e.g., list_jobs, get_job) by focusing on account-level aggregation. The API endpoint is also specified, leaving no ambiguity about the tool's function.
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 implies usage context by specifying 'for the account over a date range,' but it does not explicitly state when to use this tool versus alternatives like list_jobs or get_transcript. There are no exclusionary statements or named alternatives, so the guidance is implied rather than direct.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
Each tool targets a distinct resource and action: job management (get, delete, list), transcript retrieval, usage stats, and submission. No overlapping purposes; the only pair that could seem similar (get_job vs get_transcript) is clearly differentiated by descriptions of status vs content.
All tool names follow the exact pattern `speechmatics_<verb>_<object>`, using consistent snake_case. This is highly predictable and makes the API surface easy to navigate.
With 7 tools, the set is well-scoped for a transcription service. Each tool covers a necessary operation without redundancy, and the count feels neither too thin nor overwhelming.
The toolset covers the full lifecycle of transcription jobs: create (transcribe_url), read (list_jobs, get_job), retrieve output (get_transcript), delete (delete_job), plus operational needs (get_job_log, get_usage). No significant gaps for the stated purpose.