speechmatics_get_transcriptGet a completed job's transcript. format=txt (default, human-readable) | srt (subtitles) | json-v2 (structured words, timings, speakers, translations, summary). Only works once the job status is "done". Batch API: GET /jobs/{job_id}/transcript.
| Name | Required | Description | Default |
|---|---|---|---|
| format | No | Output format. Default txt for readability; use json-v2 for structured results. | |
| job_id | Yes | The job id. |
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?
The readOnlyHint annotation already signals safety, and the description adds meaningful behavior: the transcript is only available once the job is 'done', and it lists the available output formats (txt, srt, json-v2) with their intended uses. This goes 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 a single, well-structured sentence that front-loads the main purpose and uses a compact pipe-separated format list. Every clause adds value (formats, status requirement, API path) with no redundancy or fluff.
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?
For a simple read-only retrieval tool with two parameters and no output schema, the description covers the essentials: purpose, formats, prerequisite status, and API endpoint. The only minor omission is explicit error behavior, but the description is sufficient for an agent to invoke this tool correctly.
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% with clear descriptions for both job_id and format, including the enum. The description adds marginal value by restating the default format and the meaning of json-v2, but this largely duplicates the schema. Baseline 3 is appropriate.
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 tool's purpose with a specific verb and resource: 'Get a completed job's transcript.' It distinguishes itself from siblings like get_job (job metadata) and get_job_log by focusing on the transcript output. The format list adds further specificity.
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 clear context: it works only when the job status is 'done', which is a key prerequisite. It also implies this is the tool to use for transcript content, not job status or logs. However, it does not explicitly name alternatives or exclusions (e.g., 'use get_job for status').
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.