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Processes voice dictation from microphone audio stream and injects the recognized text into target applications
This MCP server has severe definition quality issues. The single tool 'process_dictation' lacks proper schema documentation, has an underdescribed parameter, and the implementation is a mock stub with no actual functionality. The tool name violates verb_noun patterns and the input schema is visible but minimally documented. Parameter descriptions are present but generic. The mock implementation ('text = "Meeting rescheduled..."') indicates this is not production-ready code. No error handling, no output schema documentation, no parameter constraints, and no evidence of actual voice-to-text processing capability.
Processes voice dictation from microphone audio stream and injects recognized text into target application window
Tool name 'process_dictation' violates verb_noun pattern. Should be 'inject_dictated_text' or similar to clearly signal both the input processing AND the injection action.
Input parameter 'mic_audio_stream' typed as string but described as 'Microphone audio stream input (e.g., PCM 16000Hz format)'. No schema specifies format, encoding, or expected data structure. LLM cannot determine whether to pass base64, raw binary, WAV header, or stream reference.
No output schema documented. The mock implementation returns {'dictated_text': str, 'word_error_rate_pct': float, 'injected_successfully': bool}, but LLM has no way to know this from the MCP definition.
Tool description (89 chars) is borderline short and lacks critical context: When should LLM call this? What prerequisites exist? What happens if injection fails? Does it require OS-level permissions? Descriptions should be 10-1024 chars with WHAT/WHEN/prerequisites per pattern:tool-description.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | F | 31 | 2026-07-28+ | v2 |
Implementation is a mock stub returning hardcoded text ('Meeting rescheduled to tomorrow 3 PM due to flight delay.'). No actual voice recognition, no microphone integration, no window injection. Code cannot be used in production.
No error handling. Tool provides no guidance on failure modes: What if microphone is unavailable? What if target window not found? What if audio format is invalid? Per pattern:recovery-guide, errors must guide LLM on next steps.
Parameter 'target_app_window' defaults to 'Slack' with no enum constraint or validation. LLM could pass arbitrary window names. No documentation of supported applications or behavior on unsupported targets.
No security considerations documented. Tool injects text into arbitrary application windows, this is a write operation with OS-level side effects. No permission checks, no audit logging, no user confirmation mechanism per pattern:confirmation-request.