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Windows TTS MCP Server

by balloonf

speak_slow

Converts text to speech at a slower pace using Windows TTS, ideal for improved comprehension or clear communication of complex information.

Instructions

텍스트를 천천히 읽어줍니다

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
textYes

Implementation Reference

  • The speak_slow tool handler function. Registered via @mcp.tool() decorator. Splits text into chunks, speaks each slowly (rate=-3) in a background thread using powershell_tts.
    @mcp.tool()
    def speak_slow(text: str) -> str:
        """텍스트를 천천히 읽어줍니다"""
        try:
            # 텍스트 분할
            text_chunks = split_text_for_tts(text, 400)
            total_chunks = len(text_chunks)
            
            def _speak_slow():
                for i, chunk in enumerate(text_chunks, 1):
                    safe_print(f"[SLOW TTS] {i}/{total_chunks} 부분 재생 중: {chunk[:50]}...")
                    powershell_tts(chunk, rate=-3, volume=100)  # 느린 속도
                    if i < total_chunks:
                        time.sleep(0.8)  # 느린 재생은 간격을 더 길게
            
            thread = threading.Thread(target=_speak_slow, daemon=True)
            thread.start()
            
            if total_chunks > 1:
                return f"[SLOW] 천천히 재생 시작 ({total_chunks}개 부분): '{text[:50]}...'"
            else:
                return f"[SLOW] 천천히 재생 시작: '{text[:50]}...'"
            
        except Exception as e:
            return f"[ERROR] 천천히 재생 오류: {str(e)}"
  • Core helper function that executes TTS via PowerShell, managing processes and supporting rate/volume parameters. Used by speak_slow.
    def powershell_tts(text: str, rate: int = 0, volume: int = 100) -> bool:
        """PowerShell을 사용한 TTS 실행"""
        process = None
        try:
            if platform.system() != "Windows":
                safe_print("[ERROR] Windows가 아닙니다")
                return False
            
            # 텍스트에서 작은따옴표 이스케이프 처리
            escaped_text = text.replace("'", "''")
            
            # PowerShell TTS 명령어
            cmd = [
                "powershell", "-Command",
                f"Add-Type -AssemblyName System.Speech; "
                f"$synth = New-Object System.Speech.Synthesis.SpeechSynthesizer; "
                f"$synth.Rate = {rate}; "
                f"$synth.Volume = {volume}; "
                f"$synth.Speak('{escaped_text}'); "
                f"$synth.Dispose()"
            ]
            
            # 프로세스 시작
            process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, text=True)
            
            # 실행 중인 프로세스 목록에 추가
            with process_lock:
                running_processes.append(process)
            
            # 프로세스 완료 대기
            stdout, stderr = process.communicate(timeout=180)
            
            # 완료된 프로세스 목록에서 제거
            with process_lock:
                if process in running_processes:
                    running_processes.remove(process)
            
            if process.returncode == 0:
                safe_print(f"[SUCCESS] TTS 완료: {text[:30]}...")
                return True
            else:
                safe_print(f"[ERROR] TTS 오류: {stderr}")
                return False
                
        except subprocess.TimeoutExpired:
            safe_print("[WARNING] TTS 시간 초과")
            if process:
                process.kill()
                with process_lock:
                    if process in running_processes:
                        running_processes.remove(process)
            return False
        except Exception as e:
            safe_print(f"[ERROR] TTS 예외: {e}")
            if process:
                try:
                    process.kill()
                    with process_lock:
                        if process in running_processes:
                            running_processes.remove(process)
                except:
                    pass
            return False
  • Helper function to split long text into TTS-friendly chunks, used in speak_slow.
    def split_text_for_tts(text: str, max_length: int = 500) -> list:
        """텍스트를 TTS용으로 적절히 분할"""
        if len(text) <= max_length:
            return [text]
        
        # 문장 단위로 분할 시도
        import re
        sentences = re.split(r'[.!?。!?]\s*', text)
        
        chunks = []
        current_chunk = ""
        
        for sentence in sentences:
            # 문장이 너무 긴 경우 더 작게 분할
            if len(sentence) > max_length:
                # 쉼표나 기타 구두점으로 분할
                sub_parts = re.split(r'[,;:\s]\s*', sentence)
                for part in sub_parts:
                    if len(current_chunk + part) <= max_length:
                        current_chunk += part + " "
                    else:
                        if current_chunk.strip():
                            chunks.append(current_chunk.strip())
                        current_chunk = part + " "
            else:
                # 현재 청크에 문장을 추가할 수 있는지 확인
                if len(current_chunk + sentence) <= max_length:
                    current_chunk += sentence + ". "
                else:
                    if current_chunk.strip():
                        chunks.append(current_chunk.strip())
                    current_chunk = sentence + ". "
        
        # 마지막 청크 추가
        if current_chunk.strip():
            chunks.append(current_chunk.strip())
        
        return chunks
  • MCP tool registration decorator for speak_slow.
    @mcp.tool()
Behavior2/5

Does 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. It states the tool reads text slowly, implying it's a read-only operation, but doesn't cover aspects like whether it requires specific permissions, how it handles errors, if it's rate-limited, or what the output format is (e.g., audio, status). The description is minimal and lacks critical behavioral details.

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?

The description is a single, efficient sentence that directly states the tool's function without unnecessary words. It is appropriately sized for a simple tool, though it could be more informative. The structure is straightforward and front-loaded with the core purpose.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (simple text-to-speech with one parameter), lack of annotations, no output schema, and low schema coverage, the description is incomplete. It doesn't explain the output (e.g., whether it plays audio, returns a status, or has side effects), error handling, or how it interacts with sibling tools. The description is too minimal to be fully helpful for an AI agent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The description does not add any meaning beyond what the input schema provides. The schema has 1 parameter ('text') with 0% description coverage, and the tool description does not explain what 'text' should contain (e.g., plain text, formatted text, length limits) or provide examples. With low schema coverage, the description fails to compensate for the lack of parameter documentation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states the tool's purpose ('텍스트를 천천히 읽어줍니다' translates to 'Reads text slowly'), which is a clear verb+resource combination. However, it doesn't differentiate from sibling tools like 'speak', 'speak_fast', or 'speak_quiet', leaving the distinction unclear. The purpose is understandable but lacks sibling differentiation.

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?

The description provides no guidance on when to use this tool versus alternatives like 'speak', 'speak_fast', or 'speak_quiet'. There is no mention of specific contexts, exclusions, or prerequisites for using this tool over others in the sibling list. Usage is implied by the name but not explicitly stated.

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

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