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Advanced TTS MCP Server

A high-quality, feature-rich Text-to-Speech MCP server with native TypeScript implementation. Designed for professional applications requiring natural, expressive speech synthesis with advanced controls and zero external dependencies.

✨ Features

šŸŽÆ Advanced Voice Control

  • 10 High-Quality Voices - Male and female voices with distinct personalities

  • Emotion Control - Neutral, happy, excited, calm, serious, casual, confident

  • Dynamic Pacing - Natural, conversational, presentation, tutorial, narrative modes

  • Speed & Volume - Precise control from 0.25x to 3.0x speed, 0.1x to 2.0x volume

šŸš€ Professional Capabilities

  • Streaming Audio - Real-time synthesis and playback

  • Batch Processing - Handle multiple text segments efficiently

  • Multiple Formats - WAV, MP3, FLAC, OGG output support

  • Natural Speech Enhancement - Automatic pause insertion and emotion markers

  • Queue Management - Handle multiple concurrent requests

šŸ”§ MCP Integration

  • 6 Powerful Tools - Complete synthesis, batch processing, voice management

  • 2 Rich Resources - Voice capabilities and usage examples

  • Real-time Status - Track processing progress and manage requests

  • File Management - Save, list, and organize audio outputs

Related MCP server: Fish Audio MCP Server

šŸš€ Quick Start

šŸŽÆ One-Click Deployment to Smithery Platform

  1. Deploy Now: Visit Smithery.ai and import this repository

  2. Configure: Set your preferred voice and speech settings

  3. Use Instantly: Access via Claude Desktop or any MCP-compatible client

Benefits:

  • āœ… Zero setup required

  • āœ… Automatic scaling and updates

  • āœ… No model downloads needed

  • āœ… Enterprise-grade hosting

šŸ“‹ Full Smithery Deployment Guide →

Option 2: Local Installation

Prerequisites:

  • Node.js 18+

Installation:

  1. Clone the repository

git clone https://github.com/samihalawa/advanced-tts-mcp.git
cd advanced-tts-mcp
  1. Install dependencies

npm install
  1. Configure Claude Desktop

Add to your claude_desktop_config.json:

{
  "mcpServers": {
    "advanced-tts": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/path/to/advanced-tts-mcp"
    }
  }
}
  1. Start using!

# Build TypeScript
npm run build

# Start server
npm start

Restart Claude Desktop and start synthesizing with natural, expressive voices.

šŸŽ™ļø Available Voices

Voice ID

Name

Gender

Description

af_heart

Heart

Female

Warm, friendly voice (default)

af_sky

Sky

Female

Clear, bright voice

af_bella

Bella

Female

Elegant, sophisticated voice

af_sarah

Sarah

Female

Professional, confident voice

af_nicole

Nicole

Female

Gentle, soothing voice

am_adam

Adam

Male

Strong, authoritative voice

am_michael

Michael

Male

Friendly, approachable voice

bf_emma

Emma

Female

Young, energetic voice

bf_isabella

Isabella

Female

Mature, expressive voice

bm_lewis

Lewis

Male

Deep, resonant voice

šŸ“š Usage Examples

Basic Synthesis

# Simple text-to-speech
await synthesize_speech(
    text="Hello! Welcome to Advanced TTS.",
    voice_id="af_heart"
)

Emotional Expression

# Excited announcement
await synthesize_speech(
    text="This is amazing news! You're going to love this new feature!",
    voice_id="af_heart",
    emotion="excited",
    pacing="conversational",
    speed=1.1
)

Professional Presentation

# Tutorial narration
await synthesize_speech(
    text="Step one: Open your browser. Step two: Navigate to the website.",
    voice_id="am_adam", 
    emotion="calm",
    pacing="tutorial",
    speed=0.9
)

Batch Processing

# Multiple segments with pauses
await batch_synthesize(
    segments=[
        "Welcome to our presentation.",
        "Today we'll cover three main topics.", 
        "Let's begin with the first topic."
    ],
    voice_id="af_sarah",
    emotion="confident",
    pacing="presentation",
    merge_output=True,
    segment_pause=1.0,
    save_file=True
)

šŸ› ļø Available Tools

synthesize_speech

Convert text to natural speech with full control over voice characteristics.

Parameters:

  • text - Text to synthesize (max 10,000 chars)

  • voice_id - Voice selection (see table above)

  • speed - Speech rate (0.25-3.0)

  • emotion - Voice emotion (neutral, happy, excited, calm, serious, casual, confident)

  • pacing - Speech style (natural, conversational, presentation, tutorial, narrative, fast, slow)

  • volume - Audio volume (0.1-2.0)

  • output_format - File format (wav, mp3, flac, ogg)

  • save_file - Save to file (boolean)

  • filename - Custom filename

batch_synthesize

Process multiple text segments efficiently with optional merging.

Parameters:

  • segments - List of text segments

  • merge_output - Combine into single file

  • segment_pause - Pause between segments (0.0-5.0s)

  • All synthesis parameters from above

get_voices

Retrieve complete voice information and capabilities.

get_status

Check processing status for synthesis requests.

cancel_request

Cancel active synthesis operations.

list_output_files

Browse saved audio files with metadata.

šŸŽ›ļø Voice Controls

Emotions

  • Neutral - Standard, professional tone

  • Happy - Upbeat, cheerful expression

  • Excited - Enthusiastic, energetic delivery

  • Calm - Relaxed, soothing tone

  • Serious - Formal, authoritative delivery

  • Casual - Relaxed, conversational style

  • Confident - Assured, professional tone

Pacing Styles

  • Natural - Balanced, human-like rhythm

  • Conversational - Casual discussion pace

  • Presentation - Professional speaking rhythm

  • Tutorial - Educational, clear delivery

  • Narrative - Storytelling pace

  • Fast - Quick delivery (1.2x base speed)

  • Slow - Deliberate delivery (0.8x base speed)

šŸŽµ Audio Formats

Format

Quality

Use Case

WAV

Uncompressed

Highest quality, editing

MP3

Compressed

Web, streaming, sharing

FLAC

Lossless

Archival, high-quality storage

OGG

Compressed

Open source alternative

šŸ”§ Configuration

Environment Variables

# Model paths (optional)
KOKORO_MODEL_PATH=./kokoro-v1.0.onnx
KOKORO_VOICES_PATH=./voices-v1.0.bin

# Output settings
TTS_OUTPUT_DIR=./audio_output
TTS_MAX_QUEUE_SIZE=100

# Audio settings  
TTS_DEFAULT_VOICE=af_heart
TTS_ENABLE_STREAMING=true

Server Configuration

config = ServerConfig(
    model_path="./kokoro-v1.0.onnx",
    voices_path="./voices-v1.0.bin", 
    output_dir="./audio_output",
    max_queue_size=100,
    enable_streaming=True,
    default_voice="af_heart"
)

šŸ—ļø Architecture

ā”œā”€ā”€ src/advanced_tts/
│   ā”œā”€ā”€ __init__.py          # Package initialization
│   ā”œā”€ā”€ server.py            # MCP server implementation  
│   ā”œā”€ā”€ engine.py            # Kokoro TTS engine wrapper
│   ā”œā”€ā”€ models.py            # Data models and validation
│   └── utils.py             # Utility functions
ā”œā”€ā”€ pyproject.toml           # Project configuration
ā”œā”€ā”€ README.md               # Documentation
└── LICENSE                 # MIT License

šŸ¤ Contributing

Contributions welcome! Areas for improvement:

  • Additional voice models

  • Real-time streaming synthesis

  • Advanced audio effects

  • Multi-language support

  • Performance optimizations

šŸ“„ License

MIT License - see LICENSE for details.

šŸ™ Acknowledgments

  • Kokoro TTS - High-quality neural voice synthesis

  • MCP Protocol - Seamless AI model integration

  • FastMCP - Efficient server framework


Developed by Sami Halawa

Transform your text into natural, expressive speech with Advanced TTS MCP Server.

Available Tools

5 tools
batch_synthesizeC

Synthesize multiple text segments with optional merging and intelligent pacing

ParametersJSON Schema
NameRequiredDescriptionDefault
emotionNoVoice emotion for all segmentsneutral
filenameNoCustom filename for saved audio
mergeOutputNoMerge segments into single file
outputFormatNoAudio output formatwav
pacingNoSpeech pacing style for all segmentsnatural
saveFileNoSave audio to file
segmentPauseNoPause between segments in seconds
segmentsYesList of text segments to synthesize
speedNoSpeech speed (0.25-3.0)
voiceIdNoVoice to use for all segmentsaf_heart

TDQS

C2.9/5.0
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 mentions 'optional merging and intelligent pacing,' which adds some context about output behavior, but fails to cover critical aspects: whether synthesis is resource-intensive, if there are rate limits, authentication needs, error handling, or what the output entails (e.g., audio files, metadata). For a tool with 10 parameters and no annotations, this is a significant gap in transparency.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence: 'Synthesize multiple text segments with optional merging and intelligent pacing.' It is front-loaded with the core action and key features, with zero wasted words. Every element earns its place by conveying essential information without redundancy or fluff.

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 complexity (10 parameters, no output schema, no annotations), the description is incomplete. It lacks details on output behavior (e.g., what is returned, file handling), error conditions, performance implications, and how it differs from siblings like 'synthesize_speech.' For a batch synthesis tool with rich parameters, the description should provide more context to guide effective use.

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

Parameters3/5

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

Schema description coverage is 100%, meaning all parameters are documented in the input schema. The description adds minimal value beyond the schema—it implies batch processing ('multiple text segments') and hints at 'merging' (related to 'mergeOutput') and 'pacing' (related to 'pacing'), but does not elaborate on parameter interactions or semantics. With high schema coverage, the baseline score of 3 is appropriate, as the description does not compensate with additional insights.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Synthesize multiple text segments with optional merging and intelligent pacing.' It specifies the verb ('synthesize'), resource ('multiple text segments'), and key optional features ('merging' and 'intelligent pacing'), making the intent unambiguous. However, it does not explicitly differentiate from sibling tools like 'synthesize_speech'—likely a batch version versus single synthesis—which prevents a perfect score.

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. It mentions 'optional merging and intelligent pacing,' which hints at features, but does not specify scenarios, prerequisites, or comparisons to sibling tools (e.g., 'synthesize_speech' for single segments). Without explicit when-to-use or when-not-to-use advice, the agent lacks context for tool selection.

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

get_statusC

Get processing status for a synthesis request

ParametersJSON Schema
NameRequiredDescriptionDefault
requestIdYesRequest ID to check status for

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the tool retrieves status but doesn't describe what the status includes (e.g., pending, completed, failed), whether it's a read-only operation, potential errors (e.g., invalid request ID), or rate limits. This leaves significant gaps for a tool that likely interacts with asynchronous processes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that front-loads the core purpose without unnecessary words. Every part of the sentence ('Get processing status for a synthesis request') contributes directly to understanding the tool's function.

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 lack of annotations and output schema, the description is incomplete for a status-checking tool. It doesn't explain what information is returned (e.g., status states, progress percentages, error messages) or behavioral aspects like idempotency or polling requirements. This is inadequate for guiding an agent in a synthesis workflow.

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

Parameters3/5

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

Schema description coverage is 100%, with the single parameter 'requestId' clearly documented in the schema. The description adds no additional meaning beyond implying the parameter is for a synthesis request, which is already evident from the tool's context. This meets the baseline for high schema coverage.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('processing status for a synthesis request'), making the purpose immediately understandable. It distinguishes this from sibling tools like 'synthesize_speech' (which creates requests) and 'list_output_files' (which lists results). However, it doesn't explicitly differentiate from 'batch_synthesize' or 'get_voices', which are related but serve different purposes.

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. It doesn't mention prerequisites (e.g., needing a request ID from a previous synthesis operation), exclusions, or comparisons to siblings like 'list_output_files' for retrieving results. Usage is implied 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.

get_voicesB

Get list of available voices with their capabilities and supported features

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.2/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. It mentions what information is returned but doesn't address important behavioral aspects like whether this is a read-only operation, if there are rate limits, authentication requirements, or what format the response takes. The description provides basic output content but lacks operational context needed for safe invocation.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, efficient sentence that communicates the essential information without any wasted words. It's front-loaded with the core purpose and adds specific detail about what's included in the response. Every word earns its place in this compact description.

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

Completeness3/5

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

For a parameterless read operation with no output schema, the description provides adequate but minimal information. It tells what the tool does and what information it returns, but doesn't address format, structure, or behavioral constraints. Given the lack of annotations and output schema, more detail about response format or operational considerations would improve completeness for this type of tool.

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

Parameters4/5

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

The tool has 0 parameters with 100% schema description coverage, so the schema already fully documents the parameter situation. The description appropriately doesn't discuss parameters since none exist, and it focuses instead on what the tool returns. This meets the baseline expectation for parameterless tools while adding value about the return content.

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

Purpose4/5

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

The description clearly states the verb ('Get') and resource ('list of available voices'), making the purpose immediately understandable. It adds specificity about what information is returned ('capabilities and supported features'), which goes beyond just listing voices. However, it doesn't explicitly differentiate from sibling tools like 'list_output_files' or 'get_status', which prevents a perfect score.

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 'synthesize_speech' or 'batch_synthesize'. There's no mention of prerequisites, typical use cases, or when this tool would be appropriate versus when other tools might be better suited. The agent must infer usage context from the tool name alone.

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

list_output_filesB

List saved audio files in the output directory with metadata

ParametersJSON Schema
NameRequiredDescriptionDefault

No parameters

TDQS

B3.1/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries the full burden of behavioral disclosure. It states what the tool does but does not disclose any behavioral traits such as whether it requires specific permissions, how it handles errors, if it has rate limits, or what the output format looks like. This is a significant gap for a tool with zero annotation coverage.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, well-structured sentence that directly states the tool's purpose without any waste. It is front-loaded with the core action and resource, making it highly concise and easy to parse, earning its place with no extraneous information.

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 lack of annotations and output schema, the description is incomplete. It does not explain what metadata is included, how files are sorted or filtered, or what the return values look like. For a tool that lists files with metadata, more context is needed to fully understand its behavior and output, making it inadequate for the complexity involved.

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

Parameters4/5

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

The tool has 0 parameters, and schema description coverage is 100%, so there are no parameters to document. The description does not need to add parameter semantics beyond the schema, and it appropriately avoids unnecessary details. A baseline of 4 is applied as it handles the zero-parameter case efficiently without redundancy.

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

Purpose4/5

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 ('saved audio files in the output directory with metadata'), making the purpose specific and understandable. However, it does not explicitly differentiate from sibling tools like 'get_status' or 'get_voices', which might also involve listing or retrieving information, so it misses full sibling distinction.

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. It does not mention any context, prerequisites, or exclusions, such as when to prefer 'list_output_files' over 'get_status' for checking file availability or other sibling tools. This lack of usage context leaves the agent without clear direction.

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

synthesize_speechC

Convert text to speech with advanced voice controls and natural expression

ParametersJSON Schema
NameRequiredDescriptionDefault
emotionNoVoice emotionneutral
filenameNoCustom filename for saved audio
outputFormatNoAudio output formatwav
pacingNoSpeech pacing stylenatural
saveFileNoSave audio to file
speedNoSpeech speed (0.25-3.0)
textYesText to convert to speech
voiceIdNoVoice to use for synthesisaf_heart
volumeNoAudio volume (0.1-2.0)

TDQS

C2.9/5.0
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden for behavioral disclosure. While 'Convert text to speech' implies a creation/write operation, it doesn't address key behavioral aspects: whether this is a synchronous or asynchronous process, potential rate limits, authentication requirements, file storage implications when saveFile is true, or what happens on failure. The mention of 'advanced voice controls' is vague and doesn't provide concrete behavioral information.

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 gets straight to the point. 'Convert text to speech' is front-loaded with the core function, followed by additional context. There's no wasted verbiage or redundancy. However, it could be slightly more structured by separating core function from additional capabilities.

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?

For a tool with 9 parameters, no annotations, and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns (audio data? file path? success status?), doesn't address error conditions, and provides minimal behavioral context. The combination of complex parameters and lack of structured metadata requires a more comprehensive description to guide proper tool usage.

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

Parameters3/5

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

The description adds minimal value beyond the input schema, which has 100% coverage. 'with advanced voice controls and natural expression' vaguely references the emotion, pacing, and voiceId parameters but doesn't provide additional semantic context. The schema already comprehensively documents all 9 parameters with descriptions, defaults, enums, and constraints, so the baseline 3 is appropriate.

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

Purpose4/5

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

The description clearly states the tool's purpose: 'Convert text to speech' specifies the verb and resource. It adds 'with advanced voice controls and natural expression' which provides additional context about capabilities. However, it doesn't explicitly differentiate from sibling tools like batch_synthesize or get_voices, which prevents a perfect score.

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. It doesn't mention batch_synthesize for multiple texts, get_voices for voice selection, or list_output_files for file management. There's no context about prerequisites, limitations, or appropriate use cases beyond the basic function.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. 5 tool updatesv1.0.0
    • First observedbatch_synthesize
    • First observedget_status
    • First observedget_voices
    • First observedlist_output_files
    • First observedsynthesize_speech

TDQS

A3.5/5.0
Disambiguation5/5

Each tool has a distinct, non-overlapping purpose: batch_synthesize handles multiple segments, synthesize_speech handles single conversions, get_status checks request status, get_voices lists voice options, and list_output_files manages saved files. The descriptions clearly differentiate their functions, eliminating ambiguity.

Naming Consistency5/5

All tools follow a consistent verb_noun naming pattern (e.g., batch_synthesize, get_status, get_voices, list_output_files, synthesize_speech). The verbs (batch_, get_, list_, synthesize_) are appropriate and uniform, making the set predictable and easy to understand.

Tool Count5/5

With 5 tools, the server is well-scoped for a TTS (text-to-speech) domain. The count is appropriate, covering core operations like synthesis, status checking, voice management, and file listing without being too sparse or bloated. Each tool earns its place in the workflow.

Completeness4/5

The tool set covers essential TTS operations: synthesis (single and batch), status tracking, voice discovery, and output management. A minor gap exists in lacking explicit tools for deleting or managing output files beyond listing, but agents can likely work around this, and core workflows are well-supported.

Maintenance

ActivityInactive
ResponsivenessSyncing

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