Advanced TTS MCP Server

Server Quality Checklist

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Latest release: v1.0.0
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.
Average 3/5 across 5 of 5 tools scored.
See the Tool Scores section below for per-tool breakdowns.
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How is the quality score calculated?

The overall quality score combines two components: Tool Definition Quality (70%) and Server Coherence (30%).

Tool Definition Quality measures how well each tool describes itself to AI agents. Every tool is scored 1–5 across six dimensions: Purpose Clarity (25%), Usage Guidelines (20%), Behavioral Transparency (20%), Parameter Semantics (15%), Conciseness & Structure (10%), and Contextual Completeness (10%). The server-level definition quality score is calculated as 60% mean TDQS + 40% minimum TDQS, so a single poorly described tool pulls the score down.

Server Coherence evaluates how well the tools work together as a set, scoring four dimensions equally: Disambiguation (can agents tell tools apart?), Naming Consistency, Tool Count Appropriateness, and Completeness (are there gaps in the tool surface?).

Tiers are derived from the overall score: A (≥3.5), B (≥3.0), C (≥2.0), D (≥1.0), F (<1.0). B and above is considered passing.

Tool Scores

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.
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.
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.
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.
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.

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