MCP Fish Server

Server Quality Checklist

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Latest release: v0.1.1
Disambiguation5/5
Each tool serves a clearly distinct purpose: generation, task querying (single and batch), model listing/retrieval, and usage guidance. The only similar pair is get_task vs get_tasks_batch, but singular vs plural makes the distinction clear.
Naming Consistency4/5
All tools use snake_case with a consistent fish_ prefix and a verb_noun pattern, such as generate_audio, get_model, get_task, list_models. Minor variation exists between 'get' and 'list' for retrieval operations, but this is a common and readable convention.
Tool Count5/5
Six tools cover the core concerns of a TTS-specific server: generation, status polling, model access, and usage documentation. This is a focused, well-scoped toolset without unnecessary bloat.
Completeness4/5
The surface covers the main workflow: generate audio, poll task status, and fetch models. A task cancellation or full task history listing would improve completeness, but current tools handle the primary usage path without dead ends.
Average 3.8/5 across 6 of 6 tools scored. Lowest: 3/5.
See the Tool Scores section below for per-tool breakdowns.
- No community issues in the last 6 months
- 26 commits in the last 12 weeks
- Last stable release on August 18, 2026
- No critical vulnerability alerts
- No high-severity vulnerability alerts
- No code scanning findings
- CI is failing
This repository is licensed under MIT License.
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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?
With no annotations, the description must bear the full burden of explaining behavior. It implies a read-only operation by using 'List', but it does not explicitly state that no modifications occur, nor does it mention pagination, rate limits, or any potential side effects. The return statement ('JSON response') is minimal.
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 extremely concise, using only two sentences. This is efficient and avoids any fluff, making it easy for an agent to parse. The structure is clear: action + resource, followed by a straightforward return note.
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 output schema is undefined and the tool lists models, the description lacks essential context. It does not state that the response contains a list of models, nor does it mention that pagination is supported (despite pagination parameters existing). This leaves the agent guessing about the structure and breadth of the response.
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 schema covers all 9 parameters with descriptions, meeting the high coverage baseline. However, the description adds no extra parameter context—it merely repeats the endpoint. Since schema coverage is 100%, the baseline of 3 is appropriate, but the description could have clarified how these parameters interact (e.g., filtering by tag vs. language).
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the action ('List') and the resource ('available Fish voice models'), which is unambiguous. It effectively distinguishes this tool from sibling tools like fish_generate_audio and fish_get_model, which 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 Guidelines1/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool versus the alternatives. The description does not mention that this should be used for browsing models, or that fish_get_model should be used for a specific model, leaving the selection criteria entirely unaddressed.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It mentions the return format (JSON with task_id and audio data) and shows an example, which is helpful. However, it doesn't disclose potential side effects (e.g., cost, rate limits, async behavior details) or clarify that the tool may return immediately with a task_id when async is used. The description adds some value but could be more transparent about asynchronous behavior and error cases.
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 concise and well-structured with a clear purpose, usage conditions, return format, and example. It's front-loaded with the main purpose and uses bullet points for readability. The example is useful but could be slightly trimmed. Overall, it's efficient and earns its place.
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?
Given the tool's complexity (20 parameters, async options, multiple models), the description is somewhat minimal. It doesn't explain the async behavior, callback_url usage, or how to choose between models. The output schema exists, so return values are covered, but the description could provide more context on when to use async vs sync, and how to handle the task_id for polling. It's adequate but not comprehensive for such a complex tool.
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 schema description coverage is 100%, so the schema already documents all 20 parameters. The description adds minimal extra meaning beyond the schema, only mentioning the example reference_id and the text parameter. It doesn't explain relationships between parameters (e.g., async vs callback_url) or provide guidance on which parameters are commonly used together. Baseline 3 is appropriate since the schema does the heavy lifting.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: 'Generate speech audio from text using Fish TTS.' It specifies the verb (generate), resource (speech audio), and the technology (Fish TTS). It also distinguishes from siblings by focusing on audio generation, while siblings like fish_get_task or fish_list_models handle retrieval and listing.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides explicit 'Use this when' conditions: converting text to speech and needing AI-generated audio narration. It doesn't explicitly mention when not to use it or alternatives, but the sibling tools (e.g., fish_get_task for retrieval) imply a clear separation. The guidance is clear but lacks explicit exclusions.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description must disclose behavior. It states that the tool returns a 'JSON response from /fish/model/{id}', giving a minimal glimpse into the HTTP call and return format. However, it does not mention any side effects, error conditions, or required permissions, which is a minor gap for a read-only operation but acceptable given the simplicity.
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 extremely concise, consisting of two short sentences totaling under 20 words. It efficiently conveys the purpose and return type without any unnecessary prose.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the tool's trivial complexity, a complete parameter schema, and the presence of an output schema, the description is mostly sufficient. It clearly identifies the action and the endpoint. However, it lacks a mention of how this relates to sibling tools like fish_list_models, which would help an agent decide when to use this tool. This is a minor gap, but otherwise the description covers all essential context.
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 schema covers the single parameter model_id with a clear description, so the schema fully explains its meaning. The tool description adds no additional information about the parameter beyond that, meeting the baseline but providing no extra value.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the function: 'Get a Fish voice model by ID.' This uses a specific verb and resource, and the 'by ID' explicitly distinguishes it from sibling tools like fish_list_models. The return note about the endpoint adds clarity without ambiguity.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines3/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description implies usage when you need a specific model by ID, but it gives no explicit guidance about when to use this tool versus alternatives like fish_list_models or fish_get_task. It does not mention any exclusions or when to prefer alternatives, so the usage context is only inferred from the tool's name and parameter requirements.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the burden. It discloses task states and result behavior (including audio URL and error message), but omits details like whether the operation is read-only, possible 404 errors, or polling frequency limits.
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 well-structured with a clear opening sentence, bullet-point task states, and a concise return summary. No redundant information; every sentence adds value.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a low-complexity status-check tool with an output schema available, the description covers essential aspects: purpose, states, and expected result. It lacks a note about error handling (e.g., task not found), but this is minor given the schema presence.
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?
There is only one parameter, task_id, and the schema provides full description (100% coverage) including its source from fish_generate_audio. The tool description adds no extra parameter context, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Query the status and result of a Fish audio generation task' with a specific verb and resource. The singular 'a task' differentiates it from sibling fish_get_tasks_batch, and the mention of retrieving audio URL clarifies its purpose.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
Explicitly instructs 'Use this to check if a task is complete and retrieve the resulting audio URL' and provides polling guidance via task states ('keep polling'). However, it does not mention alternatives or when not to use it.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description carries the full burden. It discloses the read-only nature ('Query') and return contents, but does not detail error behavior, rate limits, or potential side effects. This is adequate for a status query but lacks depth.
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 three sentences with no fluff, front-loads the primary purpose in the first sentence, and every sentence conveys meaningful information (purpose, efficiency, return data).
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the simple tool (one parameter, output schema provided), the description is sufficiently complete: it states what the tool does and what it returns. It could have explicitly mentioned the singular alternative or any limits, but this is not necessary for basic 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 schema provides 100% coverage for the single parameter task_ids ('List of task IDs to query status for'). The description adds no extra semantics beyond what the schema already states, so the baseline of 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Query the status of multiple Fish tasks at once' with a specific verb and resource, and distinguishes it from the singular sibling fish_get_task by emphasizing 'multiple' and 'batch' efficiency.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
It implies when to use this tool ('in a single request' for efficiency) versus repeatedly calling a single-task tool, but does not explicitly name the alternative fish_get_task or state exclusions. The context is clear enough for an agent to infer the batch use case.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Behavior3/5
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description must carry the full burden of behavioral disclosure. It states that the tool returns a 'Complete usage guide', which tells the user the primary output. It does not explicitly state that it is read-only or non-destructive, but that is obvious for a guide tool. It could mention that no state is changed, but the description adequately conveys its non-mutating nature.
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 concise and front-loaded with the primary purpose. The 'Returns:' line somewhat duplicates the initial statement but is short. Overall, every sentence contributes useful information, and there is little fluff.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Completeness4/5
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
The tool is a simple guide with no parameters and a self-explanatory output. An output schema exists but isn't detailed in the prompt, so the description's mention of 'parameters, examples, and best practices' gives adequate context about the content of the returned guide. It is complete enough for an agent to decide when to invoke it.
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?
This tool has zero parameters, and the input schema is empty. The baseline for zero-parameter tools is 4. The description adds no parameter-specific semantics because there are no parameters to explain, which is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Purpose5/5
Does the description clearly state what the tool does and how it differs from similar tools?
The description uses a specific verb ('Get') and a clear resource ('usage guide for Fish TTS tools'), and it is easily distinguished from sibling tools that perform actual TTS operations or model queries. It explicitly states this is a guide for using the Fish tools, making its purpose unambiguous.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Usage Guidelines4/5
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description clearly implies when to use this tool: when you need guidance on how to use the Fish TTS tools effectively. It mentions parameters, examples, and best practices, which signals the type of information provided. However, it does not explicitly mention alternatives or exclusions, but for a guide tool this context is sufficient.
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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