MCP Fish Server
MCP Fish Server lets you generate Fish Audio TTS and explore voice models through AceDataCloud.
Generate speech from text using Fish Audio models (
s1,s2-pro,s2.1-pro) with controls for format, bitrate, sample rate, prosody, latency, and more.Perform one-shot voice cloning with a public HTTPS reference audio URL plus its exact transcript.
Use saved or public voices by passing a
reference_idto audio generation.List, search, and paginate through available Fish voice models, including your own saved voices.
Fetch metadata for a specific voice model by ID.
Submit asynchronous generation tasks, optionally with a callback URL, and poll for results.
Query one task or batch-query multiple task statuses/results at once.
Retrieve a usage guide for the Fish TTS tools.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@MCP Fish ServerConvert this text to speech: Welcome to Fish Audio."
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
MCP Fish Server
A Model Context Protocol (MCP) server for Fish Audio TTS (Text-to-Speech) via the AceDataCloud platform. Generate natural-sounding speech and explore the Fish voice model library.
Features
High-quality TTS: Generate speech from text via Fish Audio models
Voice library: Browse, search, and fetch metadata for Fish voice models
Asynchronous tasks: Submit generation tasks and poll for results
Batch task lookup: Query multiple task results in one call
One-shot voice cloning
Pass one public HTTPS reference audio URL plus its exact transcript. This conditions only the current TTS request and does not create a reusable voice model:
fish_generate_audio(
text="New speech in the referenced voice",
reference_audio_url="https://cdn.acedata.cloud/reference.mp3",
reference_text="The exact words spoken in the reference audio",
)Use reference_id for saved or public voices, and the one-shot reference fields for a temporary voice. Do not combine them. Reference audio supports MP3/WAV and should be 10–270 seconds. Billing remains based on the target text's UTF-8 byte count.
Find saved voices
Call fish_list_models with self=true to list voices created and saved through
AceDataCloud by the calling account, including those created with its other API
tokens. This is a list filter, not a privacy setting: saved voices are public and
may be searched and used by others. The voice-creation API's visibility input
does not make them private or unlisted; this MCP does not expose voice creation.
Use page_size and the 1-based page_number for pagination. With self=true,
sort_by supports created_at and task_count, and author_id further filters
those saved voices. title_language is only supported for public library queries,
not with self=true. The returned has_more is a boolean for account-created
voices but may be null for public library queries; use total and page_size
for pagination in that case. Pass a returned voice ID as reference_id to
fish_generate_audio.
Related MCP server: Fish Audio MCP Server
Connect: hosted OAuth, API token, or local stdio
The hosted endpoint is https://fish.mcp.acedata.cloud/mcp. Choose one route for the MCP client:
Route | When to use it | Credential setup |
Hosted OAuth | The client supports remote MCP OAuth | Add only the URL, then sign in to AceDataCloud and approve access. No token needs to be pasted into client configuration. |
Hosted API token | The client cannot finish OAuth, or you need an explicit integration credential | Send an AceDataCloud API token in the |
Local stdio | The client runs a local MCP process | Install |
The hosted service advertises OAuth metadata and Dynamic Client Registration (DCR). DCR registers the client application; it is not an API key. OAuth signs you in and the client sends the resulting Bearer token; it may reuse or create an API credential for the account. Browser sign-in still requires an AceDataCloud account. The hosted service can be metered: review current service documentation and displayed pricing before a real operation. Do not configure both an OAuth login and a fixed Authorization header for the same server.
Hosted OAuth examples
Claude and Claude Desktop chat: Add a remote custom connector in
Customize → Connectors → Add custom connector, enterhttps://fish.mcp.acedata.cloud/mcp, select sign-in, and choose Register automatically if Claude asks how to register its OAuth client. Complete consent. Claude Desktop's localclaude_desktop_config.jsonis a separate setup. Claude connector guide.Claude Code:
claude mcp add --transport http --scope user fish https://fish.mcp.acedata.cloud/mcp, thenclaude mcp login fish. Check/mcp. Claude Code MCP guide.Cursor: Add a remote server with only
https://fish.mcp.acedata.cloud/mcp. For a project, merge the entry below into<project>/.cursor/mcp.json; for personal use, use~/.cursor/mcp.json. Cursor MCP guide.VS Code / Copilot: Run MCP: Add Server, select HTTP, enter
https://fish.mcp.acedata.cloud/mcp, then finish the browser sign-in. New portable workspace configs use<project>/.mcp.json; the VS Code-specific format below uses<project>/.vscode/mcp.jsonor the user profile. Check MCP: List Servers. VS Code MCP setup.Codex:
codex mcp add fish --url https://fish.mcp.acedata.cloud/mcp, thencodex mcp login fish. Its user settings are in~/.codex/config.toml. Official Codex MCP guide.
Cursor project config (OAuth):
{
"mcpServers": {
"fish": {"url": "https://fish.mcp.acedata.cloud/mcp"}
}
}VS Code-specific workspace config (OAuth):
{
"servers": {
"fish": {"type": "http", "url": "https://fish.mcp.acedata.cloud/mcp"}
}
}Hosted API token
Open Ace Data Cloud → Applications. Sign in, or register an account first.
Find General application. If it has not been created, choose Initialize General Application. A general key can use the services available to your account; a dedicated application's key is limited to its service. Check the required service/model access and current pricing.
Copy the API Key from the application card, or open Keys / Manage Keys → Create to create a separate key for this integration. Give it a recognizable name so you can revoke or rotate it independently.
When creating a key, review Expiration Time, Set Usage Limitation, and Allowed APIs. Enable only the restrictions you need, allow the operations you will call, then click Create and copy the token from the key list. Task lookup does not grant permission to submit a generation. Confirm the application's balance can cover your first request; any trial allowance is whatever your account currently displays.
Copy only the token value, without quotation marks, spaces, or
Bearer. An API Key / API Token field expects this raw value; only a manually configured HTTPAuthorizationheader needs theBearerprefix. Aplatform-...account-management token cannot replace a generation API key.


Save the key in the client's credential store or the environment variable shown below. Never put it in a prompt, shared workflow, screenshot, or Git commit. If authentication fails, check the full copied value, application/service access, expiration, usage limit, and balance before creating another key.
A fixed Bearer header bypasses OAuth. If the header is invalid, remove it before trying browser sign-in; keep the saved credential private.
For Claude Code, the shell expands the token when you add the server; treat the saved user MCP config as a secret:
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
claude mcp add --transport http --scope user fish https://fish.mcp.acedata.cloud/mcp \
--header "Authorization: Bearer $ACEDATACLOUD_API_TOKEN"For a Claude Code project config, put a variable reference in <project>/.mcp.json and set that variable in the environment that launches Claude Code:
{
"mcpServers": {
"fish": {
"type": "http",
"url": "https://fish.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${ACEDATACLOUD_API_TOKEN}"}
}
}
}Cursor uses a different environment-variable syntax in ~/.cursor/mcp.json or an uncommitted project config:
{
"mcpServers": {
"fish": {
"url": "https://fish.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${env:ACEDATACLOUD_API_TOKEN}"}
}
}
}In VS Code, run MCP: Open User Configuration and merge this server plus its masked input; ${input:...} is for VS Code's user/workspace format and is not portable to the Agent Host .mcp.json format:
{
"inputs": [
{"id": "acedata-fish-token", "type": "promptString", "description": "AceDataCloud API token", "password": true}
],
"servers": {
"fish": {
"type": "http",
"url": "https://fish.mcp.acedata.cloud/mcp",
"headers": {"Authorization": "Bearer ${input:acedata-fish-token}"}
}
}
}For Cline, use its MCP configuration UI or CLI file ~/.cline/data/settings/cline_mcp_settings.json; its remote transport value is streamableHttp. For JetBrains AI Assistant, add a remote URL from Settings → Tools → AI Assistant → Model Context Protocol (MCP). For Zed, use a context_servers entry with the URL only for OAuth or add a local Bearer header. These clients have different configuration schemas; follow their current UI rather than copying another client's JSON. Cline · JetBrains · Zed.
Local stdio
Install the package and give the local process an API token:
python -m pip install mcp-fish
export ACEDATACLOUD_API_TOKEN='YOUR_API_TOKEN'
mcp-fishFor Claude Desktop local MCP, merge this entry into the file opened by its developer settings (~/Library/Application Support/Claude/claude_desktop_config.json on macOS). uvx requires uv on PATH:
{
"mcpServers": {
"fish": {
"command": "uvx",
"args": ["mcp-fish"],
"env": {"ACEDATACLOUD_API_TOKEN": "YOUR_API_TOKEN"}
}
}
}Keep this user-level file private. Self-hosted HTTP uses mcp-fish --transport http --port 8000; expose it only with suitable network and TLS controls. Local execution still calls the AceDataCloud API.
Check before using the service
https://fish.mcp.acedata.cloud/healthreturning{"status":"ok"}checks endpoint reachability only.Confirm that the MCP client loads tools.
fish_get_usage_guideis a reference tool; it does not verify downstream API access or balance.If you need a full API check, call
fish_generate_audiowith your own valid input after reviewing current service documentation and displayed pricing. If the result contains a task ID, callfish_get_taskon that same ID until terminal success or failure. Do not resubmit the operation just to check progress.
For 401, check which auth route the client used and whether the token or OAuth session is valid. A 403 may mean an account permission or content moderation failure; read the returned error. Insufficient balance and downstream service failures need their own diagnosis. A listed tool or submitted task does not prove a successful result.
Tool Reference
Tool | Description |
| Generate speech from text via a Fish voice model |
| List available Fish voice models |
| Fetch metadata for a specific Fish voice model |
| Get the status / result of a generation task |
| Batch-fetch the status / result of multiple tasks |
| Get the API usage guide |
Documentation
License
MIT — see LICENSE at the repository root.
Available Tools
6 toolsfish_generate_audioBInspect
Generate speech audio from text using Fish TTS.
Use this when:
- You want to convert text to speech
- You need AI-generated audio narration
Returns:
JSON response containing task_id and audio data when complete.
Example:
fish_generate_audio(
text="Hello, welcome to our service!",
reference_id="d7900c21663f485ab63ebdb7e5905036"
)
| Name | Required | Description | Default |
|---|---|---|---|
| text | No | The text to synthesize. Required. | |
| async | No | Whether to process the request asynchronously. | |
| model | No | The TTS model to use. Supported values: 's1', 's2-pro', 's2.1-pro'. | s2-pro |
| top_p | No | Top-p nucleus sampling parameter. | |
| format | No | Output audio format. Defaults to 'mp3'. | mp3 |
| prompt | No | Deprecated alias for `text`. | |
| latency | No | Latency mode. Supported values: 'normal', 'balanced'. | |
| prosody | No | Prosody overrides forwarded to the upstream. | |
| voice_id | No | Deprecated alias for `reference_id`. | |
| normalize | No | Whether the upstream should apply text normalization. | |
| references | No | One-shot voice clone reference: one object with a public HTTPS audio URL and the exact transcript. Cannot be combined with reference_id. | |
| mp3_bitrate | No | MP3 bit rate when format='mp3'. Supported values: 64, 128, 192. | |
| sample_rate | No | Sampling rate of the output audio (e.g. 16000, 22050, 44100). | |
| temperature | No | Sampling temperature (0.0-1.0). | |
| callback_url | No | Optional callback URL to receive the result asynchronously. If provided, the API returns immediately with a task_id. | |
| chunk_length | No | Chunk length passed through to the upstream synthesiser. | |
| reference_id | No | Voice model id, or a list of voice model ids. Default example voice model ID: d7900c21663f485ab63ebdb7e5905036. | |
| max_new_tokens | No | Maximum number of new tokens to generate. | |
| reference_text | No | Exact transcript for reference_audio_url. | |
| min_chunk_length | No | Minimum chunk length. | |
| repetition_penalty | No | Repetition penalty applied during generation. | |
| reference_audio_url | No | Convenience HTTPS audio URL for one-shot voice cloning. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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 the JSON return shape but omits critical behavior such as async/callback_url returning a task_id before audio is ready and the need to poll fish_get_task for results. This is a significant transparency gap for a complex synthesis API.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The first sentence is an efficient purpose statement and the example is useful. However, the 'Use this when' bullets largely restate the first sentence ('convert text to speech' is nearly identical to 'generate speech audio from text'), adding redundancy without new information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
This is a complex tool with 22 optional parameters, asynchronous modes, and voice-cloning options, yet the description only covers the basic synchronous path. It omits operational guidance for async/callback_url, references/reference_audio_url, and retrieving results via fish_get_task, so an agent could easily misuse the tool for non-default use cases.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the baseline is 3. The example adds practical semantics for text and reference_id, but the description does not explain parameter relationships like references vs reference_id or async vs callback_url, which the schema also leaves implicit.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific action ('Generate speech audio from text') and the service ('Fish TTS'), which clearly distinguishes it from the sibling get/list tools. The 'Use this when' bullets reinforce the purpose and leave no doubt about what the tool is for.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
Provides explicit 'Use this when' conditions for text-to-speech and narration, giving clear invocation context. It does not name alternatives or exclusions, but no sibling tool generates audio, so the guidance is sufficient.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
fish_get_modelAInspect
Get a Fish voice model by ID.
Returns:
JSON response from /fish/model/{id}.
| Name | Required | Description | Default |
|---|---|---|---|
| model_id | Yes | The unique identifier of the voice model to fetch. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
fish_get_taskAInspect
Query the status and result of a Fish audio generation task.
Use this to check if a task is complete and retrieve the resulting audio URL.
Task states:
- 'pending': Task is queued — keep polling
- 'processing': Task is being processed — keep polling
- 'complete': Task finished successfully
- 'failed': Task failed (check error message)
Returns:
Task status and result data including audio URL.
| Name | Required | Description | Default |
|---|---|---|---|
| task_id | Yes | The task ID returned from a fish_generate_audio request. This is the 'task_id' field in the response. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
fish_get_tasks_batchAInspect
Query the status of multiple Fish tasks at once.
Efficiently checks the status of multiple audio generation tasks in a
single request.
Returns:
Status and result data for all queried tasks.
| Name | Required | Description | Default |
|---|---|---|---|
| task_ids | Yes | List of task IDs to query status for. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
fish_get_usage_guideAInspect
Get a comprehensive guide for using the Fish TTS tools.
Provides detailed information on how to use the Fish tools effectively,
including parameters, examples, and best practices.
Returns:
Complete usage guide for Fish TTS tools.
| Name | Required | Description | Default |
|---|---|---|---|
No parameters | |||
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
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.
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.
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.
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.
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.
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.
fish_list_modelsCInspect
List Fish voices; has_more is boolean for self=true, otherwise possibly null.
| Name | Required | Description | Default |
|---|---|---|---|
| tag | No | Filter by a single tag. | |
| self | No | When true, only list voices created and saved through this platform by the calling account, across its API tokens. This does not restrict public use. | |
| title | No | Filter by partial title match. | |
| sort_by | No | Sort field; self=true supports created_at and task_count. | |
| language | No | Filter by language code (e.g. en, zh). | |
| author_id | No | Filter by author id. | |
| page_size | No | Number of items per page. Defaults to 10. | |
| page_number | No | 1-based page number. Defaults to 1. | |
| title_language | No | Filter the public voice library by title language; not with self=true. |
Output Schema
| Name | Required | Description |
|---|---|---|
| result | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It discloses one nuance (has_more is boolean only when self=true, otherwise possibly null), but that is a return-shape detail already implied by the output schema, and it says nothing about pagination limits, auth requirements, or read-only safety.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
A single, front-loaded sentence with no filler; the verb+resource leads. It is efficient, though the second clause about has_more reads as an afterthought rather than essential information.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a 9-parameter listing tool with no annotations and several sibling retrieval tools, the description is too thin: it omits when to use it over fish_get_model and says nothing about the paging behavior that 9 optional filter params imply. The rich schema and output schema offset some of this, but routing guidance is absent.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents all 9 parameters, including the self/title_language interaction constraint. The description adds no parameter-level meaning beyond what the schema provides, which makes the baseline 3 appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
States a specific verb+resource ('List Fish voices'), which is clearer than the ambiguous name 'list_models' and tells the agent this returns a collection of voices. However, it does not distinguish the tool from the sibling fish_get_model (single-voice retrieval) or explain what a 'voice/model' is in this context.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no statement of when to use this tool versus fish_get_model, nor any prerequisites or exclusions. The only conditional note (has_more semantics under self=true) is about return values, not usage selection, so the agent gets no routing guidance.
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.
1 tool update
v0.1.8- Changed
fish_list_models3 fields changed- changed
Input schema / properties / self / descriptionPrevious value: -"When true, only return models owned by the calling account."New value: +"When true, only list voices created and saved through this platform by the calling account, across its API tokens. This does not restrict public use." - changed
Input schema / properties / sort_by / descriptionPrevious value: -"Sort by field accepted by upstream (e.g. created_at, task_count)."New value: +"Sort field; self=true supports created_at and task_count." - changed
Input schema / properties / title_language / descriptionPrevious value: -"Filter by title language."New value: +"Filter the public voice library by title language; not with self=true."
1 tool update
v0.1.3- Changed
fish_generate_audio7 fields changed- added
Input schema / $defsAdded value: +{ + "FishReference": { + "description": "One-shot voice reference hosted at a public HTTPS URL.", + "properties": { + "audio": { + "format": "uri", + "maxLength": 2083, + "minLength": 1, + "title": "Audio", + "type": "string" + }, + "text": { + "maxLength": 10000, + "minLength": 1, + "title": "Text", + "type": "string" + } + }, + "required": [ + "audio", + "text" + ], + "title": "FishReference", + "type": "object" + } +} - added
Input schema / properties / reference_audio_urlAdded value: +{ + "anyOf": [ + { + "format": "uri", + "maxLength": 2083, + "minLength": 1, + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Convenience HTTPS audio URL for one-shot voice cloning.", + "title": "Reference Audio Url" +} - changed
Input schema / properties / reference_id / anyOfPrevious value: -[ - { - "type": "string" - }, - { - "type": "null" - } -]New value: +[ + { + "type": "string" + }, + { + "items": { + "type": "string" + }, + "type": "array" + }, + { + "type": "null" + } +] - changed
Input schema / properties / reference_id / descriptionPrevious value: -"Voice model id (single speaker). Default example voice model ID: d7900c21663f485ab63ebdb7e5905036."New value: +"Voice model id, or a list of voice model ids. Default example voice model ID: d7900c21663f485ab63ebdb7e5905036." - added
Input schema / properties / reference_textAdded value: +{ + "anyOf": [ + { + "maxLength": 10000, + "minLength": 1, + "type": "string" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Exact transcript for reference_audio_url.", + "title": "Reference Text" +} - changed
Input schema / properties / references / anyOfPrevious value: -[ - { - "items": { - "additionalProperties": true, - "type": "object" - }, - "type": "array" - }, - { - "type": "null" - } -]New value: +[ + { + "items": { + "$ref": "#/$defs/FishReference" + }, + "maxItems": 1, + "minItems": 1, + "type": "array" + }, + { + "type": "null" + } +] - changed
Input schema / properties / references / descriptionPrevious value: -"Inline reference samples forwarded to the upstream."New value: +"One-shot voice clone reference: one object with a public HTTPS audio URL and the exact transcript. Cannot be combined with reference_id."
2 tool updates
v0.1.1- Changed
fish_generate_audio2 fields changed- added
Input schema / properties / asyncAdded value: +{ + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "Whether to process the request asynchronously.", + "title": "Async" +} - changed
Input schema / properties / mp3_bitrate / anyOfPrevious value: -[ - { - "type": "integer" - }, - { - "type": "null" - } -]New value: +[ + { + "enum": [ + 64, + 128, + 192 + ], + "type": "integer" + }, + { + "type": "null" + } +]
- Changed
fish_list_models2 fields changed- added
Input schema / properties / selfAdded value: +{ + "anyOf": [ + { + "type": "boolean" + }, + { + "type": "null" + } + ], + "default": null, + "description": "When true, only return models owned by the calling account.", + "title": "Self" +} - removed
Input schema / properties / self_onlyRemoved value: -{ - "anyOf": [ - { - "type": "boolean" - }, - { - "type": "null" - } - ], - "default": null, - "description": "When true, only return models owned by the calling account.", - "title": "Self Only" -}
6 tool updates
v0.1.0- First observed
fish_generate_audio - First observed
fish_get_model - First observed
fish_get_task - First observed
fish_get_tasks_batch - First observed
fish_get_usage_guide - First observed
fish_list_models
TDQS
Scored across 6 tools
Each tool has a clearly distinct purpose: listing voices, fetching a single voice, generating audio, polling a single task, polling batches, and fetching documentation. The list/get model and single/batch task pairs are cleanly separated by noun and cardinality.
All tools use a consistent fish_ prefix followed by a verb_noun snake_case pattern (list_models, generate_audio, get_model, get_task, get_tasks_batch, get_usage_guide). Naming is fully predictable.
Six tools is well-scoped for a TTS wrapper service, covering discovery, generation, async polling, and help. Every tool earns its place with no redundancy.
The TTS lifecycle is well covered: model discovery, audio generation, and both single and batch task polling along with a usage guide. Minor gaps exist (e.g., no voice customization/creation or delete operations), but core workflows are fully supported.
Maintenance
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