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AceDataCloud

MCP Fish Server

by AceDataCloud

fish_list_models

List available Fish voice models from the API. Filter by language, tag, author, or title, and paginate results to browse and select models for text-to-speech tasks.

Instructions

List available Fish voice models from the API.

Returns:
    JSON response from /fish/model.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
tagNoFilter by a single tag.
titleNoFilter by partial title match.
sort_byNoSort by field accepted by upstream (e.g. created_at, task_count).
languageNoFilter by language code (e.g. en, zh).
author_idNoFilter by author id.
page_sizeNoNumber of items per page. Defaults to 10.
self_onlyNoWhen true, only return models owned by the calling account.
page_numberNo1-based page number. Defaults to 1.
title_languageNoFilter by title language.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
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 that the tool returns a JSON response from /fish/model, implying a read-only fetch, but omits details such as authentication requirements, rate limits, or pagination behavior. This is adequate but not rich.

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 and front-loaded with the primary action. The 'Returns' note adds useful endpoint information without filler, making every sentence earn its place.

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 simple list endpoint with a full output schema and thoroughly documented optional parameters, the description is mostly sufficient. However, it lacks any mention of use cases or explicit pagination/filtering behavior, which keeps it from being fully complete.

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%, so the baseline is 3. The description adds no parameter-specific semantics, but the schema thoroughly documents all 9 optional parameters including filters and pagination controls, so the description does not need to repeat them.

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 it lists available Fish voice models from the API, using a specific verb and resource. It differentiates from sibling fish_get_model by clearly implying a collection-level listing operation.

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?

No guidance is provided on when to use this tool versus alternatives like fish_get_model or fish_get_tasks. There are no exclusions, prerequisites, or context for choosing this tool.

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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