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SuanLinFang — STT / TTS / OCR / Decision APIs

Submit Speech Task

submit_speech_task

提交长文本语音合成任务(≤20 万字):平台按 500 字切块合成并拼接为完整音频。 提交后用 get_speech_task 轮询结果。计费约 2 元/万字符。

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "text": "<text>"
    },
    "name": "submit_speech_task"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYes
speakerNodefault
audio_formatNowav

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

With no annotations provided, the description carries the full burden and does well: it discloses the asynchronous task model, the internal 500-character chunking and concatenation behavior, the hard input limit, and an approximate cost of 2 yuan per 10,000 characters. Not covered are permission/authorization requirements, whether failed chunks can be retried, or how partial failures are reported, which keeps it from a 5.

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?

Three compact clauses, front-loaded with the action and scope, followed by the follow-up call and the cost note. Nothing is redundant and no sentence could be dropped without losing information.

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 an asynchronous, paid submission tool with an output schema already documenting the return value, the description covers the essentials: size limit, chunking, polling follow-up, and cost. The remaining gap is documentation of the two optional parameters, which neither the schema nor the description explains.

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 0%, so the description must compensate. It does add substantial meaning for the required 'text' parameter (maximum length, 500-character chunking, and character-based billing), but 'speaker' and 'audio_format' are never mentioned — only the enum and defaults in the schema hint at their role.

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?

States a specific verb and resource ('提交长文本语音合成任务') and pins down the scope with an explicit size ceiling (≤20万字) plus the chunk-and-concatenate mechanism, so an agent immediately knows this is an asynchronous long-form synthesis submission. It does not, however, name the sibling it is not — a short-form tool like synthesize_speech is only implied by the word '长文本'.

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?

Gives a clear usage condition (long text, up to 200k characters) and an explicit next step: poll with get_speech_task after submitting. It stops short of stating when NOT to use it — e.g. that short text should go to a synchronous synthesis tool or that audio-to-text belongs to submit_transcription_task — so the agent must infer the boundaries from the size hint 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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