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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_text_to_speech

Converts text to speech using the tenant’s TTS model and returns base64-encoded audio for playback or storage.

Instructions

Turn text into speech with the tenant's TTS model; returns the audio as base64.

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": "ragflow_text_to_speech"
  }
}

Input Schema

NameRequiredDescriptionDefault
textYesText to read aloud

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

B3.4/5.0
Behavior3/5

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

Annotations declare the safety profile (not read-only, non-destructive, closed-world), so the description need not restate it. It usefully adds that the response is base64-encoded audio from the tenant's model, but says nothing about input size limits, latency, cost, or failure modes of a generation call.

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?

A single sentence that front-loads the action and ends with the return format. Every clause earns its place; there is no filler.

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?

An output schema exists and the description still adds the key detail an agent needs (base64 audio). For a one-parameter tool this is nearly complete; only practical limits on the text input and voice selection are unaddressed.

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?

Only one parameter exists and schema coverage is 100%, with the schema describing it as "Text to read aloud". The description adds no constraint on format, length, or resolution beyond that, so the baseline 3 applies.

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 ("Turn text into speech") and names the model source (tenant's TTS model), which implicitly separates it from the sibling ragflow_speech_to_text. It stops short of explicitly naming that sibling or any exclusions, so it is clear rather than exemplary.

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?

There is no guidance on when to use this versus alternatives, no note on text length limits, no mention of which voice/model is selected or whether it can be configured. Usage is only inferable from the function name and description.

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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