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ragflow-kb-mcp

ragflow-kb-mcp

by feci
OverviewSchemaSourceRelated ServersDiscussionsAdmin
Python
Hybrid

ragflow_speech_to_text

Transcribe an audio file into text using your tenant’s speech-to-text model. Provide a filename, server file path, or base64-encoded audio.

Instructions

Transcribe an audio file with the tenant's speech-to-text model.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "ragflow_speech_to_text"
  }
}

Input Schema

NameRequiredDescriptionDefault
filenameNoAudio file name with its extension (.wav, .mp3, .m4a, .aac, .flac, .ogg, .webm, .opus, .wma); defaults to the name from file_path
file_pathNoAudio file on the MCP server host, inside an allowed upload root
content_base64NoAudio bytes as base64 (small files only)

Output Schema

NameRequiredDescriptionDefault

No arguments

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.2.1

TDQS

C2.9/5.0
Behavior2/5

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

Annotations already declare non-read-only, open-world, non-destructive, so the safety profile is covered. The description adds nothing beyond them — no mention that it invokes an external/tenant model, no size or format constraints, no synchronous vs async behavior, no cost/latency expectations for a network-bound operation.

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?

A single front-loaded sentence with no filler. It is efficient, though its brevity is achieved partly by omitting necessary detail rather than by tight editing of rich content.

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?

An output schema exists, so return values needn't be explained. However, for an open-world transcription call with three optional input paths and zero required parameters, the description leaves key operational questions (which input to supply, size limits, error behavior) to the schema alone.

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 schema already explains filename fallback, allowed upload roots, and the base64 small-file caveat; baseline 3 applies. The description adds no meaning about mutual exclusivity or precedence among file_path, content_base64, and filename.

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 ('Transcribe an audio file') and names the resource owner of the model ('tenant's speech-to-text model'), which is enough to tell it apart from the sibling ragflow_text_to_speech. It stops short of stating scope (batch vs single file, synchronous vs job).

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 on when to use this versus alternatives, nor how it relates to ragflow_text_to_speech or ragflow_upload_file. Nothing tells an agent which of the three input modes to prefer or when this tool is the wrong choice.

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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