fanar-mcp

fanar_transcribe

Transcribe Arabic dialects or English audio from local files into text, SRT, or JSON, with an option for long-form recordings.

Instructions

Transcribe Arabic (including dialects) or English audio from a local file with Fanar Aura. long_form=true uses the long-audio model (about 10 requests a day); otherwise about 20 a day.

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": {
      "audio_path": "<audio_path>"
    },
    "name": "fanar_transcribe"
  }
}

Input Schema

Name	Required	Default
`output`	No	text
`long_form`	No
`audio_path`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Oct 19, 2026

TDQS

A3.7/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses quota limits (~10/day with long_form, ~20/day otherwise) and the model switch, which an agent needs for planning. It still omits accepted audio formats, length/size limits for the short-form path, and any error or failure behavior, leaving meaningful gaps.

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?

Two tightly written sentences with zero filler; the core purpose is front-loaded and the quota/model trade-off follows immediately. Every clause carries actionable information.

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?

For a 3-param tool with no annotations and no output schema, the description covers purpose, model choice, and quotas adequately. It does not describe what each output format returns or the supported audio inputs, which an agent choosing between text/srt/json would want. Adequate but with clear gaps.

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 well explains long_form (which model it selects and the rate-limit consequence), which is the least obvious parameter. It says nothing about output (text/srt/json) beyond what the enum conveys and nothing about audio_path format or constraints, so compensation is partial.

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?

States a specific verb (Transcribe), resource (audio from a local file), and the languages supported (Arabic including dialects, or English), and names the backing model (Fanar Aura). No sibling does the same thing — fanar_text_to_speech is the reverse direction and fanar_translate is text-only — so an agent can select this without ambiguity.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The long_form sentence gives an implicit decision rule (use long_form for long audio, at the cost of fewer daily requests), which is genuine usage guidance. However, it never states when to prefer this over fanar_text_to_speech or fanar_translate, nor any prerequisites for the tool itself. Usage is implied rather than prescribed.

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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