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Transcribe to Markdown

transcribe_to_markdown

Convert audio/video files into Markdown transcripts through Frenchie. Set language (ISO 639-1 code, e.g. 'th', 'en', 'ja') for better accuracy; omit for auto-detection.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNoOptional Frenchie API key (fr_...). Falls back to the FRENCHIE_API_KEY env var when omitted.
languageNoOptional ISO 639-1 language code (e.g. 'th', 'en', 'ja') for better accuracy; omit for auto-detection.
file_pathNoAbsolute local file path. Stdio transport only — over HTTP, upload via upload_file first and pass uploaded_file_reference instead.
uploaded_file_referenceNoObject key returned by upload_file. Required for HTTP transport; ignored when file_path is provided in stdio.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
jobIdNo
resultNo
statusYes
creditsUsedNo
resultExpiresAtNo
estimatedCompletionNo

TDQS

A4/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=false and destructiveHint=false, providing minimal safety info. The description adds the behavioral detail that language can be set for accuracy and omitted for auto-detection, which is a useful context but not exhaustive. It does not mention processing behavior, output storage, or dependencies like upload_file for HTTP. Given the annotation coverage, the description adds some value but lacks deeper transparency.

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 two sentences, front-loaded with the core purpose and followed by a focused parameter tip. Every sentence earns its place with no unnecessary fluff. The structure is clear and scannable, making it an excellent example of concise writing.

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?

Given the rich schema (100% parameter descriptions) and the presence of an output schema, the description doesn't need to explain return values or parameter details. However, it omits the workflow dependency on upload_file for HTTP transport, which is a key contextual aspect for a tool operating in two transport modes. Since this is covered in the schema but not in the high-level description, the completeness is good but not perfect, earning a 4.

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?

The schema provides descriptions for all four parameters, achieving 100% coverage. The description restates the language parameter guidance ('Set language...') without adding new information beyond the schema. The mutual exclusivity/transport-specific behavior of file_path vs uploaded_file_reference is already documented in the schema. Thus, the description adds minimal semantic value beyond the structured schema.

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 the tool converts audio/video files into Markdown transcripts, specifying the input type and output format. This distinguishes it from sibling tools like ocr_to_markdown (likely image-based) and extract_to_markdown (potentially text extraction). The verb 'convert' and explicit resource 'audio/video files' make the purpose unambiguous.

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?

The description provides clear context for when to use the tool: for audio/video transcription to Markdown. It does not explicitly exclude alternatives or name sibling tools, but the resource type implies the usage. The language parameter hint is practical but not a usage guideline. No when-not-to-use guidance is given, so it falls short of a 5.

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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TDQS

A4/5.0
Disambiguation5/5

Each tool has a clearly distinct purpose: extracting structured documents, OCR, transcription, image generation, file upload, result file download, and job status retrieval. The descriptions provide clear boundaries, so an agent can easily select the right tool.

Naming Consistency5/5

Tool names follow a consistent snake_case pattern with verb-first names (e.g., extract_to_markdown, generate_image, upload_file). Conversion tools share the _to_markdown suffix, while utility tools use verb_noun, creating a predictable and readable convention.

Tool Count5/5

Seven tools is well-scoped for a file conversion and image generation server. Each tool addresses a distinct need without redundancy or bloat, making the set feel complete yet focused.

Completeness4/5

The tool surface covers the main workflows: converting documents, images, audio/video to Markdown, generating images, and handling file uploads and downloads. A minor gap is that get_job_result only fetches the latest job, lacking support for retrieving specific job results by ID, which could hinder multi-job workflows.