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convert_to_rst

Idempotent

Convert Markdown to reStructuredText by transforming headers to underlined text, code blocks to directives, and links to RST syntax. Supports GitHub-Flavored Markdown and KaTeX math expressions.

Instructions

Convert Markdown to reStructuredText (RST) format. Transforms headers to underlined text, code blocks to .. code-block:: directives, and links to RST reference syntax.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
markdownYesThe raw Markdown source text to convert. Supports GitHub-Flavored Markdown (tables, task lists, strikethrough) and KaTeX math expressions. Pass the full document content as a string, not a file path.
output_pathNoOptional. Absolute or relative file path (e.g. './output.txt') where the result will be saved. Parent directories are created automatically. If omitted, the converted text content is returned directly in the response as a string. If provided, the file is written to disk and a JSON summary with { success, file_path, file_size_bytes, format } is returned instead.
Behavior4/5

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

Annotations establish idempotentHint=true and destructiveHint=false. The description adds valuable transformation details (header underlining, code-block conversion, link syntax) that explain the conversion logic. It could improve by clarifying file overwrite behavior when output_path points to an existing file.

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 tight sentences with zero waste. The first establishes purpose; the second provides concrete transformation examples that earn their place by distinguishing RST output from other markup formats. Front-loaded and appropriately sized.

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 a conversion tool with 100% schema coverage and existing annotations, the description is complete. It covers input format expectations and transformation specifics. Could mention error handling for malformed Markdown, but not required given the tool's straightforward scope.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

With 100% schema coverage, the baseline is 3. The description adds semantic value by explaining what happens to the markdown content (specific transformation rules) and implicitly clarifying the dual output behavior (string return vs file write) mentioned in the schema, helping users understand the markdown parameter's processing.

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 states the specific action (Convert) and resource (Markdown to RST) clearly. It distinguishes from siblings by detailing RST-specific transformations (underlined headers, code-block directives, reference syntax), making it clear this produces Sphinx-compatible RST rather than other formats.

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?

While the RST-specific syntax details imply when to use this tool (when you need reStructuredText output), there is no explicit guidance comparing it to sibling converters like convert_to_html or convert_to_latex, nor warnings about when to prefer alternatives.

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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