Skip to main content
Glama
agmonetti
by agmonetti

export_plan

Export Markdown implementation plans or audits to styled PDF, high-res PNG, or self-contained HTML, keeping code highlighting, Mermaid diagrams, and GitHub callouts.

Instructions

Export an AI agent implementation plan or audit (Markdown) to styled PDF, high-res PNG, or self-contained HTML matching IDE aesthetics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
inputYesPath to the markdown file (relative or absolute) OR raw markdown text content
themeNoVisual theme: "light" (GitHub Light) or "dark" (GitHub Dark). Default: "light"
formatsNoList of export formats. Default: ["png", "pdf"]
outputDirNoDirectory where exported files will be saved. Default: "./exports"
outputNameNoBase filename for exports without extension. Default: derived from input file or "plan"

Schema Changelog

Changes observed during successful MCP inspections. Dates show when Glama detected each change.

  1. First observedv0.1.0

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 disclosure burden. It conveys the core behavior—converting Markdown to styled PDF/PNG/HTML—but does not mention that files will be written to an output directory, possible overwrite behavior, or that outputName/outputDir defaults apply.

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 a single, information-dense sentence with no filler. It front-loads the input type and then enumerates output formats and styling, so every clause earns its place.

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?

The tool is simple and the schema covers all parameters, but with no annotations and no output schema the description still leaves gaps: no mention of file-system side effects, no guidance on default output naming, and no cue about when render_diagram would be the better choice.

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 input schema already explains all five parameters including defaults. The description adds only context about styling ('matching IDE aesthetics'), not new parameter-level meaning, keeping this at the baseline.

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?

The description names a specific verb ('Export'), resource ('AI agent implementation plan or audit (Markdown)'), and target formats (PDF, PNG, HTML), so an agent knows what the tool produces. It does not explicitly contrast with render_diagram, but the Markdown-plan/audit focus is enough to avoid confusion for a typical call.

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 gives clear context for when to use the tool: whenever an implementation plan or audit in Markdown needs to be exported to a styled document format. It does not state exclusions or explicitly name the sibling as an alternative, which prevents a 5.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/agmonetti/plan-export-mcp'

If you have feedback or need assistance with the MCP directory API, please join our Discord server