Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type @ followed by the MCP server name and your instructions, e.g., "@pdf-report-generator Generate a corporate PDF report with a bar chart"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
An MCP server that generates professional corporate PDF reports from structured JSON specs or raw LLM text output. Drop it into Claude Desktop (or any MCP client) and ask Claude to turn analysis, research, or meeting notes into a polished multi-page report complete with cover page, table of contents, executive summary, section headings, tables, and charts.
A sample output is at examples/sample_report.pdf.
Node.js 18+
Python 3.8+
Install Python dependencies:
pip install reportlab matplotlibRelated MCP server: rendoc
Add to your claude_desktop_config.json:
{
"mcpServers": {
"pdf-report": {
"command": "npx",
"args": ["-y", "pdf-report-generator"]
}
}
}generate_reportGenerates a PDF from a full structured spec.
Minimal example input:
{
"spec": {
"metadata": {
"title": "Q3 Performance Review",
"author": "Engineering Team",
"company": "Acme Corp",
"classification": "INTERNAL"
},
"executive_summary": "Overall performance improved this quarter...",
"sections": [
{
"heading": "Infrastructure",
"body": "Uptime reached 99.94%...",
"subsections": []
}
],
"tables": [],
"charts": []
}
}generate_report_from_textConverts raw text into a structured PDF report. Sections are auto-detected from headings.
{
"text": "# Overview\nThis quarter...\n\n# Key Findings\n...",
"title": "Q3 Summary",
"author": "Data Team",
"company": "Acme Corp",
"classification": "INTERNAL",
"theme_name": "navy"
}list_themesReturns available color themes: default, navy, charcoal, forest, burgundy.
metadata
title* string
subtitle string
author string
date string (YYYY-MM-DD; defaults to today)
company string
department string
document_id string (e.g. RPT-2026-001)
classification string (PUBLIC | INTERNAL | CONFIDENTIAL)
logo_path string (absolute path to PNG/JPG)
page_size "letter" | "a4"
executive_summary string
sections[]
heading* string
body* string (\n\n = paragraph break)
subsections[]
heading* string
body* string
tables[]
title string
headers* string[]
rows* string[][]
after_section int (0-based section index; -1 = after exec summary)
charts[]
title string
type "bar" | "line" | "pie" | "horizontal_bar"
labels* string[]
datasets* [{label, values[]}]
after_section int
images[]
path* string (absolute path)
caption string
width_inches number
after_section int
theme
primary_color [R, G, B]
accent_color [R, G, B]
highlight_color [R, G, B]"Turn this analysis into a professional internal PDF report titled 'Q3 Infrastructure Review'"
"Generate a corporate report from this research, add a bar chart for the monthly metrics"
"Create a CONFIDENTIAL report called 'Security Audit Findings' from this text"
"List the available report themes"
Python not found — ensure python or python3 is on your PATH and is version 3.8+.
reportlab not installed — run pip install reportlab matplotlib.
Charts missing — matplotlib is required for charts. Install it with pip install matplotlib.
Large PDFs — complex specs with many charts can take 5–15 seconds. This is normal.
MIT
Generate a professional PDF report from a structured JSON specification with metadata, sections, tables, charts, and images. Returns the PDF as base64-encoded data.
Inputs: spec (required).
Convert raw text or LLM chat output into a structured PDF report. Pass unstructured text and basic metadata — sections are detected automatically.
Inputs: text (required), title (required), author, company, +2 more.
List available color themes for PDF reports.
Inputs: no named parameters.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 3 tools
The two generate tools have distinct input types (raw text vs structured JSON) and clear descriptions, but their similar names could cause misselection if an agent reads quickly. list_themes is clearly separate.
All tools follow a consistent verb_noun pattern: generate_report_from_text, generate_report, list_themes. The names are predictable and descriptive.
With 3 tools, the server is slightly on the lean side but still within a reasonable scope. Each tool has a clear purpose and no redundant tools are present.
The server covers the main report generation needs: unstructured text, structured JSON, and theme customization. Minor gaps like additional output formats or template management exist but aren't critical for the core domain.
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