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AgentIndex
pdf-mcp logo
pdf-mcp
★ 155
vs
lightdash_mcp logo
lightdash_mcp
★ 24

pdf-mcp vs lightdash_mcp

pdf-mcp: pdf-mcp is a Model Context Protocol (MCP) server that enables AI agents to read, search, and extract content from PDF files. It uses PyMuPDF for PDF parsing, SQLite for persistent caching, and supports hybrid search combining BM25 keyword and semantic embeddings, OCR for scanned documents, and structured extraction of tables and images.; lightdash_mcp: Lightdash MCP Server allows AI assistants to interact with Lightdash analytics via the Model Context Protocol. It enables data discovery, chart creation, dashboard management, and ad-hoc querying programmatically. The server is installable via pip and integrates with Claude Desktop, Cursor, and other MCP clients.

01

TL;DR

pdf-mcp logo
pdf-mcpVision / Multimodal · ★ 155

Efficiently read and analyze large PDF documents without exceeding context limits

Read pdf-mcp full review →
lightdash_mcp logo
lightdash_mcpVision / Multimodal · ★ 24

Integrate AI coding assistants (Claude, Cursor) with Lightdash for natural language data analysis

Read lightdash_mcp full review →
02

Side-by-Side Comparison

Field
pdf-mcp logopdf-mcp
lightdash_mcp logolightdash_mcp
Category
Vision / Multimodal
Vision / Multimodal
Stars
★ 155
★ 24
License
MIT
MIT
Install Command
pip install pdf-mcp
pip install lightdash-mcp
Updated
1w ago
3mo ago
Open Source
Yes
Yes
Website
↗ Visit
↗ Visit
GitHub
↗ GitHub
↗ GitHub
Tags
agentic-ai, ai, claude
analytics, business-intelligence, claude
03

Features

pdf-mcp logopdf-mcp
01Hybrid search (BM25 keyword + semantic embeddings) with Reciprocal Rank Fusion
02Paginated reading to avoid context overflow
03OCR support for scanned and image-based PDFs via Tesseract
04Structured extraction of tables, images, and table of contents
05Persistent SQLite cache with automatic invalidation
lightdash_mcp logolightdash_mcp
01Data discovery: explore catalogs, find tables, and understand schemas
02Advanced querying with full filter, metric, and aggregation support
03Full chart lifecycle management (CRUD) with complex visualizations
04Comprehensive dashboard management with tiles, filters, and layouts
05Resource organization via spaces for content management
04

Use Cases

pdf-mcp logopdf-mcp
↳Efficiently read and analyze large PDF documents without exceeding context limits
↳Search for specific content or concepts within PDFs using natural language
↳Extract structured data such as tables and images from PDFs
lightdash_mcp logolightdash_mcp
↳Integrate AI coding assistants (Claude, Cursor) with Lightdash for natural language data analysis
↳Automate chart and dashboard creation based on user queries or AI recommendations
↳Enable ad-hoc metric querying and data exploration through conversational interfaces
FAQ

FAQ

What is the difference between pdf-mcp and lightdash_mcp?
Both pdf-mcp and lightdash_mcp are in the Vision / Multimodal category. pdf-mcp has 155 stars, while lightdash_mcp has 24 stars.
Which is better, pdf-mcp or lightdash_mcp?
The best choice depends on your use case. Choose pdf-mcp if Efficiently read and analyze large PDF documents without exceeding context limits, and lightdash_mcp if Integrate AI coding assistants (Claude, Cursor) with Lightdash for natural language data analysis.
Is pdf-mcp free or open source?
Yes, pdf-mcp is open source on GitHub (MIT).
Is lightdash_mcp free or open source?
Yes, lightdash_mcp is open source on GitHub (MIT).
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