# mcp-scientific-rag

> Use this tool when you need to extract structured data from technical PDFs, such as tables, formulas, and references, to automate document processing and knowledge retrieval. It takes PDF files as input and outputs extracted data in a machine-readable format, leveraging PyMuPDF4LLM and Ollama for high-performance processing. Ideal for use cases involving scientific literature analysis, research automation, and document indexing.

Canonical page: https://skillsregistry.net/skills/davinson-pezo-mcp-scientific-rag  
JSON: https://api.skillsregistry.net/v1/skills/davinson-pezo-mcp-scientific-rag

## Description

High-performance local MCP server for Scientific RAG. Optimized for extracting tables, formulas, and references from technical PDFs using PyMuPDF4LLM and Ollama.

## Trust

- **Trust score (0–1):** 0.95
- **Verification tier:** verified
- **Last scanned:** 2026-09-19

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-19

## Source

- **Source listing:** [GitHub](https://github.com/davinson-pezo/mcp-scientific-rag)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "davinson-pezo-mcp-scientific-rag"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/davinson-pezo-mcp-scientific-rag` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/davinson-pezo-mcp-scientific-rag/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt