Paper Read

Local MCP server for parsing PDFs, extracting structures, analyzing math, generating code, and visualizing experiments with local LLM support.

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Language

6 months ago

First Indexed

2 months ago

Catalog Refreshed

Documentation & install

Readme and setup notes from the catalogue, plus a client-ready config you can copy for your MCP host.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "lxy-hqu--mcp-for-paper-read-based-on-ai-ide": {
      "command": "C:\\\\Program Files\\\\nodejs\\\\node.exe",
      "args": [
        "E:\\\\path\\\\to\\\\-mcp-for-paper-read-based-on-AI-IDE\\\\dist\\\\server.js"
      ]
    }
  }
}

You run a local MCP server that analyzes and processes scientific papers on your machine. It provides local PDF parsing, structured extraction, deep math understanding, code generation, and visualization, with optional acceleration from a local large model. This setup helps you build reproducible analysis pipelines, store results in a local database, and generate Markdown reports with correctly rendered math.

How to use

Install and run the local MCP server through your MCP client. You can load a PDF, let the system summarize the paper, extract methods, parse equations, generate PyTorch code, render diagrams, and produce a full Markdown analysis report. All processing happens locally, with optional local model acceleration.

How to install

Prerequisites: Node.js (v16+), Git, and optionally Ollama for local LLM acceleration (default port 11434)

Clone the project repository to your workstation

git clone https://github.com/Lxy-hqu/-mcp-for-paper-read-based-on-AI-IDE.git
cd -mcp-for-paper-read-based-on-AI-IDE

Install dependencies

npm install

Compile the project

npx tsc

Configure Trae for MCP access (recommended auto-configuration)

{
  "mcpServers": {
    "local-papers": {
      "command": "C:\\Program Files\\nodejs\\node.exe", 
      "args": [
        "E:\\path\\to\\-mcp-for-paper-read-based-on-AI-IDE\\dist\\server.js"
      ],
      "disabled": false,
      "autoApprove": []
    }
  }
}

Tip: You can find the Node.js path by running the terminal command where node.

Additional setup and usage notes

Once Trae is restarted, the MCP client should show a connected server labeled as local-papers. You can test by asking it to load a file, for example: “请使用 pdf_loader 读取这个文件：E:\my_paper.pdf” (use your own file path).

Usage examples

Math formula parsing, code generation, visualizations, and report creation are available as dedicated capabilities. You can issue natural language prompts such as:

“解析这篇论文的数学公式，并将符号定义存入数据库。”
“根据这篇论文的方法部分，生成 PyTorch 模型代码，并提取超参数。”
“为这篇论文的模型结构生成一个 Mermaid 流程图。”
“为这篇论文生成一份完整的 Markdown 分析报告。”

Available tools

pdf_loader

Reads PDF files locally and provides raw content and metadata to downstream modules.

structure_parser

Segments and structures the paper into meaningful sections for analysis.

math_explainer

Extracts mathematical formulas, builds ASTs, and stores symbols in the local database.

code_generator

Generates PyTorch model code and training scripts based on the methods section.

visualization

Creates Mermaid diagrams and variable dependency graphs to illustrate models and experiments.

report_generator

Produces a complete Markdown analysis report with summaries, structure, visuals, and code configurations.

summarizer

Generates intelligent summaries and method sections, with optional deep content understanding via a local LLM.

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