Mcp Graph Agent
What is Mcp Graph Agent
mcp-graph-agent is a tool that utilizes the Multi-Capability Protocol (MCP) to interact with local files, extract structured knowledge from various document formats such as PDFs, and ingest this data into Neo4j databases via GraphRAG. It also supports intelligent tools like Streamlit LLM assistants.
Use cases
Use cases for mcp-graph-agent include automating the extraction of data from legal documents for analysis, ingesting research papers into a Neo4j database for knowledge graph creation, and supporting data-driven applications through intelligent assistants.
How to use
To use mcp-graph-agent, first ensure that your local directory is correctly mounted to the Docker container. You can run the command ‘node dist/index.js /path/to/your/local/directory’ to start processing files. Additionally, to run the Streamlit interface, use ‘streamlit run meta_frontend.py’. Make sure to install required packages like pdf-parse, mammoth, and xlsx using npm.
Key features
Key features of mcp-graph-agent include the ability to extract structured data from documents, seamless integration with Neo4j for data storage, support for intelligent assistants, and compatibility with various file formats.
Where to use
mcp-graph-agent can be used in fields such as data analysis, knowledge management, document processing, and any application requiring structured data extraction and storage.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Overview
What is Mcp Graph Agent
mcp-graph-agent is a tool that utilizes the Multi-Capability Protocol (MCP) to interact with local files, extract structured knowledge from various document formats such as PDFs, and ingest this data into Neo4j databases via GraphRAG. It also supports intelligent tools like Streamlit LLM assistants.
Use cases
Use cases for mcp-graph-agent include automating the extraction of data from legal documents for analysis, ingesting research papers into a Neo4j database for knowledge graph creation, and supporting data-driven applications through intelligent assistants.
How to use
To use mcp-graph-agent, first ensure that your local directory is correctly mounted to the Docker container. You can run the command ‘node dist/index.js /path/to/your/local/directory’ to start processing files. Additionally, to run the Streamlit interface, use ‘streamlit run meta_frontend.py’. Make sure to install required packages like pdf-parse, mammoth, and xlsx using npm.
Key features
Key features of mcp-graph-agent include the ability to extract structured data from documents, seamless integration with Neo4j for data storage, support for intelligent assistants, and compatibility with various file formats.
Where to use
mcp-graph-agent can be used in fields such as data analysis, knowledge management, document processing, and any application requiring structured data extraction and storage.
Clients Supporting MCP
The following are the main client software that supports the Model Context Protocol. Click the link to visit the official website for more information.
Content
The Docker command you’ve executed is correctly mounting your local /Users/ethancheung/Downloads directory to the container directory /projects.
node dist/index.js /Users/ethancheung/Downloads
*** to run streamlit
streamlit run meta_frontend.py
run in terminal
npx -y @modelcontextprotocol/server-filesystem /Users/ethancheung/Downloads
*** github mcp server
https://github.com/aezizhu/simple-mcp-fileserver
npm install pdf-parse
npm install mammoth
npm install xlsx
npm install neo4j-driver
used to start the mcp server before you run the client
node simple-mcp-fileserver.js
Dev Tools Supporting MCP
The following are the main code editors that support the Model Context Protocol. Click the link to visit the official website for more information.
