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kbg-viewer

kbg-viewer

Local
github.com/ocasazzaMemory & Knowledge2Updated 3d ago

About kbg-viewer

The README mainly contains material about Annie Jump Cannon, Graphviz, and graph visualization, which does not match the knowledge-graph viewer described by the project name and tagline. There is not enough information to establish its actual features or MCP usage.

Key features

  1. 01Includes material on Annie Jump Cannon
  2. 02Lists Graphviz papers and graph-drawing resources
  3. 03Mentions a browser demo

Use cases

  1. 01Consult references on Graphviz graph drawing
  2. 02Visit the linked browser demo

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

Jump Cannon

Annie Jump Cannon

When Cannon first started cataloging the stars, she was able to classify 1,000 stars in three years, but by 1913, she was able to work on 200 stars an hour.[20] Cannon could classify three stars a minute just by looking at their spectral patterns and, if using a magnifying glass, could classify stars down to the ninth magnitude, around 16 times fainter than the human eye can see.[8] Her work was also highly accurate.[20]

[8] Annie Cannon. She is an Astronomer. 2014. Retrieved February 18, 2014.

[20] Woman Making Index of 100,000 Stars for a Catalogue. The Danville Morning News. February 10, 1913. Retrieved April 13, 2017 – via Newspapers.com.

Browser Demo

https://ocasazza.github.io/jump-cannon/

Reference Papers

Graphviz Papers


Graph Drawing


Information Visualization


License

MIT License

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.