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Veridge MCP Server

AI & Memory Local

Enables AI assistants to query a project's unified graph (code, documents, decisions) with token-budgeted, ranked context, including focus, impact, and find operations.

Install from GitHub GitHub Docs Glama

How to connect

  1. Glama registry

    View https://glama.ai/mcp/servers/axqiyqwiid for deploy options, or install from https://github.com/galimar/veridge (see README for MCP config).

  2. GitHub

    Install from https://github.com/galimar/veridge and add the server to your MCP client configuration (see repository README).

Tools

Tool names are not listed in our registry for this server. Use Connect or Install above, then open your MCP client to see the live tool list.