Veda MCP

Veda MCP lets Claude, Cursor, Hermes, Codex, and other MCP-capable AI apps read Veda Knowledge Packs through a read-only MCP toolset.

Veda = AI-readable Knowledge Pack repository.
You ask inside your AI app; Veda supplies source-aware context through MCP.

Tools

This MCP exposes a guided Veda workflow plus read/update draft helpers:

Tool	Purpose
`get_veda_mcp_status`	Confirm the connection and return the current Veda MCP version. Call this first after installation/approval and tell the user the returned connection message.
`start_veda_session`	Start a guided flow: call, create, or update a Knowledge Pack.
`list_packs`	List Knowledge Packs allowed for your Veda MCP token.
`search_packs`	Search allowed Knowledge Packs.
`attach_pack_to_session`	Attach one Knowledge Pack as the active pack for the current AI conversation.
`get_pack_manifest`	Return pack structure, version, and file metadata.
`get_pack_sources`	Return source list for a pack.
`get_answer_policy`	Return pack-specific answer rules.
`get_pack_context_for_question`	Return compact, source-aware context from a selected pack for a user question.
`create_pack_draft`	Guide draft creation for a new Knowledge Pack.
`update_pack_draft`	Guide an update draft for an existing Knowledge Pack.
`report_pack_issue`	Report missing or wrong knowledge.

Quick install by asking your AI

Copy this into the AI app you use for coding/automation:

Install this MCP server for me.

GitHub repository:
https://github.com/tman7162-star/veda-mcp

Use this command-based MCP server:
npx -y github:tman7162-star/veda-mcp

Environment variables:
VEDA_MCP_URL=https://veda.necelab.net/mcp
VEDA_MCP_TOKEN=<my Veda connection key here>

After installing, test it by calling `get_veda_mcp_status`, tell me the returned Veda MCP version, then list my Veda Knowledge Packs.

Manual MCP config

Most MCP clients accept a config shape similar to this:

{
  "mcpServers": {
    "veda": {
      "command": "npx",
      "args": ["-y", "github:tman7162-star/veda-mcp"],
      "env": {
        "VEDA_MCP_URL": "https://veda.necelab.net/mcp",
        "VEDA_MCP_TOKEN": "veda_mcp_xxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

For local development against a local Veda web app:

{
  "mcpServers": {
    "veda-local": {
      "command": "node",
      "args": ["C:/path/to/veda-mcp/src/index.js"],
      "env": {
        "VEDA_MCP_URL": "http://127.0.0.1:8795/mcp",
        "VEDA_MCP_TOKEN": "veda_mcp_xxxxxxxxxxxxxxxxxxxx"
      }
    }
  }
}

Environment variables

Variable	Required	Default	Description
`VEDA_MCP_TOKEN`	yes	-	Veda connection key generated/approved from your Veda account.
`VEDA_MCP_URL`	no	`https://veda.necelab.net/mcp`	Veda MCP endpoint. Use a local URL for local development.
`VEDA_TIMEOUT_MS`	no	`30000`	Request timeout in milliseconds.

Security notes

Treat VEDA_MCP_TOKEN connection key like a password.
Start with read-only scopes: pack:read and pack:context.
If the token leaks, revoke/regenerate it in Veda.
Do not paste your token into public GitHub issues, screenshots, or commits.

Development

npm install
npm run check
npm run smoke:list-tools

smoke:list-tools verifies that the local stdio MCP server can start and list its tool definitions. It does not require a valid Veda token because it does not call Veda tools.

License

MIT

Veda MCP

Veda MCP

Tools

Quick install by asking your AI

Manual MCP config

Environment variables

Security notes

Development

License

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