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Veda MCP

by tman7162-star
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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.

Related MCP server: docpack MCP Server

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

This server cannot be deployed

One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Maintenance

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