Medhā MCP

Operator-tuned fork of @veniceai/mcp-server. Same 31 Venice primitives; curated presets for chat/image/video/audio/music baked in via env defaults and surfaced in tool descriptions; one discoverable medha://favorites resource so any agent can fetch the operator's preferences before making a call. Use from any MCP host — Claude Desktop, Claude Code, Cursor, Codex CLI, Hermes, Jiva, Rati, LM Studio, Continue, LibreChat, Open WebUI, AnythingLLM, Jan, Le Chat.

What you get

Surface	Count	Notes
Tools	31	Identical to upstream; descriptions augmented with `Operator preferences — default: … also try: …` overlays keyed off `src/presets.ts`
Resources	4	`venice://models`, `venice://styles`, `venice://voices` (upstream) + `medha://favorites` (canonical JSON dump of presets)
Prompts	7	3 upstream (`uncensored-research`, `nsfw-creative-writing`, `image-style-explorer`) + 4 Medhā workflow briefs (`medha_music_video_brief`, `medha_podcast_pipeline`, `medha_dashboard_poster`, `medha_character_dossier`)
Transports	stdio and Streamable HTTP (`/mcp`)	HTTP is the Railway-deployed mode; stdio stays for npx-style install

The Medhā MCP server is thin by design: it forwards agent tool calls to api.venice.ai. The agent (Claude Code / Hermes / Codex / Jiva / Rati / Cursor / etc.) is the orchestrator — it plans the steps, picks models, calls tools in order, handles async polling, and stitches art + audio together.

Quickstart (5 min deploy on Railway)

1. Get a Venice API key

venice.ai → Settings → API Keys. Save it locally on the operator's Mac:

echo "export VENICE_API_KEY=*** ~/.config/railway/venice-rati-key.env
chmod 600 ~/.config/railway/venice-rati-key.env

2. Clone + push

gh repo clone veniceai/venice-mcp-server ~/tmp/medha-deploy
cd ~/tmp/medha-deploy
# (already customized to Medhā on master if you cloned vivmuk/medha-mcp;
# upstream clone just needs the Phase 1 patches to branding files.)

3. Railway project + service

# Create `medha` project in vivmuk's workspace
railway init --name "medha" --workspace "vivmuk's Projects"
gh repo create vivmuk/medha-mcp \
    --public \
    --description "Medhā — operator-tuned Venice AI MCP bridge." \
    --default-branch master
cd ~/tmp/medha-deploy
git remote rename origin upstream
git remote add origin https://github.com/vivmuk/medha-mcp.git
git fetch upstream --unshallow
git push -u origin master

# Create Railway service that pulls from GitHub
railway add --service medha --repo vivmuk/medha-mcp --branch master

4. Set env vars

Generate a strong bearer token for the MCP HTTP layer:

BEARER=$(python3 -c "import secrets; print(secrets.token_hex(32))")
echo "$BEARER" > ~/.config/railway/medha-bearer.token
chmod 600 ~/.config/railway/medha-bearer.token

Push secrets via --stdin (chat-safe; never argv):

unset RAILWAY_TOKEN RAILWAY_API_TOKEN

python3 - <<'PY'
import subprocess
from pathlib import Path
KEY = Path("/Users/vivgatesai/.config/railway/venice-rati-key.env").read_text()
KV = {l.split("=",1)[0].lstrip("export ").strip(): l.split("=",1)[1].strip().strip('"').strip("'") for l in KEY.splitlines() if l.startswith("export ")}
BEARER = Path("/Users/vivgatesai/.config/railway/medha-bearer.token").read_text().strip()
base = ["railway","variable","set","--service","medha","--skip-deploys"]
for label, value in [("VENICE_API_KEY", KV["VENICE_API_KEY"]), ("VENICE_MCP_AUTH_TOKEN", BEARER)]:
    subprocess.run(base + [label, "--stdin"], input=value, capture_output=True, text=True, timeout=60)
PY

Plain env (no secrets — argv-safe):

for K in \
  "VENICE_MCP_HTTP=1" \
  "VENICE_MCP_HOST=0.0.0.0" \
  "VENICE_DISABLE_NSFW=0" \
  "VENICE_DEFAULT_CHAT_MODEL=minimax-m3-preview" \
  "VENICE_DEFAULT_IMAGE_MODEL=flux-2-pro" \
  "VENICE_DEFAULT_TTS_MODEL=tts-kokoro" \
  "VENICE_DEFAULT_ASR_MODEL=openai/whisper-large-v3" \
  "VENICE_DEFAULT_VIDEO_MODEL=ltx-2" \
  "VENICE_DEFAULT_MUSIC_MODEL=ace-step-15" \
  "VENICE_HTTP_TIMEOUT_MS=120000" \
  "VENICE_MCP_MAX_SESSIONS=100" \
  "VENICE_MCP_SESSION_TTL_MS=1800000" \
  "PORT=3333" \
  "MEDHA_SERVER_NAME=@medha/mcp-server" \
  "MEDHA_SERVER_VERSION=0.4.0-medha"
do
  railway variable set --service medha --skip-deploys "$K"
done

5. Deploy + attach domain

cd ~/tmp/medha-deploy
railway up --service medha --detach
railway domain --service medha
# → https://medha-production.up.railway.app/mcp

6. Smoke

BEARER=$(cat ~/.config/railway/medha-bearer.token)
URL=https://medha-production.up.railway.app/mcp

curl -sS -i -X POST "$URL" \
  -H "Authorization: Bearer *** \
  -H "Content-Type: application/json" \
  -H "Accept: application/json, text/event-stream" \
  -d '{"jsonrpc":"2.0","id":1,"method":"initialize","params":{"protocolVersion":"2025-06-18","capabilities":{},"clientInfo":{"name":"smoke","version":"0.0.1"}}}' \
  | head

HTTP 200 with a mcp-session-id UUID header → you're live.

7. Connect an agent

See presets.md for the canonical defaults table. For Claude Desktop / Claude Code CLI / Cursor / Codex CLI / Hermes / Jiva connector recipes, see CONNECTORS.md.

Configuration reference

Env	Default	Purpose
`VENICE_API_KEY` (required)	(none)	Operator's Venice API key. Forwarded as `Authorization: Bearer` to all upstream calls.
`VENICE_SIWX_TOKEN`	(none)	Optional SIWE-signed wallet token (x402 mode). Used only when `VENICE_API_KEY` is unset.
`VENICE_MCP_HTTP`	(unset)	Set to `1` to enable HTTP mode (binds `:3333/mcp`).
`VENICE_MCP_HOST`	`127.0.0.1`	Bind address. `0.0.0.0` for LAN/Railway.
`VENICE_MCP_AUTH_TOKEN`	(none)	Bearer required for `/mcp` when host ≠ loopback. Generate with `python3 -c "import secrets; print(secrets.token_hex(32))"`.
`VENICE_MCP_MAX_SESSIONS`	`100`	Cap on concurrent Streamable HTTP sessions.
`VENICE_MCP_SESSION_TTL_MS`	`1800000`	Idle session TTL (30 min).
`VENICE_HTTP_TIMEOUT_MS`	`60000`	Per-call upstream timeout. Default 120s for video/music latencies.
`VENICE_DISABLE_NSFW`	`0`	Set to `1` to remove NSFW capability notes from tool descriptions.
`VENICE_DEFAULT_CHAT_MODEL`	`venice-uncensored`	Medhā default: `minimax-m3-preview`.
`VENICE_DEFAULT_IMAGE_MODEL`	`flux-2-pro`	(same as upstream)
`VENICE_DEFAULT_TTS_MODEL`	`tts-kokoro`	(same as upstream)
`VENICE_DEFAULT_ASR_MODEL`	`openai/whisper-large-v3`	(same as upstream)
`MEDHA_SERVER_NAME`	`@medha/mcp-server`	Override the MCP `serverInfo.name`.
`MEDHA_SERVER_VERSION`	`0.4.0-medha`	Override the MCP `serverInfo.version`.
`PORT`	`3333`	Railway-respected; controls the listener.

Operator preferences

The operator-curated defaults are baked into the tool descriptions so agents read them on every tools/list call. They are also a single canonical JSON document available via the medha://favorites resource:

# After defining BEARER + SID as in the smoke test:
curl -sS -X POST https://medha-production.up.railway.app/mcp \
  -H "Authorization: Bearer *** \
  -H "Content-Type: application/json" \
  -H "mcp-session-id: $SID" \
  -d '{"jsonrpc":"2.0","id":2,"method":"resources/read","params":{"uri":"medha://favorites"}}'

See presets.md for the full table with rationale per domain (reasoning / coding / roleplay / vision / longctx / image / music / video / etc.).

The operator prefs are HINTS, not hard rules. Agents you connect can pass model="<any-venice-id>" on any call and the server will forward it. The presets exist to save the agent from re-discovering the operator's taste on every prompt.

Workflow prompts (Medhā-only)

Beyond the upstream 3 (uncensored-research, nsfw-creative-writing, image-style-explorer), Medhā ships 4 workflow prompts that guide the agent into known-good multi-tool sequences:

Prompt	Workflow	Default models
`medha_music_video_brief`	Quote → music gen → 4-12 image frames → video interpolation per frame → optional TTS.	`ace-step-15` (music) · `flux-2-pro` (image) · `ltx-2` (video) · `tts-kokoro` (TTS)
`medha_podcast_pipeline`	Web search → scrape top URLs → script via chat → TTS.	`qwen-3-7-max` (chat) · `tts-kokoro` (TTS)
`medha_dashboard_poster`	Chat-compose prompt → flux-2-pro → optional bg-remove → optional 2× upscale.	`qwen-3-7-max` (chat) · `flux-2-pro` (image)
`medha_character_dossier`	Dossier + system prompt via roleplay chat → TTS sample → optional avatar image.	`venice-uncensored-role-play` (chat) · `tts-kokoro` (TTS) · `flux-2-pro` (image)

When the agent invokes one of these prompts, MCP returns a single user-role message that lays out the recommended tool-call sequence with the operator's preferred models. The agent then orchestrates from there.

Provenance + license

Upstream: github.com/veniceai/venice-mcp-server @ v0.2.0 — MIT-licensed by Venice AI. Medhā is a fork: same code, operator-tuned UX. No warranty or SLA from Venice AI. Use at your own risk.

Maintainer: Vivek M (@vivmuk). Brand: Medhā — Sanskrit medhā (मेधा) = wisdom / intelligence / mental power.

Medhā MCP

Medhā MCP

What you get

Quickstart (5 min deploy on Railway)

1. Get a Venice API key

2. Clone + push

3. Railway project + service

4. Set env vars

5. Deploy + attach domain

6. Smoke

7. Connect an agent

Configuration reference

Operator preferences

Workflow prompts (Medhā-only)

Provenance + license

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