Trading Volatility MCP
The options & volatility data source agents can query.
A Model Context Protocol server for Trading Volatility — lets any AI agent discover and retrieve options market-structure data (GEX, gamma flip levels, dealer positioning, skew, max pain, expected-move levels, options flow, and ranked trade setups) over the public v2 API, in conversation.
- Read-only — discovers and retrieves data over your existing Trading Volatility subscription. No orders, payments, or monitoring.
- Stateless passthrough — forwards your
Authorization: Bearer <key>to the v2 API and stores nothing. Local and hosted modes are the same code path. - Two transports —
stdiofor local agents, streamable HTTP + SSE for remote, multi-user hosting. - Works without a key — the demo tickers (AAPL, VIX, KO, META, AMZN, XOM, GM, MCD) work out of the box.
Tools
| Tool | What it returns |
|---|---|
list_capabilities |
The v2 capability manifest (/llm-spec) — call first to self-orient |
get_auth_status |
Whether you're in keyed or demo mode |
get_ticker_state |
Canonical compact state snapshot |
explain_ticker |
Deterministic narrative interpretation of the regime |
get_market_structure |
Headline signal, regime, expected behavior, levels |
get_signals |
Current setup/positioning signals |
get_levels |
Key levels (json / tradingview / tos) |
get_series |
Historical daily series for selected metrics over a window |
get_gamma_curve |
Gamma strike curve (per expiration, optionally realtime) |
get_gamma_by_expiration |
Gamma decomposition by expiration bucket |
get_gex_by_strike |
Net GEX strike curve with call/put contributions |
get_options_volume |
Options volume by strike for an expiration |
rank_top_setups |
Cross-ticker opportunity ranking, with filters |
run_screener |
A named thesis preset over the ranking |
rank_income_setups |
Cross-ticker covered-call / cash-secured-put ranking, with filters |
get_trade_setup |
Compact agent-oriented trade setup for one ticker |
Quickstart (local, stdio)
# Run straight from PyPI with uvx (or: pipx run tv-mcp)
uvx tv-mcp # stdio; uses TV_API_KEY or a config file
# …or from source
python3 -m venv .venv && . .venv/bin/activate
pip install -e ".[dev]"
cp config.example.json config.json # add your API key (git-ignored)
python -m tv_mcp # stdio by default
Validate:
ruff check . && pytest
Credentials & precedence
The key is resolved in this order (first match wins):
- Per-request header —
Authorization: Bearer <key>(orX-Api-Key) — hosted mode. - Environment —
TV_API_KEY. - Local JSON config —
tv_api_keyinconfig.json(seeconfig.example.json; values support${ENV_VAR}substitution so the key can stay in the environment). - Demo mode — no key; only the demo tickers are available.
The key is never logged or persisted.
Remote (hosted, HTTP + SSE)
TV_MCP_TRANSPORT=http PORT=8000 python -m tv_mcp # serves http://0.0.0.0:8000/mcp
Each request carries its own key, so one deployment serves many users:
POST /mcp Authorization: Bearer <your-key>
GET /health liveness probe
GET /AGENTS.md agent-discovery doc (how to use this server)
Container build (binds $PORT, runs the HTTP transport — deploys to any container host such
as Cloud Run, Fly, or ECS):
docker build -t tv-mcp .
docker run -p 8080:8080 tv-mcp
The server is stateless and holds no secrets, so it scales horizontally with no extra setup;
tune limits with the env vars in .env.example.
Connecting an agent
Claude Code / Claude Desktop (local, stdio) — claude_desktop_config.json:
{
"mcpServers": {
"trading-volatility": {
"command": "uvx",
"args": ["tv-mcp"],
"env": { "TV_API_KEY": "your-key" }
}
}
}
Remote MCP clients (Claude web custom integrations, OpenAI Responses mcp tool, Gemini
function-calling, the Vercel AI SDK, ADK's MCPToolset, LangChain/LangGraph's
MultiServerMCPClient) all point at the same endpoint and pass the key as a header:
- URL:
https://<your-deployment>/mcp - Header:
Authorization: Bearer <your-key>
Because the server is a standard streamable-HTTP MCP endpoint with header auth, no per-client shim is needed — configure the URL and header in whichever framework you use. Copy-pasteable guides with real code for each:
- Anthropic Claude — Claude Code, Desktop, the Messages API MCP connector, and Claude web
- OpenAI · Google Gemini · Google ADK · LangChain/LangGraph · Vercel AI SDK
- Index: docs/integrations/
How it works
agent ──tools──▶ TV MCP ──HTTPS (Bearer key)──▶ stocks.tradingvolatility.net/api/v2
(stateless)
The agent carries continuity between turns; the server keeps no session state. It forwards the caller's key and returns the v2 payloads unchanged (they are already agent-shaped).
Repository layout
src/tv_mcp/
cli.py stdio | http entry point
server.py FastMCP wiring (tools, resources, /health, /AGENTS.md)
settings.py config loading + precedence
auth.py credential resolution (header → env → config → demo)
tv/ v2 API client + normalized errors
tools/ one module per tool group (tickers, curves, discovery, auth)
transports/ stateless HTTP + SSE app, per-request key middleware
tests/ client, auth, settings, tools, transport, smoke
docs/ design, build plan, implementation notes
Docs
docs/integrations/— per-framework integration guides (Claude, OpenAI, Gemini, ADK, LangChain, AI SDK)
Deployment runbooks and maintainer planning artifacts are kept internal and excluded from public releases.
License
MIT — see LICENSE. Open source under the Trading Volatility brand.
Metadata
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| tv_mcp-0.2.0-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 58.8 kB
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