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Thinchain

  • Updated May 22, 2026
  • SnipMCP/thinchain

io.github.SnipMCP/thinchain is a MCP server that sanitizes broker options data and compresses chains ~95% for AI trading agents.

About

io.github.SnipMCP/thinchain is an MCP server that cleans bad broker options data and returns heavily compressed options chains so AI trading agents can analyze markets without drowning in tokens or garbage quotes. Developers experimenting with agent-driven research, paper trading, or internal tooling can add the thinchain package (0.1.1) over stdio and let Claude Code, Cursor, or Codex pull normalized chain views instead of pasting CSV exports or brittle API responses. The value is narrowly technical: data hygiene plus compression for LLM context limits, not portfolio advice or execution guarantees. You should understand options basics and your broker’s terms of use; the README emphasizes sanitization and a stated 95% compression benefit for chains rather than a broad trading platform.

  • Sanitizes unreliable broker options data before agents reason over strikes and expirations.
  • Compresses options chains by about 95% to cut token load for LLM trading agents.
  • PyPI package thinchain 0.1.1 with stdio MCP transport.
  • Targets AI trading agents that need structured chain snapshots, not full vendor dumps.
  • Maintained under SnipMCP alongside other chain-style finance MCP servers.

Thinchain by the numbers

  • Data as of Jul 7, 2026 (Skillselion catalog sync)
terminal
claude mcp add thinchain -- uvx thinchain

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Packagethinchain
TransportSTDIO
AuthNone
Last updatedMay 22, 2026
RepositorySnipMCP/thinchain

What it does

Feed trading agents clean, compressed options-chain data instead of raw, noisy broker payloads.

Who is it for?

Best when you're building MCP-backed trading or research agents and need smaller, cleaner options-chain inputs.

Skip if: Non-technical investors, regulated production execution without your own compliance review, or workflows that only need static equity quotes.

What you get

After registering thinchain, your agent receives sanitized, compact chain snapshots suitable for analysis scripts and trading-agent prompts.

  • Sanitized options-chain payloads for agent tools
  • Approximate 95% smaller chain representations versus raw broker dumps
  • Reusable MCP integration for trading-agent prototypes

By the numbers

  • Stated ~95% options-chain compression for AI agents
  • Server/package version 0.1.1, PyPI identifier thinchain
  • stdio MCP transport; repository github.com/SnipMCP/thinchain
README.md

ThinChain

The catalytic converter between raw broker data and your AI trading agent.

Battle-tested. Sanitization logic extracted from a production options trading app. Handles every bad tick Tradier has thrown at us in production.

ThinChain Demo

☁️ Moving to production?

The open-source server runs locally with your own API keys. For hosted infrastructure with multi-broker failover, SLA guarantees, and webhook alerts — join the managed cloud waitlist.

The Problem

Tradier's MCP dumps 500-row JSON chains. One bad print can corrupt an agent's reasoning. ThinChain sanitizes, compresses, and circuit-breaks that data before it reaches your model.

Installation

git clone https://github.com/snipmcp/thinchain.git
cd thinchain
pip install -e ".[dev]"
cp .env.example .env

Or with Docker:

docker-compose up --build

Configuration

TRADIER_API_KEY=your_key_here
TRADIER_BASE_URL=https://api.tradier.com/v1
POLYGON_API_KEY=your_key_here
POLYGON_BASE_URL=https://api.polygon.io
DEFAULT_BROKER=tradier
CACHE_TTL_SECONDS=30
LOG_LEVEL=INFO

Usage

Three example prompts to send to Claude (or any MCP-compatible agent):

  1. Use get_compressed_chain to get an iron condor setup on SPY expiring 2026-06-19
  2. Check if SPY options data is trustworthy before I place my trade
  3. Get me the ATM straddle strikes for AAPL expiring 2026-06-19

Run it in two terminals

# Tab 1 — start the MCP server
python -m thinchain.server
# Tab 2 — call a tool from a Python shell or your MCP client
# Tool signatures:
#   get_compressed_chain(symbol, expiration, strategy="raw",
#                        delta_range=None, broker="tradier")
#   get_sanitized_quote(symbol, broker="tradier")
#   get_circuit_status(symbol, expiration, broker="tradier")

How it works

Three layers between raw broker output and your model:

Broker API → [Sanitize] → [Compress] → [Circuit Break] → MCP Tool → AI Agent
              hygiene     strategy      anomaly gate
              rules       slicing       + cache
  • Sanitize: Drops ghost quotes (0.0/0.0), inverted spreads, NaN/empty/dash values, clamps deltas to [-1, 1], normalizes positive theta on long options, flags illiquid strikes.
  • Compress: Slices the chain to only the strikes relevant to your strategy (iron condor, straddle, etc.) — typically 95%+ token reduction.
  • Circuit Break: Four-tier data quality (clean / noisy / degraded / circuit_breaker_active). When >65% of rows are anomalous, refuses to serve stale data and falls back to last known-good cache.

Real numbers from a live SPY iron condor (2026-06-19 expiration)

RAW TRADIER PAYLOAD          THINCHAIN OUTPUT
─────────────────────────────────────────────
Rows:        482             Rows:        25
Est tokens:  38,560          Est tokens:  2,000
Token savings:               95%
Anomalous rows removed:      217
Data quality:                noisy → still safely served

ThinChain compresses 482 rows / 38,560 tokens down to 25 rows / 2,000 tokens — and flags 217 anomalous strikes (illiquid, ghost quotes, wide spreads) that would have polluted the agent's context.

Roadmap

  • Managed cloud tier (hosted, multi-tenant, webhook alerts)
  • Polygon connector hardening (live SIP feed)
  • Webhook alerts for circuit breaker trips

Contributing

PRs welcome. Run pytest before submitting.

Recommended MCP Servers

How it compares

Broker data sanitizer and compressor MCP, not a strategy or risk-management skill.

FAQ

Who is io.github.SnipMCP/thinchain for?

Developers creating AI trading or market-research agents who need MCP access to cleaned, token-efficient options-chain data.

When should I use io.github.SnipMCP/thinchain?

Use it while building agent integrations that consume live or near-live options chains and you hit broker noise or context limits from full chain dumps.

How do I add io.github.SnipMCP/thinchain to my agent?

Install thinchain from PyPI (0.1.1), connect your broker or data source per repository instructions, and register the server with stdio transport in your MCP client.

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