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  1. Home
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  3. Varrd
Varrd logo
Health: ActiveRecent health check succeeded.Last checked 9/23/2026, 4:46:29 AM

Varrd

User RatingsBe the first to rate and review this MCP server!
View Repository24 GitHub StarsTotal stargazers on GitHub for the source repository (24 stars).Visit Website
tradingfinancequant-researchbacktestingmarket-data

Validated trading research, live edge monitoring, strategy audits, and AI-assisted hypothesis testing over MCP.

Quick Install

Automated & IDE Setup

Copy the AI prompt to install this server into Claude Code, Cursor, or another agent — or use 1-click editor setup below.

Add to CursorAdd to VS Code
Not yet automatically verified

This server is confirmed live — we successfully called its tools/list endpoint directly (see the verified badge above). We haven't yet sandbox-tested the stdio install command below specifically, which is a separate, ongoing check.

Manual Client & Custom JSON ConfigExpand JSON â–¾

Client Config & Setup

Choose your client or environment
Target File:~/Library/Application Support/Claude/claude_desktop_config.json
claude_desktop_config.json
{
  "mcpServers": {
    "varrd": {
      "command": "uvx",
      "args": [
        "varrd"
      ]
    }
  }
}

💡 Paste the JSON block into your client's configuration file under mcpServers, then restart the application.

Install Tool Schemas (9) Directory Badge Claim listing Alternatives💰 More in Finance & Fintech

Overview

The varrd MCP server connects AI clients to VARRD’s validated trading-edge library and quantitative research workflow. It can monitor a user’s own edges against live market data, expose trade levels and audit details, and test new hypotheses through a multi-turn AI session. Research uses a separate deterministic backtesting engine with statistical safeguards, while some detailed results and AI research actions consume credits. Use it when you need auditable market-pattern research rather than general financial data access.

Use cases

•Monitor validated edges firing across supported markets
•Test one trading hypothesis with statistical safeguards
•Audit a strategy’s formula, discovery process, and performance
•Search saved hypotheses and retrieve current trade levels
•Generate a news briefing tied to a validated edge library

Key features

•Live account-scoped edge monitoring
•Multi-turn AI hypothesis testing
•Out-of-sample validation and p-hacking controls
•Formula, setup-code, and performance audits
•Multi-market backtesting and stop optimization
•Personalized edge-linked market briefings

Capabilities & Tool Schemas (9) ~2.5k tokensApproximate context cost of this server’s tool schemas (~4 chars/token), before any tool is called. Actual usage depends on your client and model.Verified live Verified liveCaptured by calling this server’s live tools/list endpoint.

Inspect callable tools, capabilities, and parameters exposed to AI agents by Varrd.

varrd_edges

THE PRIMARY TOOL — start here. FREE at depth=0, always safe to call. Live feed of THIS USER'S OWN statistically validated trading edges — the ones on their account — running 24/7 against real market data. See which of YOUR edges are firing right now, get trade levels, or audit the full methodology. Scoped to the connected account: if the user has no edges yet, this returns none (it is NOT a general/shared library). THREE TIERS: depth=0 (FREE — call this first): See which of YOUR edges are firing right now, pending bar close, or actively in trades. Markets and status only — no direction, no stats. Get a sense of what's live. depth=1 ($0.50): Unlock direction, occurrence count, EV/trade, stop-loss, take-profit, hold horizon, and current entry prices for ALL active edges in one request. depth=2 ($1 per edge, $5 for all): Full methodology — the actual formula, setup code, how the edge was discovered, edge decay analysis, complete performance analytics (Sharpe, drawdown, equity curve, profit factor). Machine-readable so any AI can audit the statistical rigor. Includes drill-down sections (free after purchase): setup_code, horizons, analytics, occurrences, and view (interactive chart link for your user, 15 min). Every edge in this library is Bonferroni-corrected, tested against both zero returns and market baseline, with K-tracking to prevent p-hacking. Out-of-sample validated. Full transparency.

varrd_ai

Talk to VARRD AI (~$0.25/turn). Describe any trading idea in plain language and the system handles everything — loading decades of market data, charting your pattern, running statistical tests, backtesting with stops, and generating exact trade setups. MULTI-TURN: First call creates a session. Keep calling with the same session_id, following context.next_actions each time. 1. Your idea -> VARRD charts pattern 2. 'test it' -> statistical test (event study or backtest) 3. 'show me the trade setup' -> exact entry/stop/target prices HYPOTHESIS INTEGRITY (critical): VARRD tests ONE hypothesis at a time — one formula, one setup. Never combine multiple setups into one formula or ask to 'test all' — each idea must be tested as a separate hypothesis for the statistics to be valid. Say 'start a new hypothesis' between ideas to reset cleanly. - ALLOWED: Test the SAME setup across multiple markets ('test this on ES, NQ, and CL') — same formula, different data. - NOT ALLOWED: Test multiple DIFFERENT formulas/setups at once — each is a separate hypothesis requiring its own chart-test-result cycle. If ELROND council returns 4 setups, test each one separately: chart setup 1 -> test -> results -> 'start new hypothesis' -> chart setup 2 -> etc. KEY CAPABILITIES you can ask for: - 'Use the ELROND council on [market]' -> 8 expert investigators - 'Optimize the stop loss and take profit' -> SL/TP grid search - 'Test this on ES, NQ, and CL' -> multi-market testing - 'Simulate trading this with 1.5 ATR stop' -> backtest with stops EDGE VERDICTS in context.edge_verdict after testing: - STRONG EDGE: Significant vs zero AND vs market baseline - MARGINAL: Significant vs zero only (beats nothing, but real signal) - PINNED: Significant vs market only (flat returns but different from market) - NO EDGE: Neither significant test passed TERMINAL STATES: Stop when context.has_edge is true (edge found) or false (no edge — valid result). Always read context.next_actions.

search

Search your saved hypotheses by keyword or natural language query. Returns matching strategies ranked by relevance, with key stats (win rate, Sharpe, edge status). Use this to find strategies you've already validated.

get_hypothesis

Get full detail for a specific hypothesis/strategy. Returns formula, entry/exit rules, direction, performance metrics (win rate, Sharpe, profit factor, max drawdown), version history, and trade levels. Everything an agent needs to understand and act on a strategy.

check_balance

Check your credit balance and see available credit packs. Free — no credits consumed. Also auto-detects completed payments — call this after your user pays via a checkout link to confirm credits were added. If payment went through, the response includes recovered_cents.

buy_credits

Buy credits for the edge library and AI research. Default $5 minimum. Free — no credits consumed to call this. TWO PAYMENT METHODS: card (default): Returns a Stripe Checkout link for your user to click and pay. After payment, call check_balance to confirm credits were added. crypto: USDC on Base. Fully autonomous — no human needed. Three steps: 1. buy_credits(payment_method='crypto') → returns deposit address + payment_intent_id 2. Send USDC to the deposit address (use your wallet tool) 3. buy_credits(payment_intent_id='pi_...') → confirms payment, credits added instantly If you have wallet access, this is the fastest path — fully machine-to-machine.

How Varrd works

What the varrd MCP server does

The varrd MCP server gives an MCP client access to VARRD’s account-scoped library of statistically validated trading edges and its research workflow. It can show which of the connected user’s edges are firing, pending a bar close, or already in trades. Depending on the requested depth, results may include direction, occurrence counts, expected value, stop-loss and take-profit levels, hold horizon, entry prices, formulas, setup code, discovery history, and performance analytics.

The library is not a shared catalog of strategies. Results depend on the connected account having saved edges. VARRD states that its validated edges are tested out of sample, Bonferroni-corrected, compared with both zero returns and a market baseline, and tracked for repeated testing to reduce p-hacking.

How it works

Research begins with a plain-language idea sent to varrd_ai. The first call creates a session, and later calls reuse its session_id while following the returned context.next_actions. A typical sequence is to describe a setup, request a chart or pattern view, ask to test it, and then request a trade setup. The system can run event studies or backtests, test one formula across several markets, search stop-loss and take-profit combinations, and simulate stops such as an ATR-based rule.

Each research call must represent one hypothesis. Different formulas or setups need separate research cycles, while the same setup can be tested across multiple markets. Results terminate with an edge verdict such as strong edge, marginal, pinned, or no edge. The AI interprets results, but VARRD says statistical calculations are performed by a separate deterministic engine in a sandboxed kernel.

Setup and configuration

VARRD provides a Streamable HTTP endpoint at https://app.varrd.com/mcp. An MCP client can connect using that URL without an API key. The README specifically lists Claude Desktop and Cursor as compatible clients; it also mentions OpenBB and other MCP clients.

The repository also documents a Python SDK installation with pip install varrd, but that installs the SDK rather than serving as the MCP endpoint configuration. For a hosted MCP connection, configure the endpoint directly in the client.

Tools and capabilities

  • varrd_edges provides free market and status information at depth 0, paid directional and trade details at depth 1, and deeper methodology and analytics at depth 2.
  • varrd_ai supports multi-turn hypothesis development, charting, statistical testing, backtesting, and trade-setup generation.
  • autonomous_varrd_ai explores a supplied topic by generating and testing one hypothesis per call.
  • search finds saved hypotheses by keyword or natural-language query.
  • get_hypothesis retrieves a strategy’s formula, rules, metrics, version history, and trade levels.
  • get_briefed creates a personalized market-news briefing when the account has at least five strong edges.
  • check_balance, buy_credits, and reset_session manage credits and stuck research sessions.

Limitations and notes

Depth 0 edge status is free, while deeper edge data and research actions use credits. The documented prices include $0.50 for a depth-1 snapshot, $1 per edge or $5 for all edges at depth 2, and about $0.25 per AI research turn or autonomous hypothesis. Credit purchases can use Stripe Checkout or USDC on Base.

Live edge results are scoped to the connected account, and a new account may return no edges. get_briefed requires at least five strong edges. Research should stop when the returned context reports either an edge found or a valid no-edge result. If a session becomes stuck, use reset_session before starting a new one.

Read the full README →View source on GitHub →

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Adoption & maintenance

Factual signals from GitHub, npm, and our automated checks — not a rating.

GitHub stars
24
Stargazers on the source repository.
Last commit
25d ago
Most recent push to the default branch.
Tools exposed
9
Callable tools this server registers over MCP.
Directory activity
3 views
Config copies, upvotes, and views on AllMCPs.

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Frequently Asked Questions about Varrd

Configure an MCP client with the Streamable HTTP endpoint https://app.varrd.com/mcp. The README also documents pip install varrd for the Python SDK.

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Technical Specs & Signals

Category💰Finance & Fintech
PricingFree
More technical detailsExpand â–¾
TransportSTDIO
RuntimePython
AuthNo auth required
LicenseMIT
ClientsClaude Desktop, Cursor
Last updatedSep 7, 2026
11/12 checks healthy over the last 46d
Views3
Unique ViewsTotal visits recorded for this listing page on AllMCPs.
Installs0
Installs & Copy ActionsTotal times users copied install commands or configuration snippets for this server.
GitHub stars24
GitHub Star CountTotal stargazers on GitHub representing community popularity (24 stars).
Last commit25d ago
Last Repository CommitThe most recent commit or push recorded for this server's GitHub repository.Last commit on Aug 31, 2026
63Quality signal: Good · 63/100How this signal is calculated ▾
Server availabilityNot measured

Not scored for repo-hosted servers — we can't reach the running server, only its GitHub page. Hosted MCP endpoints are health-checked live.

Verified ownership10/20
Documentation & tools30/30
Adoption & activity7/15
Community engagement0/10

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Scanned 4d ago via OSV.dev · varrd (PyPI)

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