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varrd_ai

Test a single trading hypothesis by describing it in plain language. The system loads market data, charts the pattern, runs statistical tests, and provides exact entry, stop, and target prices.

Instructions

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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
messageYesYour trading idea, research question, or instruction (e.g. 'test it', 'show trade setup').
session_idNoSession ID from a previous call. Omit to start a new research session.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
textNoAI response text
contextNoWorkflow state, edge verdict, next actions
widgetsNoChart, event study, backtest, or trade setup widgets
session_idNoSession ID for multi-turn conversation
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Beyond annotations (readOnlyHint false, openWorldHint true), the description discloses cost (~$0.25/turn), session creation, terminal states (edge verdicts, context.has_edge), and the requirement to follow context.next_actions. No contradictions with annotations.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is well-structured with clear sections (MULTI-TURN, HYPOTHESIS INTEGRITY, KEY CAPABILITIES, etc.), front-loads the core purpose, and every sentence adds necessary detail without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (multi-turn, stateful, cost, hypothesis testing), the description covers all essential aspects: how to start/continue, rules, edge verdicts, terminal states, and key capabilities. It is fully self-contained for an agent to use correctly.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters5/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, and the description adds significant value: examples for 'message' (e.g., 'test it', 'show trade setup') and explicit instructions for 'session_id' ('Omit to start a new research session'). It also explains how to use parameters within the multi-turn workflow.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose: 'Talk to VARRD AI...Describe any trading idea...handles everything'. It distinguishes from siblings like 'autonomous_varrd_ai' by emphasizing multi-turn user interaction and specific workflow steps.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description provides explicit when-to-use and when-not-to-use guidance, including multi-turn session management, hypothesis integrity rules ('Never combine multiple setups'), and allowed/not-allowed actions like testing same setup across markets but not different formulas simultaneously.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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