Skip to main content
Glama
deenesjakoruzh
augiemazza

Varrd

by augiemazza
OverviewSchemaRelated ServersScoreDiscussions
Python
Remote

research

Test trading hypotheses through statistical analysis, backtesting, and expert evaluation to identify market edges with valid results.

Instructions

Talk to VARRD AI — a quant research system with 15 internal tools. Describe any trading idea in plain language, or ask for specific capabilities like the ELROND expert council, backtesting, or stop-loss optimization.

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.

Tool Definition Quality

A4.6/5.0
Behavior4/5

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

Rich behavioral disclosure beyond annotations: session lifecycle mechanics, hypothesis integrity constraints (one formula at a time rule), edge verdict definitions (STRONG/MARGINAL/PINNED), and state machine guidance (next_actions). Minor gap: no rate limit or error handling mention.

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

Conciseness4/5

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

Lengthy but appropriately so for a complex stateful tool. Well-structured with clear headers (MULTI-TURN, HYPOTHESIS INTEGRITY, KEY CAPABILITIES) that aid scanning. Every section provides actionable instruction; minimal fluff.

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?

Extremely complete despite no output schema. Documents return values indirectly via context fields (edge_verdict, has_edge, next_actions), explains the 4-tier edge classification system, and describes terminal states. No gaps for this complexity level.

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

Parameters4/5

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

Schema has 100% coverage (baseline 3). Description adds workflow context showing how 'message' content evolves across turns (idea → 'test it' → 'show trade setup'), which adds crucial semantic meaning for conversational state management.

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?

Specific verb ('Talk to') + resource ('VARRD AI') + scope (quant research, backtesting, optimization). Clearly distinguishes from sibling 'autonomous_research' by emphasizing interactive/conversational workflow vs automated execution.

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?

Exceptional guidance: explicit multi-turn workflow ('First call creates a session'), clear prohibitions ('Never combine multiple setups'), terminal state conditions ('Stop when context.has_edge is true'), and prerequisite actions ('start a new hypothesis' between ideas).

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

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/augiemazza/varrd'

If you have feedback or need assistance with the MCP directory API, please join our Discord server