Skip to main content
Glama

autonomous_varrd_ai

Provide a topic and let the tool autonomously explore related market hypotheses, testing each through a full pipeline to return trade setups with statistical results.

Instructions

Point VARRD's autonomous AI in a direction and let it discover edges for you. Give it a topic and it draws from one of the most comprehensive market structure knowledge graphs ever built — containing ideologies and theories, not statistics — so it generates genuinely novel hypotheses rather than overfitting to what already worked.

BEST FOR: Exploring a space broadly. Give it 'momentum on grains' and it might test wheat seasonal patterns, corn spread reversals, or soybean crush ratio momentum. It propagates from your seed idea into related concepts you might not think of.

Returns a complete result — edge or no edge, stats, trade setup. Each call tests ONE hypothesis through the full pipeline. Call again for another idea. Requires credits.

Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
topicYesResearch topic or trading idea (e.g. 'BTC 240min short setups', 'momentum on grains', 'mean reversion after VIX spikes').
marketsNoFocus on specific markets (e.g. ['ES', 'NQ']). Omit for VARRD to choose.
test_typeNoType of statistical test. Default: event_study.event_study
search_modeNofocused = stay close to topic. explore = creative freedom. Default: focused.focused
asset_classesNoLimit to specific asset classes. Default: all.
contextNoPrior conversation context — recent user queries to use as research inspiration. Optional.
Behavior4/5

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

Annotations include openWorldHint: true, and the description adds context: it draws from knowledge graphs, tests one hypothesis per call, returns complete results (edge/no edge, stats, trade setup), and requires credits. No contradictions with annotations. Slight omission: does not explicitly state that results are not saved or any rate limits, but adequate.

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: first paragraph explains the tool, second gives best-for context, third explains output and usage. Every sentence adds value, and it is front-loaded with the core purpose.

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

Completeness4/5

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

Given 6 parameters and no output schema, the description covers tool behavior, return values, and credit usage thoroughly. It explains the exploration process and the kind of results expected. Minor gap: no mention of error handling or timeouts, but sufficient for most use cases.

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

Parameters3/5

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

Schema coverage is 100%, so baseline is 3. The description does not add much beyond schema for individual parameters, but the overall context of how parameters (topic, markets, etc.) fit into the exploration process is clear. Acceptable.

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: 'Point VARRD's autonomous AI in a direction and let it discover edges for you.' It specifies a verb (point, let discover) and a resource (autonomous AI, edges). It also differentiates from sibling tool 'varrd_ai' by noting when to use each.

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?

Provides explicit guidance: 'BEST FOR: Exploring a space broadly.' and 'Use 'varrd_ai' instead when YOU have a specific idea to test and want full control over each step.' This clearly indicates when to use this tool vs the alternative.

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