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auto_match

Match your question with the most relevant expert legends. Analyzes your topic, returns top 2-3 legends with reasons and key insights, helping you get quick guidance without searching manually.

Instructions

Automatically find the most relevant legends for your question.

How it works:

  1. Analyzes your question for topics and keywords

  2. Matches relevant legends based on their expertise

  3. Returns matched legends with key insights

Use this when:

  • You're not sure which legend to ask

  • You want to see who has expertise in your topic

  • You want quick insights before a full conversation

What you get:

  • Top 2-3 most relevant legends

  • Why each was matched

  • A key insight from each

  • Suggested next steps

Examples:

  • "How do I raise money for my startup?" → Paul Graham, Marc Andreessen

  • "What's the future of AI?" → Sam Altman, Jensen Huang

  • "How should I think about risk?" → Ray Dalio, Howard Marks

DISCLAIMER: AI personas for educational purposes only.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
questionYesYour question or topic to find relevant legends for
max_matchesNoMaximum legends to match (default: 2, max: 3)
include_promptsNoInclude full system prompts for each legend (default: false)
Behavior5/5

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

Despite no annotations, the description details the process (analyzes, matches, returns), output contents (top 2-3 legends, why matched, key insight, next steps), and includes a disclaimer. It fully discloses behavior.

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 sections, bullet points, and examples. Every sentence adds value 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 complexity (3 params, no output schema, no annotations), the description completely covers input, process, output, and use cases, making it self-contained.

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 adds minimal additional meaning to parameters beyond what's in the schema; examples illustrate use but don't elaborate on each parameter.

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 action ('find') and the resource ('most relevant legends'), with examples that distinguish it from siblings like 'search_legends' and 'list_legends'.

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

Usage Guidelines4/5

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

The 'Use this when' section provides explicit scenarios (unsure which legend to ask, want expertise or quick insights). While it doesn't explicitly rule out alternatives, it gives clear usage context.

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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