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find_bridges

Discover potential cross-domain connections in the knowledge graph by identifying concept pairs that share neighbors but aren't directly connected, surfacing candidates for new insights.

Instructions

Find potential cross-domain connections in the knowledge graph.

    Discovers concept pairs in different domains that share neighbors
    but aren't directly connected — candidates for new insights.

    Args:
        limit: Maximum number of bridges to return.

    Returns:
        List of potential cross-domain bridges.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior4/5

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

With no annotations provided, the description carries the burden of behavioral disclosure. It explains the core algorithm (shared neighbors, not directly connected) and notes the return type. While it doesn't discuss side effects, permissions, or limits, the tool is clearly read-only, and the description adds meaningful detail beyond the tool's name.

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?

The description is well-structured with a one-line summary, a detailed explanation, and separate Args/Returns sections. It is appropriately sized but includes a Returns note that is somewhat redundant given an output schema exists. Still, every sentence contributes to understanding.

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?

The tool is simple (one parameter, output schema present), and the description covers purpose, methodology, parameter semantics, and a high-level return. It doesn't mention prerequisites or edge cases, but for a find tool of this nature, the provided information is sufficient for an agent to invoke it 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 for parameters is 0%, so the description must compensate. It explicitly explains the sole parameter, 'limit', as the maximum number of bridges to return, which fully conveys its semantics and default behavior. This is a complete and clear description for the 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 tool's function: finding potential cross-domain connections in the knowledge graph. It provides a specific verb ('Find'), a resource ('knowledge graph'), and distinguishes itself from siblings by explaining the unique criterion (concept pairs sharing neighbors but not directly connected).

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

Usage Guidelines3/5

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

The description implies a use case ('candidates for new insights') but does not explicitly mention when to use this tool versus alternatives, nor does it name any sibling tools. It offers some context but lacks direct guidance or exclusions.

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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