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get_edit_distance

Calculate the Levenshtein edit distance between two strings, returning the minimum number of single-character edits needed to transform one string into another.

Instructions

Levenshtein edit distance. Minimum single-character edits to transform s1 into s2.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
s1Yes
s2Yes

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes
Behavior3/5

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

Describes core operation (minimum single-character edits) but lacks details about case sensitivity, whitespace handling, or that it returns an integer. No annotations exist, so description carries full burden but is minimal.

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?

Two concise sentences, front-loaded with the key term 'Levenshtein edit distance'. Efficient use of words.

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?

Sufficiently complete for a simple algorithm tool given existence of output schema. Lacks mention of output type or edge-case behavior, but overall adequate.

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?

Description names s1 and s2 and implies direction (s1 to s2), adding meaning beyond the schema which only lists types. However, schema coverage is 0%, and description does not elaborate on constraints or examples.

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?

Clearly states it computes Levenshtein edit distance, specifying verb 'get' and resource 'edit distance'. Distinguishes from sibling 'get_normalized_edit_distance' by naming the specific algorithm.

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

Usage Guidelines2/5

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

No guidance on when to use this tool versus alternatives like normalized edit distance or other similarity measures. No context on prerequisites or scenarios.

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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