Calculate Levenshtein edit distance and similarity between two strings to compare text, with optional case-sensitive matching.
Calculate Levenshtein edit distance and similarity between two strings.
| Name | Required | Description | Default |
|---|---|---|---|
| a | Yes | First string. | |
| b | Yes | Second string. | |
| case_sensitive | No | Case sensitive comparison (default true). |
Changes observed during successful MCP inspections.
v0.1.11Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full behavioral burden. It names the exact algorithm (Levenshtein), which is meaningful, but it does not define the 'similarity' metric, output range or format, or whether the operation is symmetric, leaving some ambiguity for return values.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is a single, front-loaded sentence with no filler. Every word earns its place and the core operation appears immediately.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a simple pure calculation tool with three documented parameters and no annotations, the description states the core operation adequately. However, with no output schema, it does not specify what the returned distance and similarity values look like, leaving a minor completeness gap.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 100%, so the schema already documents 'a', 'b', and 'case_sensitive' including the default. The description only adds 'between two strings', which does not extend the schema's meaning, so baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states a specific verb (Calculate) and resource (Levenshtein edit distance and similarity) between two strings. Naming 'Levenshtein' distinguishes it from sibling tools like damerau_distance, hamming_distance, and edit_distance, so an agent can identify the operation without opening the schema.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description gives no when-to-use, when-not-to-use, or alternative-selection guidance. With many similar string-comparison siblings available, an agent receives no help in choosing this tool over damerau_distance, hamming_distance, edit_distance, or jaccard_similarity.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.