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Jambozx

OnlineCyberTools MCP (280+ filterable tools)

text_diff

Read-onlyIdempotent

Compare two text inputs and report line-level differences using LCS. Options for unified, side-by-side, or inline views, with optional case or whitespace normalization.

Instructions

Text Diff. Compare two text inputs and report every line-level difference using an LCS algorithm. Choose a view via diffType: 'unified' (single change list), 'side-by-side' (paired left/right lines), or 'inline'. Optional ignoreCase and ignoreWhitespace normalize before comparing. Use this for plain-text/code comparison; use file_comparer for the same diff over uploaded files, and text_find_replace to substitute matches rather than view changes. Pure local compute: read-only, non-destructive, offline, and rate-limited (60 requests/min for anonymous callers). Returns the diff segments plus per-text and change statistics.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
text1YesFirst (original/left) text; compared line by line against text2.
text2YesSecond (modified/right) text; differences are reported relative to text1.
diffTypeNoOutput shape of the diff segments; any other value falls back to unified.unified
ignoreCaseNoLowercase both texts before comparing so case differences are not reported.
ignoreWhitespaceNoTrim and collapse runs of whitespace before comparing so spacing differences are not reported.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
successNoAlways true on success.
diffNoOrdered diff segments; field set varies by diffType (unified adds prefix/lineNumber/text, side-by-side adds line1/line2/text1/text2, inline adds text).
statsNoSize metrics for each input and a change tally.
Behavior5/5

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

Annotations already indicate read-only, non-destructive, idempotent; description adds pure local compute, offline, rate limits, and output details. No contradiction.

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?

Front-loaded with purpose, efficient sentences, no redundancy. Every sentence adds value.

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?

Covers purpose, usage, behavior, parameters, and output; fully adequate for an AI agent given the complexity and rich annotations.

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 covers 100% of parameters with descriptions; description adds meaningful context like diffType views and normalization options, enhancing understanding.

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 it compares two text inputs line-by-line using LCS algorithm, and distinguishes from sibling tools file_comparer and text_find_replace.

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?

Explicitly tells when to use this tool vs. file_comparer (for files) and text_find_replace (for substitution), with clear 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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