lazymac-mcp

diff_patch_tools

Compare text differences and generate or apply unified patches using LCS-based algorithms for version control and document synchronization.

Instructions

LCS-based text diffing, unified patch generate / apply

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "diff_patch_tools"
  }
}

Input Schema

Name	Required	Description	Default
`params`	No	Free-form params object — passed as query string for GET, JSON body for POST

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Apr 15, 2026

TDQS

C2.6/5.0

Behavior2/5

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

No annotations are provided, so the description carries full burden. It mentions the operations (diffing, patch generation/application) but doesn't disclose critical behavioral traits such as input/output formats, error handling, performance characteristics, or side effects. For a tool with a single complex parameter object, this is insufficient transparency.

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 very concise with just one phrase ('LCS-based text diffing, unified patch generate / apply'), which efficiently communicates the core functionality. However, it could be more structured by separating the diffing and patching aspects for clarity, and it lacks any introductory context.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations, no output schema, and a single but complex nested parameter object, the description is incomplete. It doesn't explain what the tool returns, how to structure the 'params' object for specific operations, or provide examples. For a tool with potentially varied use cases (diffing vs. patching), more context is needed.

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?

The schema description coverage is 100%, with the parameter 'params' documented as a free-form object passed differently based on HTTP method. The description adds no additional parameter semantics beyond what the schema provides, but since schema coverage is high, the baseline score of 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose3/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description states 'LCS-based text diffing, unified patch generate / apply' which identifies the core operations (diffing, patch generation/application) and mentions the algorithm (LCS). However, it's somewhat vague about the exact resource being manipulated (text) and doesn't clearly distinguish from potential sibling tools like 'text_analysis' or 'data_transform' that might have overlapping functionality.

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 is provided on when to use this tool versus alternatives. The description doesn't mention any prerequisites, typical use cases, or exclusions. Given sibling tools like 'text_analysis' and 'data_transform' exist, this lack of differentiation leaves the agent guessing about appropriate contexts.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.