MCP Server Diff Python
mcp-server-diff-python
두 문자열 간의 텍스트 차이점을 가져오는 MCP 서버입니다. 이 서버는 Python의 표준 라이브러리인 difflib 활용하여 두 텍스트 간의 차이점을 통합 diff 형식으로 효율적으로 생성하고 제공하므로 텍스트 비교 및 버전 관리에 이상적입니다.
특징
도구
서버는 단일 도구를 제공합니다.
get-unified-diff : 두 텍스트의 차이점을 Unified diff 형식으로 가져옵니다.
인수:
string_a: 비교할 소스 텍스트(필수)string_b: 비교할 대상 텍스트(필수)
반환 값: Unified diff 형식의 차이점을 포함하는 문자열
Related MCP server: Git Stuff Server
용법
클로드 데스크탑
Claude Desktop과 함께 사용하려면 서버 구성을 추가하세요.
MacOS의 경우: ~/Library/Application\ Support/Claude/claude_desktop_config.json
Windows의 경우: %APPDATA%/Claude/claude_desktop_config.json
지엑스피1
또는 다음 구성을 추가합니다.
git clone https://github.com/tatn/mcp-server-diff-python.git
cd mcp-server-diff-python
uv sync
uv build"mcpServers": {
"mcp-server-diff-python": {
"command": "uv",
"args": [
"--directory",
"path\\to\\mcp-server-diff-python",
"run",
"mcp-server-diff-python"
]
}
}개발
디버깅
다음 명령을 사용하여 npx를 사용하여 MCP Inspector를 시작할 수 있습니다.
npx @modelcontextprotocol/inspector uvx mcp-server-diff-pythonnpx @modelcontextprotocol/inspector uv --directory path\to\mcp-server-diff-python run mcp-server-diff-pythonAvailable Tools
1 toolget-unified-diffB
Get the difference between two text articles in Unified diff format. Use this when you want to extract the difference between texts.
| Name | Required | Description | Default |
|---|---|---|---|
| string_a | Yes | ||
| string_b | Yes |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the full burden of behavioral disclosure. It mentions the output format ('Unified diff format') but fails to describe critical behaviors such as error handling, performance characteristics, or any side effects. For a tool with two inputs and no annotation coverage, this leaves significant gaps in understanding how it operates beyond its basic function.
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 highly concise and well-structured, consisting of two sentences that efficiently state the tool's purpose and usage context without any redundant information. Every sentence earns its place, making it easy to parse and understand quickly.
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?
Given the tool's complexity (comparing two texts) and the lack of annotations, output schema, and parameter descriptions, the description is incomplete. It does not explain what the Unified diff format entails, how differences are computed, or what the return values look like. This leaves too many unknowns for effective tool selection and invocation.
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 0%, meaning the input schema provides no descriptions for 'string_a' and 'string_b'. The description does not add any semantic details about these parameters, such as what they represent (e.g., original vs. modified text) or any constraints. This lack of compensation for the schema gap results in a low score, as users must guess parameter meanings.
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 clearly states the tool's purpose: 'Get the difference between two text articles in Unified diff format.' It specifies the verb ('Get'), resource ('difference'), and output format ('Unified diff format'), which is specific and actionable. However, since there are no sibling tools, the description cannot demonstrate differentiation from alternatives, preventing a perfect score.
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 provides some guidance with 'Use this when you want to extract the difference between texts,' which implies the context for usage. However, it lacks explicit when-not-to-use scenarios or comparisons to alternatives (though none exist here). This makes it adequate but not comprehensive, fitting a baseline score.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v1.0.0- First observed
get-unified-diff
TDQS
With only one tool, there is no possibility of ambiguity or overlap with other tools, making it perfectly clear and distinct in purpose.
The single tool name 'get-unified-diff' follows a consistent verb_noun pattern, and with no other tools to compare, there is no inconsistency.
One tool is too few for a server named 'MCP Server Diff Python', which suggests a broader scope for diff operations, such as handling multiple diff formats or additional text comparison features.
The server is severely incomplete; it only provides a single diff tool, lacking obvious operations like applying diffs, comparing multiple texts, or supporting other diff formats, which are typical for diff-related domains.
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