@lex-tools/codebase-context-dumper
Official코드베이스-컨텍스트-덤퍼 MCP 서버
코드베이스 컨텍스트를 대규모 언어 모델(LLM)로 쉽게 덤프하도록 설계된 모델 컨텍스트 프로토콜(MCP) 서버입니다.
왜 이것을 사용해야 하나요?
LLM의 넓은 컨텍스트 창은 강력하지만, 방대한 코드베이스에서 파일을 수동으로 선택하고 서식을 지정하는 것은 번거롭습니다. 이 도구는 다음과 같은 방법으로 프로세스를 자동화합니다.
프로젝트 디렉토리를 재귀적으로 스캔합니다.
.gitignore규칙에 의해 제외되지 않은 지정된 디렉토리 트리의 텍스트 파일을 포함합니다.자동으로 바이너리 파일을 건너뜁니다.
명확한 파일 경로 표시로 콘텐츠를 연결합니다.
LLM의 컨텍스트 창보다 큰 코드베이스를 처리하기 위한 청킹 지원.
MCP 호환 클라이언트와 완벽하게 통합됩니다.
Related MCP server: code-index-mcp
사용법 (권장: npx)
이 도구를 사용하는 가장 쉬운 방법은 npx 사용하는 것인데, 이를 사용하면 로컬에 설치할 필요 없이 최신 버전을 실행할 수 있습니다.
다음 명령을 사용하여 MCP 클라이언트(예: Claude Desktop, VS Code 확장 프로그램)를 구성하세요.
지엑스피1
그러면 MCP 클라이언트는 이 서버에서 제공하는 dump_codebase_context 도구를 호출할 수 있습니다.
기능 및 도구 세부 정보
도구: dump_codebase_context
.gitignore 규칙을 준수하고 바이너리 파일을 건너뛰며 지정된 디렉터리에서 텍스트 파일을 재귀적으로 읽습니다. 파일 경로 헤더/푸터와 콘텐츠를 연결합니다. 대규모 코드베이스에 대한 출력 분할을 지원합니다.
기능 :
base_path에 제공된 디렉토리를 스캔합니다.모든 레벨의
.gitignore파일을 존중합니다(중첩된 파일과 기본적으로.git포함).바이너리 파일을 감지하고 건너뜁니다.
각 유효한 텍스트 파일의 내용을 읽습니다.
각 파일의 내용에 헤더(
--- START: relative/path/to/file ---)를 추가하고, 푸터(--- END: relative/path/to/file ---)를 추가합니다.처리된 모든 파일 내용을 단일 문자열로 연결합니다.
입력 매개변수 :
base_path(문자열, 필수): 스캔할 프로젝트 디렉토리의 절대 경로입니다.num_chunks(정수, 선택 사항, 기본값: 1): 출력을 나눌 청크의 총 개수입니다. 1 이상이어야 합니다.chunk_index(정수, 선택 사항, 기본값: 1): 반환할 청크의 1부터 시작하는 인덱스입니다.num_chunks > 1,chunk_index <= num_chunks필요합니다.
출력 : 연결된(그리고 잠재적으로 청크화된) 텍스트 콘텐츠를 반환합니다.
로컬 설치 및 사용(고급)
로컬 버전을 실행하려는 경우(예: 개발용):
저장소를 복제합니다.
git clone git@github.com:lex-tools/codebase-context-dumper.git cd codebase-context-dumper종속성 설치:
npm install서버를 빌드하세요:
npm run build로컬 빌드 출력을 가리키도록 MCP 클라이언트를 구성하세요.
{ "mcpServers": { "codebase-context-dumper": { "command": "/path/to/your/local/codebase-context-dumper/build/index.js" // Adjust path } } }
기여하다
기여를 환영합니다! 개발, 디버깅 및 새 버전 출시에 대한 자세한 내용은 CONTRIBUTING.md를 참조하세요.
특허
이 프로젝트는 Apache 라이선스 2.0에 따라 라이선스가 부여됩니다. 자세한 내용은 라이선스 파일을 참조하세요.
Available Tools
1 tooldump_codebase_contextA
Recursively reads text files from a specified directory, respecting .gitignore rules and skipping binary files. Concatenates content with file path headers/footers. Supports chunking the output for large codebases.
| Name | Required | Description | Default |
|---|---|---|---|
| base_path | Yes | The absolute path to the project directory to scan. | |
| num_chunks | No | Optional total number of chunks to divide the output into (default: 1). | |
| chunk_index | No | Optional 1-based index of the chunk to return (default: 1). Requires num_chunks > 1. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key operational traits: recursive file reading, .gitignore respect, binary file skipping, output formatting with headers/footers, and chunking for large outputs. However, it doesn't mention potential limitations like file size constraints, permission requirements, or error handling, leaving some gaps.
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 in two sentences: the first covers core functionality and constraints, the second addresses scalability. Every phrase adds value (e.g., 'respecting .gitignore rules', 'skipping binary files', 'chunking the output'), with no wasted words or redundancy.
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 moderate complexity (recursive file operations, chunking) and lack of annotations/output schema, the description does a good job covering core behavior and constraints. It explains what the tool does, key features, and output handling, but omits details like return format, error scenarios, or performance implications, which would enhance completeness for an agent.
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 input schema already fully documents all three parameters (base_path, num_chunks, chunk_index). The description adds no additional parameter-specific information beyond what's in the schema, such as examples or edge cases. The baseline score of 3 reflects adequate but minimal value addition from the description.
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 specific action ('recursively reads text files', 'concatenates content with file path headers/footers') and resource ('from a specified directory'), including key behavioral details like respecting .gitignore rules and skipping binary files. With no sibling tools, it fully defines the tool's unique purpose without redundancy.
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 implies usage for scanning codebases ('large codebases') and mentions chunking for scalability, but provides no explicit guidance on when to use this tool versus alternatives or any prerequisites. Since there are no sibling tools, the lack of comparative guidance is less critical, but still leaves usage context somewhat open-ended.
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.
1 tool update
- First observed
dump_codebase_context
TDQS
Scored across 1 tool
With only one tool, there is no possibility of ambiguity or overlap between tools. The tool has a single, clearly defined purpose.
A single tool inherently has perfect naming consistency, as there are no other tools to compare it against for patterns or conventions.
One tool is too few for most practical server purposes, as it severely limits functionality and interaction. While the tool is well-described, a single tool feels thin and incomplete for a codebase context server.
The server's purpose appears to be codebase context management, but with only a dump tool, there are significant gaps. Missing operations like search, filter, update, or delete context make the surface incomplete and likely insufficient for agent workflows.
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