@lex-tools/codebase-context-dumper
OfficialCodebase-Kontext-Dumper MCP-Server
Ein Model Context Protocol (MCP)-Server, der dafür konzipiert ist, Ihren Codebase-Kontext einfach in Large Language Models (LLMs) zu übertragen.
Warum dies verwenden?
Große Kontextfenster in LLMs sind zwar leistungsstark, aber das manuelle Auswählen und Formatieren von Dateien aus einer großen Codebasis ist mühsam. Dieses Tool automatisiert den Prozess durch:
Rekursives Scannen Ihres Projektverzeichnisses.
Einschließlich Textdateien aus dem angegebenen Verzeichnisbaum, die nicht durch
.gitignore-Regeln ausgeschlossen sind.Binärdateien werden automatisch übersprungen.
Verketten des Inhalts mit eindeutigen Dateipfadmarkierungen.
Unterstützt Chunking zur Verarbeitung von Codebasen, die größer sind als das Kontextfenster des LLM.
Nahtlose Integration mit MCP-kompatiblen Clients.
Related MCP server: code-index-mcp
Verwendung (Empfohlen: npx)
Am einfachsten lässt sich dieses Tool über npx verwenden, da hier die neueste Version ohne lokale Installation ausgeführt wird.
Konfigurieren Sie Ihren MCP-Client (z. B. Claude Desktop, VS Code-Erweiterungen) für die Verwendung des folgenden Befehls:
{
"mcpServers": {
"codebase-context-dumper": {
"command": "npx",
"args": [
"-y",
"@lex-tools/codebase-context-dumper"
]
}
}
}Der MCP-Client kann dann das von diesem Server bereitgestellte Tool dump_codebase_context aufrufen.
Funktionen und Tooldetails
Werkzeug: dump_codebase_context
Liest rekursiv Textdateien aus einem angegebenen Verzeichnis, beachtet dabei .gitignore -Regeln und überspringt Binärdateien. Verkettet Inhalte mit Dateipfad-Headern/-Footern. Unterstützt die Aufteilung der Ausgabe in große Codebasen.
Funktionalität :
Durchsucht das im
base_pathangegebene Verzeichnis.Respektiert
.gitignore-Dateien auf allen Ebenen (einschließlich verschachtelter Dateien und standardmäßig.git).Erkennt und überspringt Binärdateien.
Liest den Inhalt jeder gültigen Textdatei.
Fügt dem Inhalt jeder Datei eine Kopfzeile (
--- START: relative/path/to/file ---) voran und eine Fußzeile (--- END: relative/path/to/file ---) an.Verkettet alle verarbeiteten Dateiinhalte zu einer einzigen Zeichenfolge.
Eingabeparameter :
base_path(Zeichenfolge, erforderlich): Der absolute Pfad zum zu scannenden Projektverzeichnis.num_chunks(Ganzzahl, optional, Standard: 1): Die Gesamtzahl der Chunks, in die die Ausgabe aufgeteilt werden soll. Muss >= 1 sein.chunk_index(Ganzzahl, optional, Standard: 1): Der 1-basierte Index des zurückzugebenden Chunks. Erfordertnum_chunks > 1undchunk_index <= num_chunks.
Ausgabe : Gibt den verketteten (und möglicherweise in Blöcke unterteilten) Textinhalt zurück.
Lokale Installation und Nutzung (Erweitert)
Wenn Sie lieber eine lokale Version ausführen möchten (z. B. für die Entwicklung):
Klonen Sie das Repository:
git clone git@github.com:lex-tools/codebase-context-dumper.git cd codebase-context-dumperInstallieren Sie Abhängigkeiten:
npm installErstellen Sie den Server:
npm run buildKonfigurieren Sie Ihren MCP-Client so, dass er auf die lokale Build-Ausgabe verweist:
{ "mcpServers": { "codebase-context-dumper": { "command": "/path/to/your/local/codebase-context-dumper/build/index.js" // Adjust path } } }
Beitragen
Beiträge sind willkommen! Weitere Informationen zur Entwicklung, Fehlerbehebung und Veröffentlichung neuer Versionen finden Sie in CONTRIBUTING.md .
Lizenz
Dieses Projekt ist unter der Apache-Lizenz 2.0 lizenziert. Weitere Informationen finden Sie in der Datei LICENSE .
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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