This server provides AI coding assistants with token-efficient, task-aware access to a repository's code structure and contents.
get_codebase_map: Generates a compressed AST symbol outline (classes, functions, interfaces) of the entire repository, reducing token usage by ~80%.
extract_relevant_context: Accepts a user query (e.g., "Fix JWT auth error") and returns only the most relevant code files, pruned by semantic ranking, within a configurable token budget.
read_file_outline: Returns a compressed skeleton of a single file’s public API (signatures and docstrings) without implementation details, saving tokens.
Note: The server integrates with MCP-compatible clients (Claude, Cursor, Windsurf) and supports automated secret redaction, though the schema shows only the three tools above.
The Next-Generation AST Codebase Map, Token Compression, and Context Packaging Engine for AI Coding Agents and IDEs.
When feeding codebases into modern AI coding assistants (Claude Code, Cursor, Windsurf, OpenAI Codex, Antigravity, Cline), developers face three critical bottlenecks:
💥 Token Explosion & High API Costs: Raw code dumps quickly exceed context limits or cost tens of dollars per query.
📉 Low Signal-to-Noise Ratio: 80% of lines are routine implementation details. LLMs only need the semantic API surface (signatures, types, interfaces, docstrings).
🔓 Security Leaks: Accidental exposure of .env credentials, API keys, and database URIs in prompts.
RepoContext solves all three with intelligent AST outline pruning, task-focused semantic file ranking, automated credential redaction, and native Model Context Protocol (MCP) integration.
Related MCP server: AtlasMemory
⚡ AST Codebase Map & Skeletonizer: Compresses source files by 70%~85% while retaining 100% of interfaces, types, functions, and JSDoc/docstrings.
🎯 Task-Oriented Relevance Pruning: Pass your prompt query (e.g. repocontext pack -q "Fix JWT auth token expiry") to automatically rank and pack only the most critical files.
🛡️ Automated Secret Scanner & Redaction: Built-in entropy and regex scanner detects and masks OpenAI keys, GitHub tokens, AWS keys, JWTs, and database URIs.
🔌 Native Model Context Protocol (MCP) Server: Directly integrates with Claude Desktop, Cursor, Windsurf, and Antigravity via standard MCP tools.
📑 Multi-Format Export: Generates clean Markdown with directory trees, Claude-optimized XML (<documents>), or structured JSON.
📊 Multi-Model Token & Cost Analytics: Real-time token counter with cost estimates for GPT-4o, GPT-5, Claude 3.5 Sonnet, and Gemini 1.5 Pro.
npx# Pack entire repository into an optimized Markdown prompt
npx repocontext pack
# Pack only files relevant to a specific task prompt
npx repocontext pack -q "Implement Stripe payment webhook handler" -o prompt.md
# Generate lightweight AST skeleton map (75%+ token reduction)
npx repocontext map -o codebase-map.mdnpm install -g repocontext# 1. Pack with direct clipboard copy (ready to paste into ChatGPT/Claude)
repocontext pack -c
# 2. Pack relevant files only based on task query
repocontext pack -q "Fix JWT auth expiration" -o prompt.md
# 3. Generate AST symbol map
repocontext map -c
# 4. Analyze token distribution without writing a file
repocontext analyze .pack:Option | Description | Default |
| Packing mode: |
|
| Filter and rank files relevant to coding prompt | None |
| Output format: |
|
| Output file path |
|
| Maximum token budget |
|
| Copy output directly to system clipboard |
|
| Disable automated secret redaction |
|
RepoContext comes with built-in MCP server support. Add it to your claude_desktop_config.json or Cursor/Windsurf MCP settings:
{
"mcpServers": {
"repocontext": {
"command": "npx",
"args": ["-y", "repocontext", "mcp"]
}
}
}get_codebase_map: Extracts token-efficient AST symbol outline map across the repository (~80% token savings).
extract_relevant_context: Prunes codebase using semantic ranking to return files pertinent to a task prompt.
read_file_outline: Reads compressed AST skeleton for an individual file (signatures & docstrings).
read_file_content: Precise source code reading with optional line-range slicing (startLine/endLine) and automated credential masking.
Feature | Raw Concatenation | Repomix | RepoContext |
AST Skeleton Pruning | ❌ No | ❌ No | ✅ Yes (80% Token Savings) |
Task Relevance Ranker | ❌ No | ❌ No | ✅ Yes (Query-guided Pruning) |
Secret Auto-Redaction | ❌ No | ⚠️ Basic | ✅ Yes (10+ Security Rules) |
Native MCP Server | ❌ No | ❌ No | ✅ Yes (Claude / Cursor / Windsurf) |
Multi-Model Cost Est. | ❌ No | ❌ No | ✅ Yes (GPT-4o/5, Claude, Gemini) |
Contributions, issues and feature requests are welcome! Feel free to check issues page.
This project is MIT licensed.
Performs semantic relevance ranking to extract and package code files pertinent to a specific user coding task or bug report within a token budget.
Behavior: Read-only operation. Zero file modifications, zero persistent side-effects, and zero network calls. Applies automatic secret redaction. Returns formatted Markdown.
When to use: Use this when given a specific feature request, issue, or debugging objective to gather only the essential source code.
When NOT to use: Do NOT use this for full-repository structural overviews (use get_codebase_map instead), or to inspect single file outlines (use read_file_outline instead).
Inputs: query (required), rootDir, maxTokens.
Extracts a token-efficient AST symbol outline map (classes, interfaces, functions, methods, exported types) across the repository.
Behavior: Read-only operation. Zero file modifications, zero persistent side-effects, and zero network calls. Returns structured Markdown.
When to use: Call this at the start of a coding task to inspect repository architecture, directory structure, and symbol hierarchies with 70%~85% token savings.
When NOT to use: Do NOT use this if you need full function implementations (use read_file_content instead), or if you already know the target task and want only relevant files (use extract_relevant_context instead).
Inputs: rootDir.
Reads raw source code from a single file, with optional line-range slicing and automatic security redaction for secrets and sensitive keys.
Behavior: Read-only operation. Zero file modifications and zero network calls. Automatically masks detected credentials. Returns formatted Markdown code block with line indicators.
When to use: Use this when you must inspect, debug, or verify exact line-by-line implementation logic of a specific file identified through previous outline or mapping steps.
When NOT to use: Do NOT use this for blind exploration of unfamiliar files (use read_file_outline or get_codebase_map first to conserve token budget).
Inputs: filePath (required), endLine, rootDir, startLine.
Extracts the semantic AST skeleton outline of a single source file, preserving all class declarations, function signatures, interfaces, and docstrings while stripping function bodies.
Behavior: Read-only operation. Zero file modifications and zero network calls. Returns syntax-highlighted code block.
When to use: Use this to understand API contracts, public types, and method signatures of an individual file without consuming tokens on implementation details.
When NOT to use: Do NOT use this if you need complete implementation logic (use read_file_content instead), or if you need an overview of multiple repository files (use get_codebase_map instead).
Inputs: filePath (required), rootDir.
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
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Scored across 4 tools
Each tool serves a clearly distinct purpose: repo-wide symbol map, task-specific file extraction, single-file outline, and full file content. The descriptions include explicit 'when to use' and 'when NOT to use' guidance, eliminating any potential for misselection.
All four tools follow the verb_noun snake_case pattern (get_codebase_map, extract_relevant_context, read_file_outline, read_file_content). While the verbs differ (get, extract, read), the structure is consistent and the names accurately reflect each tool's function.
With exactly 4 tools, the server is well-scoped for its purpose of repository context exploration. Each tool fills a distinct role in the workflow, and there is no redundancy or unnecessary bloat.
The tool set covers the full lifecycle of code exploration: repo-wide mapping, semantic relevance filtering for tasks, single-file outlines, and raw content access. This provides a complete read-only context-gathering workflow with no obvious dead ends or missing operations.
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