MCP server for depth-packed codebase context
A developer has created an MCP server that packs codebase context at 5 depth levels within a token budget. The tool addresses a specific problem: when Claude Code or other AI coding agents ask questions like "how does auth work?", they typically either load 3 files fully (missing the big picture) or get a flat repository map without actual content.
How it works
The server sits between these extremes, packing 40+ files at graded depth levels. The most relevant files are included at full content, while peripheral files are represented as just paths. This approach provides both breadth and depth within token constraints.
Three search modes
- Keyword mode: Free text search
- Semantic mode: Uses embeddings (~$0.0001 per query)
- Graph mode: Follows import relationships
These modes can be composed together for more sophisticated searches.
Technical implementation
The server uses AST parsing via tree-sitter and supports 14 programming languages. It provides several MCP tools:
packindex_workspaceindex_github_repobuild_embeddingsresolvestats
The tool is available on GitHub at https://github.com/victorgjn/agent-skills. The developer notes they haven't published the skill on skills.sh yet but plan to do so soon.
📖 Read the full source: r/ClaudeAI
👀 See Also
Colony: A Local-First Coordination Layer That Cuts Multi-Agent Handoff Tokens from 30K to 400
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LLMSpend: Open-source cost tracker for Anthropic and OpenAI SDKs
LLMSpend is a Python library that adds cost tracking to Anthropic and OpenAI SDK calls with two lines of code. It provides local SQLite storage, CLI reporting, and a web dashboard without sending data externally.
llm-use – An Open-Source Framework for Routing and Orchestrating Multi-LLM Agent Workflows
llm-use is revolutionizing automation with its open-source framework designed to efficiently route and orchestrate multi-LLM agent workflows. Explore its impact on AI operations.
Engramx v3.4: MCP Server + SQLite Knowledge Graph Cuts Claude Code Token Usage by 89%
Engramx v3.4 intercepts file reads for Claude Code agents, returning structural summaries instead of raw content. Benchmarks show 89.1% aggregate token reduction across an 87-file codebase.